A BIM-based approach for multi-objective optimization of sustainable materials selection through life cycle analysis

A BIM-based approach for multi-objective optimization of sustainable materials selection through life cycle analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A BIM-based approach for multi-objective optimization of sustainable materials selection through life cycle analysis Mehran Jani, Sahar Falegari, Ali Akbar Shirzadi Javid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4062986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Given the increasing need for design coupled with constrained financial resources, a comprehensive approach that combines life cycle assessment (LCA), life cycle cost analysis (LCCA), and multi-dimensional optimization was suggested to develop a decision-making framework for cost-effective buildings. The proposed framework considers various aspects such as performance, economic considerations, and environmental factors. Integrating environmental and economic analysis into building construction and design was explored, emphasizing the use of Building Information Modeling (BIM) to manage building data and conduct cost and environmental assessments. Finally, a framework was suggested for selecting optimal materials for pre-construction activities. The study also highlights the importance of considering sustainability and long-term costs in decision-making. In addition, the integration of economic aspects into sustainability assessments was discussed, and challenges and areas for future research were identified. The research methodology included creating a comprehensive database, utilizing life cycle assessment software, and employing optimization techniques to select the most suitable materials for different regions. The results showed significant differences with more than 50% reduction in cost evaluation between generic and optimal materials in the life cycle assessment. In the doors category in North America, greenhouse gas production was reduced by 47%, which was observed between the United States and Australia. Life cycle assessment life cycle cost multi-objective optimization BIM-based LCA conceptual design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction The construction and buildings industry contributes significantly to greenhouse gas emissions, energy consumption, and waste generation worldwide, with a staggering 30%, 40%, and 32%, respectively (Y. L. Li et al. 2019 ; Nejat et al. 2015 ). Housing, being the largest energy consumer in the building sector, accounting for over half of its total energy usage (Programme 2021 ), plays a crucial role in mitigating environmental impacts. Without proactive measures, energy consumption and greenhouse gas emissions from buildings are projected to double by 2050 due to population growth and economic expansion (Hu 2020 ). Over the past two decades, researchers have tried to develop and implement various methods for integrating the environmental and economic analysis of different technologies and systems. These methods range from micro turbines to large-scale infrastructures. Decision-makers face the challenge of managing and delivering economically feasible and environmentally sustainable projects (Miah, Koh, and Stone 2017 ). Sustainability, defined as meeting the needs of the present without compromising the ability of future generations to meet their own needs (Imperatives 1987 ), is a key consideration in the building construction industry. The goal is to minimize environmental impacts and resource use while maximizing investment returns (Ding 2008). With rising environmental concerns and limited budgets, the architecture, engineering and construction (AEC) industry faces growing pressure to deliver sustainable and affordable building projects. Integrating life cycle assessment (LCA) and life cycle costing (LCC) into early design stages provides a systematic approach to evaluate environmental impacts and economic costs over a building’s lifespan. Life cycle assessment (LCA) is a valuable tool for evaluating sustainability in buildings (Romano, Negro, and Taucer 2014). It assesses the environmental impacts of a building throughout its entire life cycle, from material extraction to demolition (Akbarnezhad and Xiao 2017). Life cycle cost analysis (LCCA) complements LCA by evaluating the financial costs associated with different options (Kale, Joshi, and Menon 2016; Rad et al. 2021 ). It balances initial construction costs with long-term energy savings to identify the most cost-effective strategies (K. P. Kim and Park 2018). However, conducting comprehensive LCA/LCC analyses manually can be resource intensive. Building information modeling (BIM) has emerged as a promising tool for streamlining and enhancing the accuracy of carbon emission assessments in buildings (Abbasi and Noorzai 2021; Carvalho et al. 2020 ; Feng et al. 2020 ; Safari and AzariJafari 2021; B Soust-Verdaguer, Llatas, and Moya 2020 ). Integration of life cycle assessment (LCA) and life cycle cost analysis (LCC) is facilitated by building information modeling (BIM), which connects material quantities, costs, and sustainability data (Barbini et al. 2020 ; Kylili et al. 2015 ; Najjar et al. 2017 ). Prior studies have focused on evaluating the operational and embodied energy levels of construction components to promote sustainability and reduce energy consumption (Parkinson, Parkinson, and de Dear 2019). By integrating this evaluation with Building Information Modeling (BIM), its application extends further. For instance, a combination of BIM and Building Energy Modeling has been employed to enhance operational energy efficiency in buildings and aid decision-makers during the early design phase (Gao, Koch, and Wu 2019). The outcomes of integrating BIM and LCA can also contribute to achieving other sustainability aspects in construction projects (Díaz and Antön 2014). While numerous researchers have explored BIM-LCA integration (Gao, Koch, and Wu 2019; Bernardette Soust-Verdaguer, Llatas, and García-Martínez 2017a), there remains a gap in the application of mathematical optimization modeling to the decision-making process during the design phase and in optimizing energy performance through BIM-LCA integration to achieve energy-efficient buildings (Chen and Yang 2017 ). Nevertheless, most studies focus solely on environmental impacts without considering economic trade-offs. Consequently, this study addresses the need for an integrated LCA-LCC-BIM approach optimizing both sustainability and costs. By combining LCA and LCCA, decision-makers can optimize sustainable retrofits that are both environmentally sound and financially feasible (Dauletbek and Zhou 2022; Schwartz, Raslan, and Mumovic 2016). This approach is supported by the similarities between LCA and LCCA, such as their timing, system scope, and analytical processes (Y. Shin, Engineering, and 2015 n.d.). Given the increasing significance of life cycle assessment in the construction industry, numerous techniques have been developed to apply this method effectively. The environmental performance of a building during its life cycle depends on various components, some of which are mentioned in the study. Building functionality, temperature requirements, consumption patterns, and resident behavior all influence energy consumption and greenhouse gas production. Additionally, regional climate variables are crucial for building performance during its life cycle evaluation. Different regions have distinct heating and cooling requirements based on their prevailing climates. This research investigated the potential benefits or drawbacks of adapting building design and life cycle assessment results to align with regional climatic needs. It also investigated whether such changes were economically worthwhile. The proposed framework entails three main steps. First, an extensive materials database is compiled containing LCA and LCC data. Next, BIM software models the building and assigns material alternatives. Finally, a multi-objective optimization algorithm determines optimal materials minimizing LCC and LCA impacts. This methodology is implemented on residential buildings in the US and Australia to examine regional variances. Compared to conventional materials, optimized selections reduced LCC by over 50% and greenhouse gases by up to 47% in the US. Significant cost and environmental savings demonstrate the value of integrated LCA-LCC optimization. Additionally, differing US and Australia results highlight the importance of localization. 2. Literature review Building life cycle encompasses their system boundary, which defines the processes included in their assessment. While the importance of defining the system boundary has been emphasized in numerous studies, the EN 15978 (EN 2011 ) standard, widely regarded as the most reliable standard in industry and academia, outlines the following life stages for building projects: A: The embodied stage, which includes production and construction. B: The operation stage. C: The end-of-life stage. In addition to these fundamental system boundaries, some studies also investigate the benefits and outcomes beyond the life of materials (D) (Lu et al. 2021 ). 2.1. Integration of Building Information Modeling (BIM) into life cycle assessment BIM is a tool that reduces time and effort in managing building data. It can be used for cost and environmental analyses. BIM can be integrated with LCA and LCC in three ways: maintaining inventory lists, exporting models, and incorporating information. LOD 300 is often used for LCA and LCC (Lu et al. 2021 ). A review paper (Bernardette Soust-Verdaguer, Llatas, and García-Martínez 2017b) critically examined research on integrating Building Information Modeling and Life Cycle Assessment for building applications. Fifteen case studies were analyzed to compare methodologies, results, limitations, and future recommendations. Major challenges identified included a lack of interoperability between software tools, system boundary limitations, and data reliability concerns. Further standardization of data exchange and mapping system boundaries could improve BIM-LCA capabilities. In another study (Bueno and Fabricio 2018), the authors compared a detailed, manual life cycle assessment following ISO standards versus a simplified LCA conducted using a BIM-LCA plugin tool. The study compared manual LCA with the BIM-LCA plugin for a residential building in Brazil. Manual LCA offered comprehensive impact analysis, while the BIM-LCA plugin was limited. BIM-LCA is helpful for early design but needs further development. A study by Ajayi et al. (Ajayi et al. 2015 ) utilized an integrated Building Information Modeling - Life Cycle Assessment approach to compare material specs for a residential building. Results showed the value of combining BIM and LCA early in design. The authors recommend further development of regional LCA data and integration with BIM. Few researches ((Basbagill et al. 2013 ; Hollberg, Genova, and Habert 2020; Bernardette Soust-Verdaguer, Llatas, and García-Martínez 2017b)) examined the integration and application of life cycle assessment in building design. Soust-Verdaguer reviewed BIM-LCA integration, Hollberg evaluated the consistency in results between manual life cycle assessment methods versus automated BIM-LCA integration for building design, and Basbagill applied LCA early in design to reduce embodied impacts. The studies highlighted opportunities to leverage LCA to guide design optimization and material selection to improve building sustainability. 2.2. Life Cycle Costs and Assessment Life cycle cost analysis involves the economic assessment of existing or potential future investments, considering short-term and long-term economic effects. Life cycle costs encompass the costs of an asset or its components throughout its life cycle while meeting performance requirements (ISO 2017 ). Life cycle cost calculations are used to improve the selection process by creating a reasonable structure regarding the economic performance of a project over its lifetime. Although life cycle costing has a long history since the 1930s, it is a relatively novel tool in sustainability. It is important to note that life cycle costs differ from total project life costs, with life cycle costs being a part of the overall costs. Typically, life cycle costs are divided into four parts to cover construction costs during their lifetime, including initial costs (construction costs), operation and maintenance costs, replacement costs, and end-of-life costs, which include the value of the building. Total project life costs encompass life cycle costs, externalities, non-construction costs, and revenue (Schau et al. 2011 ). Most life cycle cost research focuses on one or two phases, with few considering the entire life cycle. RS Means and Spons are widely used databases, while some use local or market price lists. NPV is the most common method, with discount rates from 2 to 1.6% (Santos et al. 2019 ). The current value can also be applied if the research duration focuses on a specific life stage (Lu et al. 2021 ). Using project life cycle costing methods at the beginning of the project is most effective. Therefore, managers and engineers often explore different options from an economic perspective, focusing on elements and construction methods (W. Li et al. n.d.). In most cases, life cycle costing presents all costs at their present value. The present value of future costs is estimated based on the future inflation rate and a discount rate. Future costs are calculated using Eq. 1 and converted into discounted costs using a specific discount rate defined in Eq. 2. For instance, research conducted by Islam et al. (Islam et al. 2015 ) in Australia considered a 3% inflation rate, the ten-year average inflation, and a 6% discount rate based on the Australian Manufacturing Industry Organization proposal. \(FC=PV \times {\left(1+\text{f}\right)}^{n}\) Eq. 1 \(DPV=FC/{\left(1+\text{d}\right)}^{n}\) Eq. 2 FC represents future costs, PV indicates present value, DPV represents discounted present value, f is the inflation rate, d is the discount rate, and n is the years under consideration. Table 2 summarizes the formulas. The first formula accounts for costs at zero points, such as material, labor, and equipment. The second formula relates to annual costs associated with building use, such as energy costs and annual replacement or repair costs for various items. The third formula calculates replacement costs after a specified number of years. The fourth formula represents the current value of the building after the research period (the building's service life), where F represents the residential value of the building after n years in the future. In a study by Marzuk et al. (M Marzouk, Azab, and Metawie 2016 ), a system dynamics model was used as a decision-making tool to select green materials for affordable, sustainable housing. The model was combined with the LEED index (Yung, Robotic, and 2014 2014) and a genetic algorithm to optimize life cycle costs. Marzuk et al. (Yung, Robotic, and 2014 2014) developed a framework to determine the timing of affordable housing projects and select the most suitable alternative materials based on their sustainability aspects. The framework was tested on a 5-story building in Badr, Egypt. It was found that achieving 8 out of 11 possible LEED points was less than $ 2.25 million. Figure 1 shows the effect of higher costs on obtaining more LEED points. The study concluded that sustainable materials have significantly lower operating costs than traditional materials. Marzuk et al. (Mohamed Marzouk, Azab, and Metawie 2018 ) also discussed the use of BIM, genetic algorithms, and Monte Carlo simulation in a study to identify the best and most suitable building materials from economic and environmental perspectives. The environmental aspect was evaluated using the maximum LEED score. The study also examined which buildings were most affected by changes. It is worth mentioning that this article builds upon previous work by utilizing two objective functions, economic and environmental, through a genetic algorithm. Additionally, the article emphasizes the impact of uncertainty on the building system from economic and environmental perspectives. A 3D model of BIM was created, and different materials were assigned to various building components. A sensitivity analysis was conducted to identify the cost components with the most significant impact on the project for each building system. Life cycle assessment (LCA) utilizes various indicators to evaluate environmental impacts, including carbon gas emissions, energy consumption, acidification potential (AP), eutrophication potential (EP), the abiotic depletion potential of materials (ADPM), human health respiratory effects potential (HHREP), photochemical ozone creation potential (POCP), and ozone depletion potential (ODP). Among these indicators, energy is the most important and commonly used. It is often analyzed through carbon dioxide emissions, global warming potential, greenhouse gas emissions, and carbon footprint. Several databases, such as Athena, the Inventory of Carbon & Energy (ICE), GaBi, and Ecoinvent, are frequently employed in LCA research. Local databases like the Korea Life Cycle Inventory and Belgium EPDs have also been used (Lu et al. 2021 ). These three literature reviews (Buyle, Braet, and Audenaert 2013; Cabeza et al. 2014 ; Sharma et al. 2011 ) summarize the state of research on applying life cycle assessment to evaluate the environmental impacts of buildings and the construction sector. The reviews covered common LCA goals, scopes, methodologies, impact indicators, findings, limitations, and future directions. Key results highlighted the significance of the use phase and benefits of LCA to inform building design and material selection to improve sustainability. Apart from life cycle costs, significant attention has been directed towards greenhouse gas production in all industries worldwide, with buildings being a major contributor. In the United States, buildings account for over 39% of total primary energy consumption and greenhouse gas production (Hong et al. n.d.). Consequently, numerous studies have been conducted to assess and mitigate greenhouse gas emissions, with life cycle assessment (LCA) being widely employed. LCA encompasses greenhouse gas emissions and other assessments throughout the process, including product production, handling, assembly, operation, and disposal (Y. S. Shin et al. 2015 ). Table 1 summarizes the disparity between life cycle costs and life cycle assessment (Zhao, Huppes, and Van der Voet 2011). LCC LCA Decision-making point of view Investor supply chain actors Goal For economic evaluation of business decisions and options To assess the environmental impact of products and processes Range Only direct costs or direct profits from an investment All stages of the life cycle Unit Currency (eg. Dollar, pound and ...) Units of mass and energy (eg. Kg, kWh and ...) Time Adjust costs over a period of time to reflect the effect of time using various discounting methods such as net present value. The effect of time on environmental impacts is usually not considered, especially its decrease or increase over time Conclusion In most researches, the interest rate is effective, but sometimes it is not taken into account Future environmental effects are usually not factored into results proportionally to time 2.3. Life Cycle Sustainability Assessment Life cycle sustainability assessment incorporates sustainability's economic, social, and environmental dimensions. It utilizes three methods: life cycle costs (economic), life cycle assessment (environmental), and social assessment of life cycle costs (social) (K. P. Kim and Park 2018). The social aspect of sustainability assessment encompasses research on indoor and outdoor user comfort, user security, human resources, and the learning process. Security-related analyses contribute significantly to social aspect. Recent research has also explored the benefits of Building Information Modeling (BIM) for preserving and assessing cultural heritage. However, integrating social aspects with economic and environmental aspects has been limited, with individual investigations conducted (Santos et al. 2019 ). BIM for sustainability faces integration, sustainability understanding, and library availability challenges. Current research focuses on costs, life cycle assessment, certifications, and sensors (J.-U. Kim et al. 2018 ). A study by Alwan et al. (Alwan, Jones, and Holgate 2017) proposed a novel methodology to integrate the Framework for Strategic Sustainable Development (FSSD) principles into Building Information Modeling (BIM) workflows for construction projects. The aim was to guide sustainability transformations in the built environment by bringing strategic planning into BIM and design processes. A case study of a commercial building redevelopment project in the UK was presented. The FSSD principles were used to identify sustainability challenges and develop strategic action plans. BIM offers various tools to model and assess sustainability strategies related to energy, materials, and construction techniques. The integrated FSSD-BIM approach helped develop more sustainable building design concepts than conventional methods. Further validation across more extensive case studies was recommended. 2.4. Combination of Life Cycle Assessment and Life Cycle Costs and Optimization Life cycle assessment and costing share three common characteristics: 1) their impact is maximized when applied early in the project. 2) They can be applied to a production system encompassing all building elements and construction methods. 3) They provide an analytical platform for selecting the most optimal option by considering economic and environmental resources (Y. S. Shin et al. 2015 ). A study by Ferreira et al. (J. Ferreira, Pinheiro, and De Brito 2015) analyzed the economic and environmental savings of adding structural insulation to a residential building in Portugal over its life cycle. The life cycle assessment and life cycle cost analysis determined the optimal insulation thickness that minimized costs and carbon footprint—adding insulation significantly reduced space heating needs and emissions but added material and installation costs. The results provided an optimal insulation level for this building type and climate, demonstrating the importance of life cycle optimization to balance multiple objectives. Liu et al. (Liu, Meng, and Tam 2015 ) conducted a study to optimize building design and enhance stability using a beam-based optimization method. The project's life cycle was considered, considering life cycle costs and carbon dioxide gas emissions. The Ecoect Analysis software was utilized to assess the life cycle impacts of the light and heat system. The study incorporated decision-making variables such as wall type, window-to-wall ratio, window glazing type, exterior canopy, and building orientation. The particle swarm optimization algorithm was employed for optimization purposes, as it is known for effectively solving multi-objective optimization problems. A significant aspect of this research was utilizing a work breakdown structure (WBS) to allocate resources and separately analyze direct and indirect costs and carbon emissions for different activities. However, the destruction and maintenance stages were not considered due to incomplete databases and the significant uncertainty associated with material repair and recycling. The proposed method was implemented on a commercial building in Hong Kong, demonstrating its positive impact on both economic and environmental aspects. The processing time remained relatively unchanged despite expanding the investigation range to find the best elements. In a separate study, Islam et al. (Islam et al. 2015 ) aimed to find an optimal balance between life cycle costs and environmental impacts in Australian residential buildings. The research focused on two objective functions: project life cycle costs and environmental effects, which were evaluated through life cycle cost and life cycle assessment methods. Alternative options were considered for a basic house with 18 walls and four floors. Linear programming was employed as the optimization algorithm for single and multi-objective functions. Weighting coefficients were also used to compare different scenarios. The study concluded that a house with fixed life cycle costs could have up to 20% less environmental impact. PRé’s SimaPro software (Jrade and Jalaei 2013) was used for life cycle assessment, with the Ecoinvent database (Swiss) and Australian Lifecycle Bank (Akbarnezhad, Ong, and Chandra 2014) serving as the primary data sources. The research investigated four factors related to life cycle assessment: greenhouse gas (GHG) emissions (tCO 2 -eq), cumulative energy demand (CED) (GJ), water usage (kL), and solid waste generation (tonne). A separate database (Rawlinsons 2015 ) was utilized to estimate life cycle costs, assuming a building service life of 50 years. The study revealed variations in life cycle greenhouse gas emissions (approximately 20%), cumulative energy demand (approximately 15%), water usage (approximately 26%), waste production (approximately 29%), and costs (approximately 22%) across different scenarios. No single design achieved the lowest life cycle costs and environmental effects, highlighting the need for optimization methods to obtain the best design. There are three studies focused on combining life cycle cost analysis and optimization to improve the energy efficiency of residential and commercial buildings. Kneifel (Kneifel 2010 ) developed a method to optimize energy efficiency measures in commercial buildings by minimizing life cycle costs and carbon emissions. Ascione et al. (Ascione et al. 2015 ) and Gustafsson (Gustafsson 2000 ) applied optimization techniques to determine cost-effective insulation levels for retrofitting existing buildings, one residential or another office building, to balance insulation costs with heating energy savings over the lifespan. The research demonstrated the value of optimization methods using simulations and life cycle cost models to support cost-effective building energy efficiency. These two articles (Congedo et al. 2015 ; M. Ferreira et al. 2016 ) optimized school and office buildings to achieve net zero energy status. Ferreira compared cost-optimal versus net zero energy retrofits in Portugal. Congedo used optimization to design net zero schools and offices in Italy. The results quantified the costs and challenges of achieving net zero buildings in Mediterranean climates. In another research endeavor, Sharif et al. (Sharif and Hammad 2019) developed a method to optimize the selection of appropriate renovation strategies for educational buildings. This method considers energy consumption, life cycle costs, and environmental impacts within budget constraints. The study focused on building elements, ventilation systems (Abanda and Byers 2016 ), and lighting systems. The research model consisted of four main parts: 1) collection of input data, 2) expansion of the database, 3) definition of reconstruction methods, and 4) multi-objective optimization based on simulation. The input data encompassed various parameters required for future calculations, including price limits and insurance models. The first phase involved determining input data and expanding the beam model. Databases containing information on building materials, ventilation systems, lighting systems, and environmental and economic data were utilized. The third phase combined the previous steps' information to generate different reconstruction strategies. This phase included determining energy performance goals, expanding reconstruction strategies, searching databases for the best equivalents, and evaluating the suitability of reconstruction methods for each strategy. The process was repeated until all reconstruction methods were exhausted. The final phase involved using MATLAB software to select the best strategy. Optimization results were presented in Fig. 2 , showcasing two functions: total energy consumption and life cycle costs and total energy consumption and life cycle assessment. The left figure demonstrated a more diverse Pareto front. This indicates that using this function for optimization provided a more comprehensive range of options than the right figure, where the Pareto front members were nearly identical. 3. Methodology This research aims to identify the most suitable materials for residential houses based on their life cycle costs. The study uses building information modeling software to evaluate life cycle and climatic variables. The architecture, engineering, and construction industry significantly contributes to global energy consumption. One way to assess its energy consumption and environmental impact is through a life cycle assessment. However, conducting a comprehensive analysis often exceeds specific software capabilities due to the extensive data and inputs required. Additionally, the dynamic nature of life cycle analysis and the potential for changing variables during a product's life can lead to initial inaccuracies. Therefore, it is crucial to present life cycle results early in the design process to facilitate cost-effective adjustments. In this research, a range of fixed materials in two different regions were placed (The state of Minnesota in North America and the city of Darwin in the northern region of Australia), and optimization techniques were used to select the best materials for each location (In the following, America and Australia are used, which mean the same city and state mentioned). Finally, the impact of this optimal choice on the most commonly used materials was compared. It is important to note that optimal materials may vary across different regions due to climatic differences (Abd Rashid and Yusoff 2015). Several justifications underpin the selection of these two regions as research samples. The key reasons include the following: Comparable Economic Development: The United States and Australia share the status of developed nations with advanced economies. Their comparable levels of industrialization, technological advancements, and consumption patterns make them suitable candidates for comparative analysis. Diverse Environmental Landscapes: The United States and Australia boast diverse environmental landscapes, encompassing forests, deserts, coastal regions, and unique ecosystems. Ecological diversity provides an opportunity to comprehensively examine various environmental impacts and cost factors. Abundance of Resources: Both countries possess abundant natural resources, including minerals, fossil fuels, and agricultural land. Resource richness significantly influences environmental impact and cost considerations, rendering them pertinent subjects for comparison. Global Significance: The United States and Australia hold significant global influence, and their environmental policies and practices can have far-reaching consequences. We can contribute to a broader understanding of global sustainability endeavors by scrutinizing their environmental impact and cost. Minnesota has a temperate continental climate with very cold winters, while Darwin has a tropical wet and dry climate with a distinct wet and dry season. Comparing energy use, transportation across two different climates could yield insights. Comparing two disparate regions like Minnesota and Darwin with a life cycle assessment can rigorously test the flexibility of this methodology itself, as well as highlight any data or inventory analysis gaps that may emerge when applying it across different geographical and cultural contexts. Considering these reasons and the possibility of more accessible access to environmental and cost data, these regions were the best options for research. The research methodology is as follows: Step 1: Building a Comprehensive Material Database: Gather detailed information about various materials to establish a robust database. Utilizing One Click LCA software streamlines this process by providing life cycle assessment results within specified timeframes for diverse materials. Employ country-specific databases like RSMEANS (US) and Rawlinson (Australia) to calculate accurate life cycle costs. Consider all relevant costs including initial construction, installation (labor & consumables), annual maintenance, and residual value. Presenting these costs upfront empowers informed decision-making throughout the project. Note that annual maintenance includes potential repairs or replacements. Transportation costs were deemed negligible in this study due to project stage and minimal fuel price variations. This omission likely has minimal impact on results. Step 2: Optimizing with Advanced Algorithms: Leverage the NSGA-II algorithm implemented within MATLAB software to tackle the project's multi-objective nature. Utilize Autodesk Revit 2022 building information modeling software for creating 3D models. Material selection occurs from pre-existing families within its material database, each containing life cycle information. These families can be further expanded for future use. Export the family materials with life cycle data into Excel and subsequently import them into MATLAB. Upon identifying the optimal solution, calculate and report the percentage improvement in cost and environmental impact compared to the initial scenario. Step 3: Comparative Analysis of Optimal Solutions: Conduct a comparative analysis of the optimal solutions for both buildings located in different countries. While selecting the same material for both is technically possible, distinct regional factors are likely to yield different results. Remember that a richer and more comprehensive database significantly increases the probability of achieving highly optimized outcomes. The workflow of this research closely follows the flowchart presented in the article by Sharif and Hammad (Sharif and Hammad 2019). The work steps can also be summarized as shown in Fig. 3 . 3.1. Creating a database This section performs a life cycle assessment based on the One Click LCA software specifications, whose online version is also available. The calculations for life cycle assessments vary based on factors such as the type of electricity and fuel used, heating and cooling methods, energy consumption, building location, and other considerations. These factors influence the numbers used to calculate life cycle assessments obtained from the software. Environmental effects can be assessed using factors such as global warming, ozone layer depletion, acidification, eutrophication, tropospheric ozone formation, and energy source depletion. For this research, the primarily focus is on global warming, using kilograms of CO 2 equivalent as the unit of measurement. While the One Click LCA software can also calculate life cycle costs, it does not provide a dedicated database. Therefore, the researchers relied on other databases to obtain cost data. For North America and Canada, the RSMEANS 2012 database is utilized. The costs were adjusted to 2021 values to account for inflation based on the Federal Reserve's annual conversion rate of 0.64%. In Australia, the Rawlinsons 2021 database is used, which provides separate cost data for five different regions. The Darwin region, located in the northernmost part of Australia, was considered the primary region near the sea to ensure consistency. It is important to note that the calculations do not consider water-related information and Part B7 of the life cycle components. Additionally, due to the early stage of the project and the lack of information regarding material suppliers and their locations, transportation costs (A2, A4, and C2) were excluded from our analysis. The research period spans 30 years, chosen to avoid equipment replacement and remain within the service interval of building components, thereby minimizing potential errors. 3.2. Introducing the model The model used in this research was developed in Autodesk Revit software and is depicted in Fig. 4 . The model covers 246.5 m 2 . This model shows a residential house with four residents. The decision-making variables considered in this research include the building entrance, building windows, internal walls, external walls, and the roof. The model features one external door, five internal doors (without differentiation), and eight windows. It is important to note that the modeled walls are non-structural and primarily investigated for their shell role. Therefore, the building structure differs from the presented shell model. Slight adjustments were made based on the available information to tailor each family to its specific materials. The cost and description of items were modified within the identity data section based on Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) data, respectively. Material description is shown in Table 2 . Table 2 Description of the materials used for different building elements Category Material Window Apartment windows with aluminium framing, single-glazed, 47.7 kg/m2 Apartment windows with aluminium framing, double-glazed, 47.7 kg/m2 Apartment windows with aluminium framing, triple-glazed, 47.7 kg/m2 Apartment windows with wood framing, double-glazed Apartment windows with wood framing, triple-glazed Door Door from cross laminated timber (CLT), 470 kg/m3, moisture content 12% Door from Particleboard, standard, melamine coated, 18 mm, 11.8 kg/m2, 656 kg/m3, 7% moisture content Door from Medium density fibreboard (MDF), moisture resistant, melamine coated, 18 mm, 13 kg/m2, 721 kg/m3, 7% moisture content Door from Plywood, maritime pine, 18mm, 11.1kg/m2, 616.7kg/m3 Medium density fibreboard (MDF), standard, melamine coated, 16 mm, 11.6 kg/m2, 724 kg/m3 Roof Sandwich panel, for roofs, R = 2.34 m2K/W, PIR insulation core 40 mm, 9.3 kg/m2 Bitumen roofing membrane, 20 mm Sandwich panels with PIR core and double steel siding, for roof and wall application, U = 0.18–0.28 W/m2K, R = 5.64 m2K/W Precast concrete roof slab, 117.63 kg/m2 Profiled steel sheet for roofing, 8.96 kg/m2 External Wall CLT external wall assembly, 5-ply, incl. mineral wool insulation and plasterboard, U-value 0.18 W/m2K, 310 mm Aerated concrete block wall assembly, incl. interior insulation and plaster covering, R > 5m2.K/W Lightweight aggregate (LECA) block external wall assembly, incl. mineral wool insulation and timber frame, U-value 0.18 W/m2K Sandwich panel, for walls, R = 7.15 m2K/W, PIR insulation core 120 mm, 14.3 kg/m2 Brick sandwich wall assembly, incl. mineral wool insulation, U-value 0.28 W/m2K Internal Wall Sandwich panels with PIR high-density foam core and double steel siding, wall application, 12.356 kg/m2 Concrete block internal wall assembly, incl. render Hollow clay bricks internal wall assembly, incl. mineral wool insulation Steel stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard Wooden stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard By incorporating these data along with the keynote section, new insights into the unique characteristics of each material was gained and material properties was extracted from the model when utilizing only these two data points. Subsequently, when transferring the data to MATLAB software, each material in the optimization algorithm was included, with the outcome determined by the keynote. Table 3 Presents the modified family sample from external information. Table 3 Modified family sample from external information Identity Data Keynote L10/210 Type image Model MANUFACTURE Type comments URL Description 169.25 Assembly code Cost 978.01 Table 4 provides examples of materials used in the model. After selecting one of the most commonly used and accessible materials in each country, their life cycle costs and life cycle assessments was calculated. Subsequently, incorporate these materials were incorporated into the model and export the data to Excel for further analysis and transfer to MATLAB. Table 4 Materials used in the model Category Material Window Apartment windows with aluminium framing, single-glazed, 47.7 kg/m2 Apartment windows with aluminium framing, double-glazed, 47.7 kg/m2 Apartment windows with aluminium framing, triple-glazed, 47.7 kg/m2 Apartment windows with wood framing, double-glazed Apartment windows with wood framing, triple-glazed Door Door from cross laminated timber (CLT), 470 kg/m3, moisture content 12% Door from Particleboard, standard, melamine coated, 18 mm, 11.8 kg/m2, 656 kg/m3, 7% moisture content Door from Medium density fibreboard (MDF), moisture resistant, melamine coated, 18 mm, 13 kg/m2, 721 kg/m3, 7% moisture content Door from Plywood, maritime pine, 18mm, 11.1kg/m2, 616.7kg/m3 Medium density fibreboard (MDF), standard, melamine coated, 16 mm, 11.6 kg/m2, 724 kg/m3 Roof Sandwich panel, for roofs, R = 2.34 m2K/W, PIR insulation core 40 mm, 9.3 kg/m2 Bitumen roofing membrane, 20 mm Sandwich panels with PIR core and double steel siding, for roof and wall application, U = 0.18–0.28 W/m2K, R = 5.64 m2K/W Precast concrete roof slab, 117.63 kg/m2 Profiled steel sheet for roofing, 8.96 kg/m2 External Wall CLT external wall assembly, 5-ply, incl. mineral wool insulation and plasterboard, U-value 0.18 W/m2K, 310 mm Aerated concrete block wall assembly, incl. interior insulation and plaster covering, R > 5m2.K/W Lightweight aggregate (LECA) block external wall assembly, incl. mineral wool insulation and timber frame, U-value 0.18 W/m2K Sandwich panel, for walls, R = 7.15 m2K/W, PIR insulation core 120 mm, 14.3 kg/m2 Brick sandwich wall assembly, incl. mineral wool insulation, U-value 0.28 W/m2K Internal Wall Sandwich panels with PIR high-density foam core and double steel siding, wall application, 12.356 kg/m2 Concrete block internal wall assembly, incl. render Hollow clay bricks internal wall assembly, incl. mineral wool insulation Steel stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard Wooden stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard 3.3. Optimization algorithm The optimization algorithm employed in this research is NSGA-II, a genetic algorithm designed for multi-objective problems. We aim to minimize two cost functions: life cycle costs and life cycle assessment. However, these functions do not move in the same direction. Using stronger and more advanced materials with better construction quality naturally increases costs. Therefore, there may be a need to consider materials with higher or more negative life cycle assessment results to lower costs and vice versa. The implementation of the NSGA-II algorithm involves six steps: Select the optimization model parameters, including the number of generations, initial population size, mutation rate, and crossover rate. Generate an initial population ready for crossover and mutation operations. Define an objective function that assigns a score to each input number based on continuous issues. In discrete problems like the one at hand, where the number of options is limited, the points assigned to each material for the project's life cycle costs and assessment functions was determined. Select the non-dominated answers (the first in front of the dominant answers). Eliminate the answers from the first front and repeat the previous steps to identify the second front of dominant answers. This front can only be dominated by the same first front. Repeat the non-dominant sorting process and assign a proportional score to each answer. Create a new child population using genetic algorithm operations such as selection, crossover, and mutation. Repeat the process until reaching the specified algorithm limits (Mohamed Marzouk, Azab, and Metawie 2018 ). The algorithm settings were as follows: Five decision variables and five options were given as answers to each variable. The initial population consisted of 30 members. The maximum number of iterations is 50. The crossover percentage was 70%, and the mutation percentage was 10%. It should be noted that the mutation rate was also 2%. The Excel data from the model's output into MATLAB were imported and select the optimal point based on the created database. The percentage of optimization that was reported is achieved under the model's conditions compared to the initial model. Finally, the final models for Australia and America was compared, examining the differences and similarities between the selected materials. 4. Results and discussion This chapter discusses the analysis and interpretation of the results obtained, as well as the relationship between these results. Initially, a framework was developed to select the best materials for the North American region, and the outcomes were compared with those obtained from generic materials. Subsequently, a similar process was performed for the northern part of Australia. Finally, the optimal results from both countries were compared, and a comprehensive examination and analysis of the findings was conducted. Optimal Results in North America Table 5 presents the generic materials used in America and their corresponding life cycle costs. Table 5 LCC and LCA of generic materials North America Category Material LCC LCA Window Wood-aluminium window, triple insulated 798.52 228.13 Door Fiberglass reinforced PE door 723.70 11.56 Roof Asphalt shingle roofing system, fiberglass reinforced 687.25 17.30 External Wall Timber frame external wall assembly, incl. mineral wool insulation 363.32 81.37 Internal Wall Wooden stud internal wall assembly 195.25 53.55 All costs are presented in US dollars to ensure consistency. Future years' costs were converted to their present value using a 1% interest rate based on information from the US Office of Management and Budget (OMB). The final results are summarized in Table 6 after applying the optimization algorithm with two genetic objectives to the provided database. Table 6 LCC and LCA of optimal materials North America Category Material LCC (USD) LCA (KgCO2) Window Wood window frame triple-glazed 978.01 169.25 Door Cross laminated timber (CLT) 647.00 5.40 Roof Bitumen roofing membrane 385.90 14.90 External Wall Sandwich panel, for walls, PIR insulation core 399.00 41.31 Internal Wall Steel stud internal wall assembly 288.00 45.74 Significant differences between generic and optimal materials were observed in all cases, as Fig. 5 illustrates the disparity in life cycle costs between the two modes. Additionally, Fig. 6 showcases the variation in life cycle assessment between generic and optimal modes. Table 7 provides a detailed breakdown of each category's selected materials and percentage differences. It is worth noting that a positive sign indicates that the optimal number is greater than the generic number, while a negative sign suggests the opposite. The optimal selection considers both factors simultaneously, resulting in varying percentages. In some cases, the most recommended materials align with the optimal materials. This indicates that the materials used in practice have minimal environmental impact while considering costs. Table 7 The difference between LCC and LCA results after the optimization North America Category Material Material LCC difference (%) LCA difference (%) Window Wood-aluminium window, triple insulated Wood window frame triple-glazed 22% -35% Door Fiberglass reinforced PE door Cross laminated timber (CLT), moisture content 12% -12% -114% Roof Asphalt shingle roofing system, fiberglass reinforced Bitumen roofing membrane -78% -16% External Wall Timber frame external wall assembly, incl. mineral wool insulation Sandwich panel, for walls, PIR insulation core 10% -97% Internal Wall Wooden stud internal wall assembly Steel stud internal wall assembly 48% -17% Similar analyses were conducted for Australia; the corresponding results are presented in Table 8 . Table 8 LCC and LCA of generic materials Australia Category Material LCC LCA Window Aluminum frame double-glazed 2312.52 316.13 Door Cross laminated timber (CLT) 671.26 7.29 Roof Corrugated steel sheet for roofing 610.02 19.80 External Wall Brick sandwich wall assembly 223.13 114.03 Internal Wall Plasterboard wall 420.84 78.33 In Australia, as in the United States, costs are measured in US dollars (converted from Australian dollar), and the project's life cycle assessment is measured in kilograms of carbon dioxide. The interest rate used is 1.4%, based on information from the Australian Reserve Bank. Figure 7 displays the non-dominant solutions obtained from the NSGA-II algorithm for the United States, highlighting the multiple correct solutions to a two-objective optimization problem. Based on the depicted figure, it is evident that there exist eleven optimal solutions for the problem at hand. Among these solutions, some exhibit a more pronounced cost reduction, while others demonstrate a more significant decrease in environmental assessment. Ultimately, none of these cases can be deemed superior to the others, as each represents a valid and optimal outcome based on the provided data. Table 9 shows cases of optimal materials obtained through the algorithm for Australia. Table 9 LCC and LCA of optimal materials in Australia Australia Category Material LCC LCA Window Aluminum frame single-glazed 1146.26 464.71 Door Particleboard, standard, melamine coated 360.63 12.96 Roof Roof with Sandwich panels with PIR core 278.13 41.50 External Wall Sandwich panel, for walls, PIR insulation core 372.01 62.70 Internal Wall Steel stud internal wall assembly 305.32 34.73 To facilitate a better understanding of the material selection differences, Fig. 8 illustrates a side-by-side comparison of the life cycle costs. In contrast, Fig. 8 compares the life cycle assessment between generic and optimal materials. Figure 10 presents the non-dominant results of the NSGA-II algorithm for Australia, emphasizing the existence of multiple non-dominant solutions. Finally, Table 10 presents the percentage differences in costs and project life cycle assessments. A negative sign indicates a lower value in the optimal state, while a positive sign suggests the opposite. Since economic and environmental costs were considered important factors in the optimization process, the negative sign and lower optimal state are deemed more desirable. Table 10 The difference between LCC and LCA results after the optimization in Australia Australia Category Material Material LCC (USD) LCA ( KgCO2) Window Aluminum frame double-glazed Aluminum window frame single-glazed -102% 47% Door Cross laminated timber (CLT) Particleboard, standard, melamine coated -86% 78% Roof Corrugated steel sheet for roofing Roof with Sandwich panels with PIR core -119% 110% External Wall Brick sandwich wall assembly Sandwich panel, for walls, PIR insulation core 67% -82% Internal Wall Plasterboard wall Steel stud internal wall assembly -38% -126% By independently determining the optimum materials in the U.S. and Australia, then cross-comparing the solutions, opportunities emerge for optimal decision making. After analyzing each country separately, the optimal results from the United States and Australia are compared at the final stage. Table 11 compares the optimal materials between the two countries. The difference column indicates the disparity between Australia and the United States, with a negative value indicating lower costs or a lower life cycle assessment in Australia. Table 11 Comparison between optimal materials in North America and Australia Region North America Australia Difference (%) Category Material LCC (USD) LCA ( KgCO2) LCC (USD) LCA ( KgCO2) Material LCC LCA Window Wood window frame triple-glazed 978.01 169.25 771.26 433.10 Aluminum window frame single-glazed -27% 156% Door Cross laminated timber (CLT) 647.00 5.40 360.63 12.96 Particleboard, standard, melamine coated -79% 140% Roof Bitumen roofing membrane 385.90 14.90 278.13 41.50 Roof with Sandwich panels with PIR core -39% 179% External Wall Sandwich panel, for walls, PIR insulation core 399.00 41.31 372.01 62.70 Sandwich panel, for walls, PIR insulation core -7% 52% Internal Wall Steel stud internal wall assembly 288.00 45.74 305.32 34.73 Steel stud internal wall assembly 6% -32% The data in Table 11 reveals that the economic costs for optimal materials in Australia are significantly lower, while the environmental costs are considerably higher. This does not imply that Australia prioritizes materials with lower economic costs over those with lower environmental costs. Instead, it suggests that achieving materials with lower environmental costs in Australia requires a higher economic investment than in the United States. When economic and environmental costs are given equal weight in selecting optimal materials, the materials with higher economic costs in Australia are likely to be favored. Several factors contribute to this outcome: Market size: Australia's small population and market for sustainable building materials result in limited production and distribution, leading to higher costs. In contrast, the larger market in the United States allows for more efficient production and lower costs. Supply and demand: Australia's demand for sustainable building materials exceeds the available supply, increasing prices. Limited competition among suppliers further contributes to increased costs. Distance and transportation: Australia's geographical location necessitates long-distance transportation, which adds to the expenses associated with importing materials. These transportation costs contribute to the overall price of sustainable building materials. Regulations and standards: Australia has strict building codes and regulations regarding sustainability and energy efficiency. Meeting these requirements often involves additional research, development, and testing, which increase material costs. Labor costs: Australia generally has higher labor costs than the United States, which impacts the overall price of sustainable building materials. It is important to note that as demand increases and technology advances, the price gap between Australia and the United States may narrow over time. Additionally, although the difference in interest rates between the two countries is not substantial, it can significantly impact the final cost of the project's life cycle due to compound growth rules over 30 years. Conclusion This thesis aims to establish a decision-making framework for pre-construction activities, enabling the selection of optimal materials in specific geographical and local contexts. The framework serves as a simple method for environmental and economic analysis, facilitating decision-making in the early stages of a project. It comprises three key stages: life cycle cost analysis, project life cycle assessment analysis, and optimization algorithm presentation. By expanding the database and utilizing building information modeling (BIM) software, this framework can be widely adopted and yield improved performance under more optimal conditions. Furthermore, future research will explore the application of this framework to lay the groundwork for future environmental and economic analyses. Compared to traditional design methods, this study addresses multi-objective problems, such as cost minimization and life cycle assessment. Using life cycle analysis provides designers and clients with a comprehensive understanding of their projects, increasing the likelihood of success. Moreover, all steps in this process are executed through coding programs and software, reducing designers' workload and minimizing errors. Consequently, designers can obtain optimal designs more efficiently, contributing to the advancement of sustainable building development. An optimization algorithm was developed and utilized in North America to investigate the reduction potential in various aspects of building design. Our findings revealed significant improvements in cost and environmental effects across different building components. The largest reduction in roof area resulted in a remarkable 44% decrease in cost and a 14% reduction in environmental impact. Similarly, in Australia, we observed a substantial 27% cost reduction and an impressive 56% decrease in environmental effects for internal walls. Further analysis demonstrated that doors exhibited the most substantial reduction in environmental effects in the United States, with a notable decrease of 53%. In Australia, the environmental impact of internal walls decreased by 44%. These results highlight the potential for significant environmental improvements in building design. Interestingly, the study also revealed variations in the focus of improvements between the two countries. In the United States, most changes were related to environmental effects, while in Australia, the emphasis was on cost reduction. Notably, the environmental impacts in Australia were occasionally up to 179% higher than those in the United States. Due to the higher cost and impact of windows in both countries, the most significant changes were observed in this particular component. Although the cost of windows increased by $ 180 in the United States, we reduced the environmental impact by 59 kg CO 2 . Conversely, in Australia, window-related changes resulted in a cost decrease of $ 1166 and an increase of 149 kg CO 2 . Overall, our study highlights the effectiveness of the optimization algorithm in achieving substantial improvements in cost and environmental effects across various building components. These findings underscore the importance of considering economic and environmental factors in building design. They provide valuable insights into sustainable construction practices in North America and Australia. Declarations No potential conflict of interest was reported by the author(s). 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Falegari","suffix":""},{"id":279857993,"identity":"35220b68-4955-4276-b5d0-6d68b62ddfa3","order_by":2,"name":"Ali Akbar Shirzadi Javid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCcYGIMksxwYTYMOtFlWLMVAlmEWMFjDJnNgA00IQyM9ubvv4o8Y6vU8i/fkDhho7Bj7pA/i1GNw52DxD4lh6bptEjmEDw7FkBja+BAJaJBKbGQzYDoO0AB3GdoCBjYeQw2YAtST8O5zOJpH+sIHhHxFaGG4AtRxsO5zAJpFg2MDYRoQWA6AWxsa+dMM2njeGMxL7knmIcFj6Y8Yf36zl5dvTH3z48M1OTr6HkMNQQAIDA0GfjIJRMApGwSggAgAADio6GjvLNZ8AAAAASUVORK5CYII=","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"Akbar Shirzadi","lastName":"Javid","suffix":""}],"badges":[],"createdAt":"2024-03-10 08:01:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4062986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4062986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52931799,"identity":"4a40877c-04db-43d9-956f-bf7d2f990588","added_by":"auto","created_at":"2024-03-18 20:31:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30490,"visible":true,"origin":"","legend":"\u003cp\u003eLCC of the project versus LEED points in Badr city in Egypt [43]\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/7a434f0dbbf009cf1fdf1dc5.png"},{"id":52931443,"identity":"67897636-b7f4-4ef4-bd65-d7c78d02c50a","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":272114,"visible":true,"origin":"","legend":"\u003cp\u003eTwo set of optimizations results: a) Total Energy Consumption vs. LCC b) Total Energy Consumption vs LCA [63].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/226eaf8bbf4031641dfd664f.png"},{"id":52931444,"identity":"143859c4-9510-44fd-bc95-ce436986055a","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299108,"visible":true,"origin":"","legend":"\u003cp\u003eProposed steps of the research\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/019ce094c5ea49635c2290cf.jpeg"},{"id":52931441,"identity":"748b42a4-2797-499c-bc7f-87f2e6cfe137","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17959,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the model used in the research\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/2a695b627b1791c1cc490aa0.png"},{"id":52931800,"identity":"f6158857-d784-442c-a4a4-4bf55d85ea03","added_by":"auto","created_at":"2024-03-18 20:31:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13515,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in life cycle costs in optimal and generic materials\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/95eee5364fc371a9914f5b18.png"},{"id":52931447,"identity":"d9b4b48c-dbb9-4dcd-9472-f34f5eed3610","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13890,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences between life cycle assessment in optimal and generic materials\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/cadc854da5d3afd12e25e4c9.png"},{"id":52931448,"identity":"c9985c7e-d744-4734-a74c-26b65c417c6a","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101980,"visible":true,"origin":"","legend":"\u003cp\u003eNon-dominant answers in America\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/cc5aa80f7f2a36eed1e4b3ca.png"},{"id":52931446,"identity":"0027fea0-f579-4198-b0a6-7350ff36df47","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":11684,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in life cycle costs in optimal and generic materials\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/5338c0bd8378992d6af6dd6d.png"},{"id":52931801,"identity":"b67c2577-dc04-4c46-b5d2-43e2c05b82c4","added_by":"auto","created_at":"2024-03-18 20:31:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":26656,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences between life cycle assessment in optimal and generic materials\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/29a4500d92fe006efc9c038c.png"},{"id":52931450,"identity":"fdd169e2-c8c4-438e-9aac-2fedebc6173b","added_by":"auto","created_at":"2024-03-18 20:23:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":125808,"visible":true,"origin":"","legend":"\u003cp\u003eNon-dominant answers in Australia\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/ce15649f4256e49a51f3326a.png"},{"id":65819545,"identity":"2921c676-c465-4f5d-9153-82ed145bcd47","added_by":"auto","created_at":"2024-10-03 07:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1628532,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4062986/v1/08525449-7ff0-4002-83ad-6f29926077dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A BIM-based approach for multi-objective optimization of sustainable materials selection through life cycle analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe construction and buildings industry contributes significantly to greenhouse gas emissions, energy consumption, and waste generation worldwide, with a staggering 30%, 40%, and 32%, respectively (Y. L. Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nejat et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Housing, being the largest energy consumer in the building sector, accounting for over half of its total energy usage (Programme \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), plays a crucial role in mitigating environmental impacts. Without proactive measures, energy consumption and greenhouse gas emissions from buildings are projected to double by 2050 due to population growth and economic expansion (Hu \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past two decades, researchers have tried to develop and implement various methods for integrating the environmental and economic analysis of different technologies and systems. These methods range from micro turbines to large-scale infrastructures. Decision-makers face the challenge of managing and delivering economically feasible and environmentally sustainable projects (Miah, Koh, and Stone \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Sustainability, defined as meeting the needs of the present without compromising the ability of future generations to meet their own needs (Imperatives \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), is a key consideration in the building construction industry. The goal is to minimize environmental impacts and resource use while maximizing investment returns (Ding 2008). With rising environmental concerns and limited budgets, the architecture, engineering and construction (AEC) industry faces growing pressure to deliver sustainable and affordable building projects. Integrating life cycle assessment (LCA) and life cycle costing (LCC) into early design stages provides a systematic approach to evaluate environmental impacts and economic costs over a building\u0026rsquo;s lifespan. Life cycle assessment (LCA) is a valuable tool for evaluating sustainability in buildings (Romano, Negro, and Taucer 2014). It assesses the environmental impacts of a building throughout its entire life cycle, from material extraction to demolition (Akbarnezhad and Xiao 2017). Life cycle cost analysis (LCCA) complements LCA by evaluating the financial costs associated with different options (Kale, Joshi, and Menon 2016; Rad et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It balances initial construction costs with long-term energy savings to identify the most cost-effective strategies (K. P. Kim and Park 2018). However, conducting comprehensive LCA/LCC analyses manually can be resource intensive. Building information modeling (BIM) has emerged as a promising tool for streamlining and enhancing the accuracy of carbon emission assessments in buildings (Abbasi and Noorzai 2021; Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Safari and AzariJafari 2021; B Soust-Verdaguer, Llatas, and Moya \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Integration of life cycle assessment (LCA) and life cycle cost analysis (LCC) is facilitated by building information modeling (BIM), which connects material quantities, costs, and sustainability data (Barbini et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kylili et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Najjar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrior studies have focused on evaluating the operational and embodied energy levels of construction components to promote sustainability and reduce energy consumption (Parkinson, Parkinson, and de Dear 2019). By integrating this evaluation with Building Information Modeling (BIM), its application extends further. For instance, a combination of BIM and Building Energy Modeling has been employed to enhance operational energy efficiency in buildings and aid decision-makers during the early design phase (Gao, Koch, and Wu 2019). The outcomes of integrating BIM and LCA can also contribute to achieving other sustainability aspects in construction projects (D\u0026iacute;az and Ant\u0026ouml;n 2014). While numerous researchers have explored BIM-LCA integration (Gao, Koch, and Wu 2019; Bernardette Soust-Verdaguer, Llatas, and Garc\u0026iacute;a-Mart\u0026iacute;nez 2017a), there remains a gap in the application of mathematical optimization modeling to the decision-making process during the design phase and in optimizing energy performance through BIM-LCA integration to achieve energy-efficient buildings (Chen and Yang \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nevertheless, most studies focus solely on environmental impacts without considering economic trade-offs. Consequently, this study addresses the need for an integrated LCA-LCC-BIM approach optimizing both sustainability and costs. By combining LCA and LCCA, decision-makers can optimize sustainable retrofits that are both environmentally sound and financially feasible (Dauletbek and Zhou 2022; Schwartz, Raslan, and Mumovic 2016). This approach is supported by the similarities between LCA and LCCA, such as their timing, system scope, and analytical processes (Y. Shin, Engineering, and 2015 n.d.).\u003c/p\u003e \u003cp\u003eGiven the increasing significance of life cycle assessment in the construction industry, numerous techniques have been developed to apply this method effectively. The environmental performance of a building during its life cycle depends on various components, some of which are mentioned in the study. Building functionality, temperature requirements, consumption patterns, and resident behavior all influence energy consumption and greenhouse gas production. Additionally, regional climate variables are crucial for building performance during its life cycle evaluation. Different regions have distinct heating and cooling requirements based on their prevailing climates. This research investigated the potential benefits or drawbacks of adapting building design and life cycle assessment results to align with regional climatic needs. It also investigated whether such changes were economically worthwhile. The proposed framework entails three main steps. First, an extensive materials database is compiled containing LCA and LCC data. Next, BIM software models the building and assigns material alternatives. Finally, a multi-objective optimization algorithm determines optimal materials minimizing LCC and LCA impacts. This methodology is implemented on residential buildings in the US and Australia to examine regional variances. Compared to conventional materials, optimized selections reduced LCC by over 50% and greenhouse gases by up to 47% in the US. Significant cost and environmental savings demonstrate the value of integrated LCA-LCC optimization. Additionally, differing US and Australia results highlight the importance of localization.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eBuilding life cycle encompasses their system boundary, which defines the processes included in their assessment. While the importance of defining the system boundary has been emphasized in numerous studies, the EN 15978 (EN \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) standard, widely regarded as the most reliable standard in industry and academia, outlines the following life stages for building projects:\u003c/p\u003e \u003cp\u003eA: The embodied stage, which includes production and construction. B: The operation stage. C: The end-of-life stage.\u003c/p\u003e \u003cp\u003eIn addition to these fundamental system boundaries, some studies also investigate the benefits and outcomes beyond the life of materials (D) (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Integration of Building Information Modeling (BIM) into life cycle assessment\u003c/h2\u003e \u003cp\u003eBIM is a tool that reduces time and effort in managing building data. It can be used for cost and environmental analyses. BIM can be integrated with LCA and LCC in three ways: maintaining inventory lists, exporting models, and incorporating information. LOD 300 is often used for LCA and LCC (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A review paper (Bernardette Soust-Verdaguer, Llatas, and Garc\u0026iacute;a-Mart\u0026iacute;nez 2017b) critically examined research on integrating Building Information Modeling and Life Cycle Assessment for building applications. Fifteen case studies were analyzed to compare methodologies, results, limitations, and future recommendations. Major challenges identified included a lack of interoperability between software tools, system boundary limitations, and data reliability concerns. Further standardization of data exchange and mapping system boundaries could improve BIM-LCA capabilities.\u003c/p\u003e \u003cp\u003eIn another study (Bueno and Fabricio 2018), the authors compared a detailed, manual life cycle assessment following ISO standards versus a simplified LCA conducted using a BIM-LCA plugin tool. The study compared manual LCA with the BIM-LCA plugin for a residential building in Brazil. Manual LCA offered comprehensive impact analysis, while the BIM-LCA plugin was limited. BIM-LCA is helpful for early design but needs further development.\u003c/p\u003e \u003cp\u003eA study by Ajayi et al. (Ajayi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) utilized an integrated Building Information Modeling - Life Cycle Assessment approach to compare material specs for a residential building. Results showed the value of combining BIM and LCA early in design. The authors recommend further development of regional LCA data and integration with BIM.\u003c/p\u003e \u003cp\u003eFew researches ((Basbagill et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hollberg, Genova, and Habert 2020; Bernardette Soust-Verdaguer, Llatas, and Garc\u0026iacute;a-Mart\u0026iacute;nez 2017b)) examined the integration and application of life cycle assessment in building design. Soust-Verdaguer reviewed BIM-LCA integration, Hollberg evaluated the consistency in results between manual life cycle assessment methods versus automated BIM-LCA integration for building design, and Basbagill applied LCA early in design to reduce embodied impacts. The studies highlighted opportunities to leverage LCA to guide design optimization and material selection to improve building sustainability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Life Cycle Costs and Assessment\u003c/h2\u003e \u003cp\u003eLife cycle cost analysis involves the economic assessment of existing or potential future investments, considering short-term and long-term economic effects. Life cycle costs encompass the costs of an asset or its components throughout its life cycle while meeting performance requirements (ISO \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Life cycle cost calculations are used to improve the selection process by creating a reasonable structure regarding the economic performance of a project over its lifetime. Although life cycle costing has a long history since the 1930s, it is a relatively novel tool in sustainability.\u003c/p\u003e \u003cp\u003eIt is important to note that life cycle costs differ from total project life costs, with life cycle costs being a part of the overall costs. Typically, life cycle costs are divided into four parts to cover construction costs during their lifetime, including initial costs (construction costs), operation and maintenance costs, replacement costs, and end-of-life costs, which include the value of the building. Total project life costs encompass life cycle costs, externalities, non-construction costs, and revenue (Schau et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost life cycle cost research focuses on one or two phases, with few considering the entire life cycle. RS Means and Spons are widely used databases, while some use local or market price lists. NPV is the most common method, with discount rates from 2 to 1.6% (Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The current value can also be applied if the research duration focuses on a specific life stage (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing project life cycle costing methods at the beginning of the project is most effective. Therefore, managers and engineers often explore different options from an economic perspective, focusing on elements and construction methods (W. Li et al. n.d.). In most cases, life cycle costing presents all costs at their present value. The present value of future costs is estimated based on the future inflation rate and a discount rate. Future costs are calculated using Eq.\u0026nbsp;1 and converted into discounted costs using a specific discount rate defined in Eq.\u0026nbsp;2. For instance, research conducted by Islam et al. (Islam et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in Australia considered a 3% inflation rate, the ten-year average inflation, and a 6% discount rate based on the Australian Manufacturing Industry Organization proposal.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(FC=PV \\times {\\left(1+\\text{f}\\right)}^{n}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(DPV=FC/{\\left(1+\\text{d}\\right)}^{n}\\)\u003c/span\u003e \u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eFC represents future costs, PV indicates present value, DPV represents discounted present value, f is the inflation rate, d is the discount rate, and n is the years under consideration. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the formulas. The first formula accounts for costs at zero points, such as material, labor, and equipment. The second formula relates to annual costs associated with building use, such as energy costs and annual replacement or repair costs for various items. The third formula calculates replacement costs after a specified number of years. The fourth formula represents the current value of the building after the research period (the building's service life), where F represents the residential value of the building after n years in the future.\u003c/p\u003e \u003cp\u003eIn a study by Marzuk et al. (M Marzouk, Azab, and Metawie \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a system dynamics model was used as a decision-making tool to select green materials for affordable, sustainable housing. The model was combined with the LEED index (Yung, Robotic, and 2014 2014) and a genetic algorithm to optimize life cycle costs. Marzuk et al. (Yung, Robotic, and 2014 2014) developed a framework to determine the timing of affordable housing projects and select the most suitable alternative materials based on their sustainability aspects. The framework was tested on a 5-story building in Badr, Egypt. It was found that achieving 8 out of 11 possible LEED points was less than \u003cspan\u003e$\u003c/span\u003e2.25\u0026nbsp;million. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the effect of higher costs on obtaining more LEED points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study concluded that sustainable materials have significantly lower operating costs than traditional materials.\u003c/p\u003e \u003cp\u003eMarzuk et al. (Mohamed Marzouk, Azab, and Metawie \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also discussed the use of BIM, genetic algorithms, and Monte Carlo simulation in a study to identify the best and most suitable building materials from economic and environmental perspectives. The environmental aspect was evaluated using the maximum LEED score. The study also examined which buildings were most affected by changes. It is worth mentioning that this article builds upon previous work by utilizing two objective functions, economic and environmental, through a genetic algorithm. Additionally, the article emphasizes the impact of uncertainty on the building system from economic and environmental perspectives. A 3D model of BIM was created, and different materials were assigned to various building components. A sensitivity analysis was conducted to identify the cost components with the most significant impact on the project for each building system.\u003c/p\u003e \u003cp\u003eLife cycle assessment (LCA) utilizes various indicators to evaluate environmental impacts, including carbon gas emissions, energy consumption, acidification potential (AP), eutrophication potential (EP), the abiotic depletion potential of materials (ADPM), human health respiratory effects potential (HHREP), photochemical ozone creation potential (POCP), and ozone depletion potential (ODP). Among these indicators, energy is the most important and commonly used. It is often analyzed through carbon dioxide emissions, global warming potential, greenhouse gas emissions, and carbon footprint. Several databases, such as Athena, the Inventory of Carbon \u0026amp; Energy (ICE), GaBi, and Ecoinvent, are frequently employed in LCA research. Local databases like the Korea Life Cycle Inventory and Belgium EPDs have also been used (Lu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese three literature reviews (Buyle, Braet, and Audenaert 2013; Cabeza et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) summarize the state of research on applying life cycle assessment to evaluate the environmental impacts of buildings and the construction sector. The reviews covered common LCA goals, scopes, methodologies, impact indicators, findings, limitations, and future directions. Key results highlighted the significance of the use phase and benefits of LCA to inform building design and material selection to improve sustainability.\u003c/p\u003e \u003cp\u003eApart from life cycle costs, significant attention has been directed towards greenhouse gas production in all industries worldwide, with buildings being a major contributor. In the United States, buildings account for over 39% of total primary energy consumption and greenhouse gas production (Hong et al. n.d.). Consequently, numerous studies have been conducted to assess and mitigate greenhouse gas emissions, with life cycle assessment (LCA) being widely employed. LCA encompasses greenhouse gas emissions and other assessments throughout the process, including product production, handling, assembly, operation, and disposal (Y. S. Shin et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarizes the disparity between life cycle costs and life cycle assessment (Zhao, Huppes, and Van der Voet 2011).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLCA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision-making point of view\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvestor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esupply chain actors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor economic evaluation of business decisions and options\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo assess the environmental impact of products and processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnly direct costs or direct profits from an investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll stages of the life cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrency (eg. Dollar, pound and ...)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnits of mass and energy (eg. Kg, kWh and ...)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjust costs over a period of time to reflect the effect of time using various discounting methods such as net present value.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe effect of time on environmental impacts is usually not considered, especially its decrease or increase over time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn most researches, the interest rate is effective, but sometimes it is not taken into account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture environmental effects are usually not factored into results proportionally to time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Life Cycle Sustainability Assessment\u003c/h2\u003e \u003cp\u003eLife cycle sustainability assessment incorporates sustainability's economic, social, and environmental dimensions. It utilizes three methods: life cycle costs (economic), life cycle assessment (environmental), and social assessment of life cycle costs (social) (K. P. Kim and Park 2018). The social aspect of sustainability assessment encompasses research on indoor and outdoor user comfort, user security, human resources, and the learning process. Security-related analyses contribute significantly to social aspect. Recent research has also explored the benefits of Building Information Modeling (BIM) for preserving and assessing cultural heritage. However, integrating social aspects with economic and environmental aspects has been limited, with individual investigations conducted (Santos et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBIM for sustainability faces integration, sustainability understanding, and library availability challenges. Current research focuses on costs, life cycle assessment, certifications, and sensors (J.-U. Kim et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study by Alwan et al. (Alwan, Jones, and Holgate 2017) proposed a novel methodology to integrate the Framework for Strategic Sustainable Development (FSSD) principles into Building Information Modeling (BIM) workflows for construction projects. The aim was to guide sustainability transformations in the built environment by bringing strategic planning into BIM and design processes. A case study of a commercial building redevelopment project in the UK was presented. The FSSD principles were used to identify sustainability challenges and develop strategic action plans. BIM offers various tools to model and assess sustainability strategies related to energy, materials, and construction techniques. The integrated FSSD-BIM approach helped develop more sustainable building design concepts than conventional methods. Further validation across more extensive case studies was recommended.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Combination of Life Cycle Assessment and Life Cycle Costs and Optimization\u003c/h2\u003e \u003cp\u003eLife cycle assessment and costing share three common characteristics: 1) their impact is maximized when applied early in the project. 2) They can be applied to a production system encompassing all building elements and construction methods. 3) They provide an analytical platform for selecting the most optimal option by considering economic and environmental resources (Y. S. Shin et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study by Ferreira et al. (J. Ferreira, Pinheiro, and De Brito 2015) analyzed the economic and environmental savings of adding structural insulation to a residential building in Portugal over its life cycle. The life cycle assessment and life cycle cost analysis determined the optimal insulation thickness that minimized costs and carbon footprint\u0026mdash;adding insulation significantly reduced space heating needs and emissions but added material and installation costs. The results provided an optimal insulation level for this building type and climate, demonstrating the importance of life cycle optimization to balance multiple objectives.\u003c/p\u003e \u003cp\u003eLiu et al. (Liu, Meng, and Tam \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conducted a study to optimize building design and enhance stability using a beam-based optimization method. The project's life cycle was considered, considering life cycle costs and carbon dioxide gas emissions. The Ecoect Analysis software was utilized to assess the life cycle impacts of the light and heat system. The study incorporated decision-making variables such as wall type, window-to-wall ratio, window glazing type, exterior canopy, and building orientation. The particle swarm optimization algorithm was employed for optimization purposes, as it is known for effectively solving multi-objective optimization problems. A significant aspect of this research was utilizing a work breakdown structure (WBS) to allocate resources and separately analyze direct and indirect costs and carbon emissions for different activities. However, the destruction and maintenance stages were not considered due to incomplete databases and the significant uncertainty associated with material repair and recycling. The proposed method was implemented on a commercial building in Hong Kong, demonstrating its positive impact on both economic and environmental aspects. The processing time remained relatively unchanged despite expanding the investigation range to find the best elements.\u003c/p\u003e \u003cp\u003eIn a separate study, Islam et al. (Islam et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) aimed to find an optimal balance between life cycle costs and environmental impacts in Australian residential buildings. The research focused on two objective functions: project life cycle costs and environmental effects, which were evaluated through life cycle cost and life cycle assessment methods. Alternative options were considered for a basic house with 18 walls and four floors. Linear programming was employed as the optimization algorithm for single and multi-objective functions. Weighting coefficients were also used to compare different scenarios. The study concluded that a house with fixed life cycle costs could have up to 20% less environmental impact. PR\u0026eacute;\u0026rsquo;s SimaPro software (Jrade and Jalaei 2013) was used for life cycle assessment, with the Ecoinvent database (Swiss) and Australian Lifecycle Bank (Akbarnezhad, Ong, and Chandra 2014) serving as the primary data sources. The research investigated four factors related to life cycle assessment: greenhouse gas (GHG) emissions (tCO\u003csup\u003e2\u003c/sup\u003e-eq), cumulative energy demand (CED) (GJ), water usage (kL), and solid waste generation (tonne). A separate database (Rawlinsons \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was utilized to estimate life cycle costs, assuming a building service life of 50 years. The study revealed variations in life cycle greenhouse gas emissions (approximately 20%), cumulative energy demand (approximately 15%), water usage (approximately 26%), waste production (approximately 29%), and costs (approximately 22%) across different scenarios. No single design achieved the lowest life cycle costs and environmental effects, highlighting the need for optimization methods to obtain the best design.\u003c/p\u003e \u003cp\u003eThere are three studies focused on combining life cycle cost analysis and optimization to improve the energy efficiency of residential and commercial buildings. Kneifel (Kneifel \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) developed a method to optimize energy efficiency measures in commercial buildings by minimizing life cycle costs and carbon emissions. Ascione et al. (Ascione et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Gustafsson (Gustafsson \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) applied optimization techniques to determine cost-effective insulation levels for retrofitting existing buildings, one residential or another office building, to balance insulation costs with heating energy savings over the lifespan. The research demonstrated the value of optimization methods using simulations and life cycle cost models to support cost-effective building energy efficiency.\u003c/p\u003e \u003cp\u003eThese two articles (Congedo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M. Ferreira et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) optimized school and office buildings to achieve net zero energy status. Ferreira compared cost-optimal versus net zero energy retrofits in Portugal. Congedo used optimization to design net zero schools and offices in Italy. The results quantified the costs and challenges of achieving net zero buildings in Mediterranean climates.\u003c/p\u003e \u003cp\u003eIn another research endeavor, Sharif et al. (Sharif and Hammad 2019) developed a method to optimize the selection of appropriate renovation strategies for educational buildings. This method considers energy consumption, life cycle costs, and environmental impacts within budget constraints. The study focused on building elements, ventilation systems (Abanda and Byers \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and lighting systems. The research model consisted of four main parts: 1) collection of input data, 2) expansion of the database, 3) definition of reconstruction methods, and 4) multi-objective optimization based on simulation. The input data encompassed various parameters required for future calculations, including price limits and insurance models. The first phase involved determining input data and expanding the beam model. Databases containing information on building materials, ventilation systems, lighting systems, and environmental and economic data were utilized. The third phase combined the previous steps' information to generate different reconstruction strategies. This phase included determining energy performance goals, expanding reconstruction strategies, searching databases for the best equivalents, and evaluating the suitability of reconstruction methods for each strategy. The process was repeated until all reconstruction methods were exhausted. The final phase involved using MATLAB software to select the best strategy. Optimization results were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, showcasing two functions: total energy consumption and life cycle costs and total energy consumption and life cycle assessment. The left figure demonstrated a more diverse Pareto front. This indicates that using this function for optimization provided a more comprehensive range of options than the right figure, where the Pareto front members were nearly identical.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis research aims to identify the most suitable materials for residential houses based on their life cycle costs. The study uses building information modeling software to evaluate life cycle and climatic variables. The architecture, engineering, and construction industry significantly contributes to global energy consumption. One way to assess its energy consumption and environmental impact is through a life cycle assessment. However, conducting a comprehensive analysis often exceeds specific software capabilities due to the extensive data and inputs required. Additionally, the dynamic nature of life cycle analysis and the potential for changing variables during a product's life can lead to initial inaccuracies. Therefore, it is crucial to present life cycle results early in the design process to facilitate cost-effective adjustments. In this research, a range of fixed materials in two different regions were placed (The state of Minnesota in North America and the city of Darwin in the northern region of Australia), and optimization techniques were used to select the best materials for each location (In the following, America and Australia are used, which mean the same city and state mentioned). Finally, the impact of this optimal choice on the most commonly used materials was compared. It is important to note that optimal materials may vary across different regions due to climatic differences (Abd Rashid and Yusoff 2015).\u003c/p\u003e \u003cp\u003eSeveral justifications underpin the selection of these two regions as research samples. The key reasons include the following:\u003c/p\u003e \u003cp\u003eComparable Economic Development: The United States and Australia share the status of developed nations with advanced economies. Their comparable levels of industrialization, technological advancements, and consumption patterns make them suitable candidates for comparative analysis.\u003c/p\u003e \u003cp\u003eDiverse Environmental Landscapes: The United States and Australia boast diverse environmental landscapes, encompassing forests, deserts, coastal regions, and unique ecosystems. Ecological diversity provides an opportunity to comprehensively examine various environmental impacts and cost factors.\u003c/p\u003e \u003cp\u003eAbundance of Resources: Both countries possess abundant natural resources, including minerals, fossil fuels, and agricultural land. Resource richness significantly influences environmental impact and cost considerations, rendering them pertinent subjects for comparison.\u003c/p\u003e \u003cp\u003eGlobal Significance: The United States and Australia hold significant global influence, and their environmental policies and practices can have far-reaching consequences. We can contribute to a broader understanding of global sustainability endeavors by scrutinizing their environmental impact and cost.\u003c/p\u003e \u003cp\u003eMinnesota has a temperate continental climate with very cold winters, while Darwin has a tropical wet and dry climate with a distinct wet and dry season. Comparing energy use, transportation across two different climates could yield insights. Comparing two disparate regions like Minnesota and Darwin with a life cycle assessment can rigorously test the flexibility of this methodology itself, as well as highlight any data or inventory analysis gaps that may emerge when applying it across different geographical and cultural contexts.\u003c/p\u003e \u003cp\u003eConsidering these reasons and the possibility of more accessible access to environmental and cost data, these regions were the best options for research.\u003c/p\u003e \u003cp\u003eThe research methodology is as follows:\u003c/p\u003e \u003cp\u003eStep 1: Building a Comprehensive Material Database:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGather detailed information about various materials to establish a robust database. Utilizing One Click LCA software streamlines this process by providing life cycle assessment results within specified timeframes for diverse materials.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEmploy country-specific databases like RSMEANS (US) and Rawlinson (Australia) to calculate accurate life cycle costs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConsider all relevant costs including initial construction, installation (labor \u0026amp; consumables), annual maintenance, and residual value. Presenting these costs upfront empowers informed decision-making throughout the project.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNote that annual maintenance includes potential repairs or replacements. Transportation costs were deemed negligible in this study due to project stage and minimal fuel price variations. This omission likely has minimal impact on results.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStep 2: Optimizing with Advanced Algorithms:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLeverage the NSGA-II algorithm implemented within MATLAB software to tackle the project's multi-objective nature.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUtilize Autodesk Revit 2022 building information modeling software for creating 3D models. Material selection occurs from pre-existing families within its material database, each containing life cycle information. These families can be further expanded for future use.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExport the family materials with life cycle data into Excel and subsequently import them into MATLAB.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUpon identifying the optimal solution, calculate and report the percentage improvement in cost and environmental impact compared to the initial scenario.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStep 3: Comparative Analysis of Optimal Solutions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eConduct a comparative analysis of the optimal solutions for both buildings located in different countries. While selecting the same material for both is technically possible, distinct regional factors are likely to yield different results.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRemember that a richer and more comprehensive database significantly increases the probability of achieving highly optimized outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe workflow of this research closely follows the flowchart presented in the article by Sharif and Hammad (Sharif and Hammad 2019).\u003c/p\u003e \u003cp\u003eThe work steps can also be summarized as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Creating a database\u003c/h2\u003e \u003cp\u003eThis section performs a life cycle assessment based on the One Click LCA software specifications, whose online version is also available. The calculations for life cycle assessments vary based on factors such as the type of electricity and fuel used, heating and cooling methods, energy consumption, building location, and other considerations. These factors influence the numbers used to calculate life cycle assessments obtained from the software. Environmental effects can be assessed using factors such as global warming, ozone layer depletion, acidification, eutrophication, tropospheric ozone formation, and energy source depletion. For this research, the primarily focus is on global warming, using kilograms of CO\u003csup\u003e2\u003c/sup\u003e equivalent as the unit of measurement.\u003c/p\u003e \u003cp\u003eWhile the One Click LCA software can also calculate life cycle costs, it does not provide a dedicated database. Therefore, the researchers relied on other databases to obtain cost data. For North America and Canada, the RSMEANS 2012 database is utilized. The costs were adjusted to 2021 values to account for inflation based on the Federal Reserve's annual conversion rate of 0.64%. In Australia, the Rawlinsons 2021 database is used, which provides separate cost data for five different regions. The Darwin region, located in the northernmost part of Australia, was considered the primary region near the sea to ensure consistency.\u003c/p\u003e \u003cp\u003eIt is important to note that the calculations do not consider water-related information and Part B7 of the life cycle components. Additionally, due to the early stage of the project and the lack of information regarding material suppliers and their locations, transportation costs (A2, A4, and C2) were excluded from our analysis. The research period spans 30 years, chosen to avoid equipment replacement and remain within the service interval of building components, thereby minimizing potential errors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Introducing the model\u003c/h2\u003e \u003cp\u003eThe model used in this research was developed in Autodesk Revit software and is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The model covers 246.5 m\u003csup\u003e2\u003c/sup\u003e. This model shows a residential house with four residents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe decision-making variables considered in this research include the building entrance, building windows, internal walls, external walls, and the roof. The model features one external door, five internal doors (without differentiation), and eight windows. It is important to note that the modeled walls are non-structural and primarily investigated for their shell role. Therefore, the building structure differs from the presented shell model.\u003c/p\u003e \u003cp\u003eSlight adjustments were made based on the available information to tailor each family to its specific materials. The cost and description of items were modified within the identity data section based on Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) data, respectively. Material description is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the materials used for different building elements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, single-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, double-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, triple-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with wood framing, double-glazed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with wood framing, triple-glazed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from cross laminated timber (CLT), 470 kg/m3, moisture content 12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Particleboard, standard, melamine coated, 18 mm, 11.8 kg/m2, 656 kg/m3, 7% moisture content\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Medium density fibreboard (MDF), moisture resistant, melamine coated, 18 mm, 13 kg/m2, 721 kg/m3, 7% moisture content\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Plywood, maritime pine, 18mm, 11.1kg/m2, 616.7kg/m3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium density fibreboard (MDF), standard, melamine coated, 16 mm, 11.6 kg/m2, 724 kg/m3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for roofs, R\u0026thinsp;=\u0026thinsp;2.34 m2K/W, PIR insulation core 40 mm, 9.3 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitumen roofing membrane, 20 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panels with PIR core and double steel siding, for roof and wall application, U\u0026thinsp;=\u0026thinsp;0.18\u0026ndash;0.28 W/m2K, R\u0026thinsp;=\u0026thinsp;5.64 m2K/W\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecast concrete roof slab, 117.63 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfiled steel sheet for roofing, 8.96 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLT external wall assembly, 5-ply, incl. mineral wool insulation and plasterboard, U-value 0.18 W/m2K, 310 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAerated concrete block wall assembly, incl. interior insulation and plaster covering, R\u0026thinsp;\u0026gt;\u0026thinsp;5m2.K/W\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLightweight aggregate (LECA) block external wall assembly, incl. mineral wool insulation and timber frame, U-value 0.18 W/m2K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for walls, R\u0026thinsp;=\u0026thinsp;7.15 m2K/W, PIR insulation core 120 mm, 14.3 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick sandwich wall assembly, incl. mineral wool insulation, U-value 0.28 W/m2K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panels with PIR high-density foam core and double steel siding, wall application, 12.356 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcrete block internal wall assembly, incl. render\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHollow clay bricks internal wall assembly, incl. mineral wool insulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteel stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWooden stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBy incorporating these data along with the keynote section, new insights into the unique characteristics of each material was gained and material properties was extracted from the model when utilizing only these two data points. Subsequently, when transferring the data to MATLAB software, each material in the optimization algorithm was included, with the outcome determined by the keynote. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Presents the modified family sample from external information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModified family sample from external information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentity Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeynote\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL10/210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType image\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMANUFACTURE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType comments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssembly code\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides examples of materials used in the model. After selecting one of the most commonly used and accessible materials in each country, their life cycle costs and life cycle assessments was calculated. Subsequently, incorporate these materials were incorporated into the model and export the data to Excel for further analysis and transfer to MATLAB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterials used in the model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, single-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, double-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with aluminium framing, triple-glazed, 47.7 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with wood framing, double-glazed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment windows with wood framing, triple-glazed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from cross laminated timber (CLT), 470 kg/m3, moisture content 12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Particleboard, standard, melamine coated, 18 mm, 11.8 kg/m2, 656 kg/m3, 7% moisture content\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Medium density fibreboard (MDF), moisture resistant, melamine coated, 18 mm, 13 kg/m2, 721 kg/m3, 7% moisture content\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoor from Plywood, maritime pine, 18mm, 11.1kg/m2, 616.7kg/m3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium density fibreboard (MDF), standard, melamine coated, 16 mm, 11.6 kg/m2, 724 kg/m3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for roofs, R\u0026thinsp;=\u0026thinsp;2.34 m2K/W, PIR insulation core 40 mm, 9.3 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitumen roofing membrane, 20 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panels with PIR core and double steel siding, for roof and wall application, U\u0026thinsp;=\u0026thinsp;0.18\u0026ndash;0.28 W/m2K, R\u0026thinsp;=\u0026thinsp;5.64 m2K/W\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecast concrete roof slab, 117.63 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfiled steel sheet for roofing, 8.96 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLT external wall assembly, 5-ply, incl. mineral wool insulation and plasterboard, U-value 0.18 W/m2K, 310 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAerated concrete block wall assembly, incl. interior insulation and plaster covering, R\u0026thinsp;\u0026gt;\u0026thinsp;5m2.K/W\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLightweight aggregate (LECA) block external wall assembly, incl. mineral wool insulation and timber frame, U-value 0.18 W/m2K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for walls, R\u0026thinsp;=\u0026thinsp;7.15 m2K/W, PIR insulation core 120 mm, 14.3 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick sandwich wall assembly, incl. mineral wool insulation, U-value 0.28 W/m2K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panels with PIR high-density foam core and double steel siding, wall application, 12.356 kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcrete block internal wall assembly, incl. render\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHollow clay bricks internal wall assembly, incl. mineral wool insulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteel stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWooden stud internal wall assembly, 100 mm, incl. mineral wool insulation and double gypsumboard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Optimization algorithm\u003c/h2\u003e \u003cp\u003eThe optimization algorithm employed in this research is NSGA-II, a genetic algorithm designed for multi-objective problems. We aim to minimize two cost functions: life cycle costs and life cycle assessment. However, these functions do not move in the same direction. Using stronger and more advanced materials with better construction quality naturally increases costs. Therefore, there may be a need to consider materials with higher or more negative life cycle assessment results to lower costs and vice versa.\u003c/p\u003e \u003cp\u003eThe implementation of the NSGA-II algorithm involves six steps:\u003c/p\u003e \u003cp\u003eSelect the optimization model parameters, including the number of generations, initial population size, mutation rate, and crossover rate. Generate an initial population ready for crossover and mutation operations.\u003c/p\u003e \u003cp\u003eDefine an objective function that assigns a score to each input number based on continuous issues. In discrete problems like the one at hand, where the number of options is limited, the points assigned to each material for the project's life cycle costs and assessment functions was determined.\u003c/p\u003e \u003cp\u003eSelect the non-dominated answers (the first in front of the dominant answers).\u003c/p\u003e \u003cp\u003eEliminate the answers from the first front and repeat the previous steps to identify the second front of dominant answers. This front can only be dominated by the same first front.\u003c/p\u003e \u003cp\u003eRepeat the non-dominant sorting process and assign a proportional score to each answer.\u003c/p\u003e \u003cp\u003eCreate a new child population using genetic algorithm operations such as selection, crossover, and mutation. Repeat the process until reaching the specified algorithm limits (Mohamed Marzouk, Azab, and Metawie \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe algorithm settings were as follows: Five decision variables and five options were given as answers to each variable. The initial population consisted of 30 members. The maximum number of iterations is 50. The crossover percentage was 70%, and the mutation percentage was 10%. It should be noted that the mutation rate was also 2%.\u003c/p\u003e \u003cp\u003eThe Excel data from the model's output into MATLAB were imported and select the optimal point based on the created database. The percentage of optimization that was reported is achieved under the model's conditions compared to the initial model. Finally, the final models for Australia and America was compared, examining the differences and similarities between the selected materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cp\u003eThis chapter discusses the analysis and interpretation of the results obtained, as well as the relationship between these results. Initially, a framework was developed to select the best materials for the North American region, and the outcomes were compared with those obtained from generic materials. Subsequently, a similar process was performed for the northern part of Australia. Finally, the optimal results from both countries were compared, and a comprehensive examination and analysis of the findings was conducted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOptimal Results in North America\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the generic materials used in America and their corresponding life cycle costs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLCC and LCA of generic materials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood-aluminium window, triple insulated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e798.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiberglass reinforced PE door\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e723.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsphalt shingle roofing system, fiberglass reinforced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e687.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimber frame external wall assembly, incl. mineral wool insulation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.37\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWooden stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.55\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAll costs are presented in US dollars to ensure consistency. Future years' costs were converted to their present value using a 1% interest rate based on information from the US Office of Management and Budget (OMB). The final results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e after applying the optimization algorithm with two genetic objectives to the provided database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLCC and LCA of optimal materials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLCC (USD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCA (KgCO2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood window frame triple-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e978.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross laminated timber (CLT)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e647.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitumen roofing membrane\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.74\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSignificant differences between generic and optimal materials were observed in all cases, as Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the disparity in life cycle costs between the two modes. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e showcases the variation in life cycle assessment between generic and optimal modes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provides a detailed breakdown of each category's selected materials and percentage differences. It is worth noting that a positive sign indicates that the optimal number is greater than the generic number, while a negative sign suggests the opposite. The optimal selection considers both factors simultaneously, resulting in varying percentages. In some cases, the most recommended materials align with the optimal materials. This indicates that the materials used in practice have minimal environmental impact while considering costs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference between LCC and LCA results after the optimization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCC difference (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLCA difference (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood-aluminium window, triple insulated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWood window frame triple-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-35%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiberglass reinforced PE door\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross laminated timber (CLT), moisture content 12%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-114%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsphalt shingle roofing system, fiberglass reinforced\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBitumen roofing membrane\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimber frame external wall assembly, incl. mineral wool insulation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-97%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWooden stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSimilar analyses were conducted for Australia; the corresponding results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLCC and LCA of generic materials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum frame double-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2312.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross laminated timber (CLT)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e671.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrugated steel sheet for roofing\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e610.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick sandwich wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasterboard wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.33\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn Australia, as in the United States, costs are measured in US dollars (converted from Australian dollar), and the project's life cycle assessment is measured in kilograms of carbon dioxide. The interest rate used is 1.4%, based on information from the Australian Reserve Bank.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the non-dominant solutions obtained from the NSGA-II algorithm for the United States, highlighting the multiple correct solutions to a two-objective optimization problem. Based on the depicted figure, it is evident that there exist eleven optimal solutions for the problem at hand. Among these solutions, some exhibit a more pronounced cost reduction, while others demonstrate a more significant decrease in environmental assessment. Ultimately, none of these cases can be deemed superior to the others, as each represents a valid and optimal outcome based on the provided data. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows cases of optimal materials obtained through the algorithm for Australia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLCC and LCA of optimal materials in Australia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum frame single-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1146.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e464.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticleboard, standard, melamine coated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoof with Sandwich panels with PIR core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.70\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.73\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTo facilitate a better understanding of the material selection differences, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates a side-by-side comparison of the life cycle costs. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e compares the life cycle assessment between generic and optimal materials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the non-dominant results of the NSGA-II algorithm for Australia, emphasizing the existence of multiple non-dominant solutions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the percentage differences in costs and project life cycle assessments. A negative sign indicates a lower value in the optimal state, while a positive sign suggests the opposite. Since economic and environmental costs were considered important factors in the optimization process, the negative sign and lower optimal state are deemed more desirable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference between LCC and LCA results after the optimization in Australia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCC (USD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLCA (\u003c/b\u003eKgCO2)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminum frame double-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAluminum window frame single-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-102%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross laminated timber (CLT)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticleboard, standard, melamine coated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-86%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrugated steel sheet for roofing\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoof with Sandwich panels with PIR core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-119%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick sandwich wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-82%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasterboard wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-38%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-126%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eBy independently determining the optimum materials in the U.S. and Australia, then cross-comparing the solutions, opportunities emerge for optimal decision making.\u003c/p\u003e \u003cp\u003eAfter analyzing each country separately, the optimal results from the United States and Australia are compared at the final stage. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e compares the optimal materials between the two countries. The difference column indicates the disparity between Australia and the United States, with a negative value indicating lower costs or a lower life cycle assessment in Australia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between optimal materials in North America and Australia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eDifference (%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLCC (USD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLCA (\u003c/b\u003eKgCO2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLCC (USD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLCA (\u003c/b\u003eKgCO2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMaterial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eLCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eLCA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood window frame triple-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e978.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e771.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e433.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAluminum window frame single-glazed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-27%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e156%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross laminated timber (CLT)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e647.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParticleboard, standard, melamine coated\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-79%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e140%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitumen roofing membrane\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRoof with Sandwich panels with PIR core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-39%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e179%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e372.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSandwich panel, for walls, PIR insulation core\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-7%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal Wall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSteel stud internal wall assembly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-32%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e reveals that the economic costs for optimal materials in Australia are significantly lower, while the environmental costs are considerably higher. This does not imply that Australia prioritizes materials with lower economic costs over those with lower environmental costs. Instead, it suggests that achieving materials with lower environmental costs in Australia requires a higher economic investment than in the United States. When economic and environmental costs are given equal weight in selecting optimal materials, the materials with higher economic costs in Australia are likely to be favored. Several factors contribute to this outcome:\u003c/p\u003e \u003cp\u003eMarket size: Australia's small population and market for sustainable building materials result in limited production and distribution, leading to higher costs. In contrast, the larger market in the United States allows for more efficient production and lower costs.\u003c/p\u003e \u003cp\u003eSupply and demand: Australia's demand for sustainable building materials exceeds the available supply, increasing prices. Limited competition among suppliers further contributes to increased costs.\u003c/p\u003e \u003cp\u003eDistance and transportation: Australia's geographical location necessitates long-distance transportation, which adds to the expenses associated with importing materials. These transportation costs contribute to the overall price of sustainable building materials.\u003c/p\u003e \u003cp\u003eRegulations and standards: Australia has strict building codes and regulations regarding sustainability and energy efficiency. Meeting these requirements often involves additional research, development, and testing, which increase material costs.\u003c/p\u003e \u003cp\u003eLabor costs: Australia generally has higher labor costs than the United States, which impacts the overall price of sustainable building materials.\u003c/p\u003e \u003cp\u003eIt is important to note that as demand increases and technology advances, the price gap between Australia and the United States may narrow over time. Additionally, although the difference in interest rates between the two countries is not substantial, it can significantly impact the final cost of the project's life cycle due to compound growth rules over 30 years.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis thesis aims to establish a decision-making framework for pre-construction activities, enabling the selection of optimal materials in specific geographical and local contexts. The framework serves as a simple method for environmental and economic analysis, facilitating decision-making in the early stages of a project. It comprises three key stages: life cycle cost analysis, project life cycle assessment analysis, and optimization algorithm presentation. By expanding the database and utilizing building information modeling (BIM) software, this framework can be widely adopted and yield improved performance under more optimal conditions. Furthermore, future research will explore the application of this framework to lay the groundwork for future environmental and economic analyses.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eCompared to traditional design methods, this study addresses multi-objective problems, such as cost minimization and life cycle assessment. Using life cycle analysis provides designers and clients with a comprehensive understanding of their projects, increasing the likelihood of success. Moreover, all steps in this process are executed through coding programs and software, reducing designers' workload and minimizing errors. Consequently, designers can obtain optimal designs more efficiently, contributing to the advancement of sustainable building development.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAn optimization algorithm was developed and utilized in North America to investigate the reduction potential in various aspects of building design. Our findings revealed significant improvements in cost and environmental effects across different building components. The largest reduction in roof area resulted in a remarkable 44% decrease in cost and a 14% reduction in environmental impact. Similarly, in Australia, we observed a substantial 27% cost reduction and an impressive 56% decrease in environmental effects for internal walls.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFurther analysis demonstrated that doors exhibited the most substantial reduction in environmental effects in the United States, with a notable decrease of 53%. In Australia, the environmental impact of internal walls decreased by 44%. These results highlight the potential for significant environmental improvements in building design.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInterestingly, the study also revealed variations in the focus of improvements between the two countries. In the United States, most changes were related to environmental effects, while in Australia, the emphasis was on cost reduction. Notably, the environmental impacts in Australia were occasionally up to 179% higher than those in the United States.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDue to the higher cost and impact of windows in both countries, the most significant changes were observed in this particular component. Although the cost of windows increased by \u003cspan\u003e$\u003c/span\u003e180 in the United States, we reduced the environmental impact by 59 kg CO\u003csup\u003e2\u003c/sup\u003e. Conversely, in Australia, window-related changes resulted in a cost decrease of \u003cspan\u003e$\u003c/span\u003e1166 and an increase of 149 kg CO\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, our study highlights the effectiveness of the optimization algorithm in achieving substantial improvements in cost and environmental effects across various building components. These findings underscore the importance of considering economic and environmental factors in building design. They provide valuable insights into sustainable construction practices in North America and Australia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\nNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [Ali Akbar Shirzadi Javid]; Methodology: [Mehran Jani]; Formal analysis and investigation: [Mehran Jani]; Writing ‐ original draft preparation: [Mehran Jani, Sahar Falegari]; Writing ‐ review and editing: [Mehran Jani, Sahar Falegari]; Funding acquisition: [Ali Akbar Shirzadi Javid]; Resources: [Mehran Jani, Sahar Falegari]; Supervision: [Ali Akbar Shirzadi Javid]\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbanda, F H, and L Byers. 2016. \u0026ldquo;An Investigation of the Impact of Building Orientation on Energy Consumption in a Domestic Building Using Emerging BIM (Building Information Modelling).\u0026rdquo; 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Waste management 31(6): 1407\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Life cycle assessment, life cycle cost, multi-objective optimization, BIM-based LCA, conceptual design","lastPublishedDoi":"10.21203/rs.3.rs-4062986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4062986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven the increasing need for design coupled with constrained financial resources, a comprehensive approach that combines life cycle assessment (LCA), life cycle cost analysis (LCCA), and multi-dimensional optimization was suggested to develop a decision-making framework for cost-effective buildings. The proposed framework considers various aspects such as performance, economic considerations, and environmental factors. Integrating environmental and economic analysis into building construction and design was explored, emphasizing the use of Building Information Modeling (BIM) to manage building data and conduct cost and environmental assessments. Finally, a framework was suggested for selecting optimal materials for pre-construction activities. The study also highlights the importance of considering sustainability and long-term costs in decision-making. In addition, the integration of economic aspects into sustainability assessments was discussed, and challenges and areas for future research were identified. The research methodology included creating a comprehensive database, utilizing life cycle assessment software, and employing optimization techniques to select the most suitable materials for different regions. The results showed significant differences with more than 50% reduction in cost evaluation between generic and optimal materials in the life cycle assessment. In the doors category in North America, greenhouse gas production was reduced by 47%, which was observed between the United States and Australia.\u003c/p\u003e","manuscriptTitle":"A BIM-based approach for multi-objective optimization of sustainable materials selection through life cycle analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-18 20:23:16","doi":"10.21203/rs.3.rs-4062986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f361cd82-9582-4922-b1c1-57e7bfcea620","owner":[],"postedDate":"March 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T07:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-18 20:23:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4062986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4062986","identity":"rs-4062986","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}