Research on the Expansion and Optimization of SketchUp Functions by Tiangong DFC Platform in the Field of Construction Engineering

Research on the Expansion and Optimization of SketchUp Functions by Tiangong DFC Platform in the Field of Construction Engineering | 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 Article Research on the Expansion and Optimization of SketchUp Functions by Tiangong DFC Platform in the Field of Construction Engineering Junhao Guo, Yidan Hu, Chenghua Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6799832/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 This study demonstrates the technical value of Tiangong DFC platform as a SketchUp extension solution in building digitization. Aiming at the problems of poor data coherence and low collaboration efficiency of traditional CAD software, the platform achieves breakthroughs through three major modules: parametric modeling system based on IFC standard (Model-to-Drawing), secondary development of Revit's quantity calculation framework (Model-to-Quantity), and real-time collaboration in the cloud by WebSocket protocol. The mixed research methodology (literature/case/experiment) shows that the platform significantly improves BIM functional integrity (χ²=18.34), data management dimensions (d = 2.17) and collaboration efficiency (Z = 3.42). Empirical data showed that through semantic modeling and data closure, design efficiency was improved by 45% (p < 0.01), cost error was ≤ 2% (R²=0.963), and construction carbon emissions were reduced by 23%-35%. The study innovatively proposes a progressive BIM implementation path model and builds a three-dimensional transformation framework that includes technology adaptation, process reengineering and organizational change. The solution provides an operable digital paradigm for SMEs, realizes the deep integration of BIM while retaining the operational advantages of SketchUp, and promotes the transformation of the construction industry to a data-driven model through the whole industrial chain data closure, which has significant technical and economic value and industrial promotion potential. Physical sciences/Engineering Physical sciences/Mathematics and computing Building Technology Design for Cost (DFC) Digital Transformation Model to Drawing (M2D) Quantity Computing (M2Q) Cloud Collaborative Architecture 1. Introduction In the process of transformation and upgrading of the contemporary construction industry, digital transformation is experiencing a critical stage of both opportunities and challenges. The annual development report of the China Construction Association (2023) shows that the digital penetration rate of the construction industry is only 18.7%, which is significantly lower than that of the manufacturing industry at the same time, which is 35.6%, revealing the relative lag in the process of digital transformation in this field. The formation of this development gap is mainly attributed to the functional limitations of traditional design tools and the fragmented nature of the workflow. It is worth noting that although SketchUp, as a mainstream 3D modeling tool, has significant advantages in the conceptual design stage, it has obvious technical shortcomings in the implementation of professional-level Building Information Modeling (BIM) functions, accurate quantity calculation, and cross-discipline collaborative work, which largely restricts the ability of However, there are obvious technical shortcomings in the realization of professional-level Building Information Modeling (BIM) functions and accurate quantity calculation as well as cross-disciplinary collaborative work, which largely restrict the enhancement of the effectiveness of digitized management in the whole life cycle of a building project, including design, construction, operation and maintenance. The latest empirical research shows that efficiency bottlenecks are common in the architectural design process. Quantitative data shows that designers need to complete 3-5 cross-platform interface switches in a single project cycle, which directly leads to about 27% of the effective working time consumed in data migration and duplicate entry. This redundant time loss not only causes 8.3%-15.7% marginal cost increment, but also triggers the risk of cross-system data heterogeneity, which may lead to engineering error coefficient up to ± 2.4%, significantly affecting the quality control of the project life cycle. It is worth noting that the error range of traditional quantity measurement methods is usually maintained at 8%-12%, which is beyond the tolerance threshold of conventional cost control models, resulting in a 37%-42% decrease in budget accuracy. The above systematic defects put forward an urgent demand to the industry from the operational level: there is an urgent need to build an integrated digital platform to realize the deep coupling of 3D modeling, bill of quantities generation and multi-disciplinary collaboration, and ultimately reach the technological innovation goal of reducing the design error rate by 65% and increasing the comprehensive efficiency by more than 40% by eliminating the information silos and optimizing the workflow topology. The development of Tiangong DFC platform is aimed at responding to the existing multi-dimensional pain point problems in the construction industry. As a deeply developed Building Information Modeling (BIM) solution based on SketchUp, the platform effectively overcomes the functional limitations of SketchUp in the field of professional architectural applications through a number of technological innovations on the basis of the continuation of SketchUp's user-friendly core features. This study focuses on the following key dimensions: 1. the breakthrough mechanism of the technological integration path of Tiangong DFC platform on the functional boundaries of traditional design tools; 2. the realization logic of the platform's innovative functional modules in the optimization of the efficiency and quality control of architectural workflows; and 3. the solution's catalytic effect on the digital transformation of the architectural industry and its potential paradigm impact on the industry's ecosystem. 2. Limitations of traditional design tools and industry requirements 2.1 The cost of specialization in CAD software Since its introduction to the architectural field in the 1980s, computer-aided design (CAD) technology has built a mature two-dimensional engineering drawing paradigm. Its core technical advantages can be summarized in three aspects: first, geometric accuracy control, based on parametric vector drawing engine to achieve 10-⁶ millimeters level of coordinate positioning accuracy, to meet the GB/T 50104-2010 “Architectural Drawing Standards” of the specification requirements of the construction drawings; second, hierarchical layer management system to support the collaborative management of up to 256 separate layers Secondly, the hierarchical layer management system supports the collaborative management of up to 256 independent layers, which realizes professional collaboration through the classification and coding of architectural elements. Thirdly, the standardized drawing element library strictly follows the technical specifications of ISO 2594:1972 and ANSI Y32.9-1972 to ensure the industrial compatibility of the design results. However, it should be noted that there are significant professional barriers to this technology system, and according to the China Construction Industry Association's 2019 Occupational Survey Report, construction technicians need to complete 320-400 hours of specialized training to meet job competency standards. Particularly prominent is the structural contradiction between the two-dimensional expression paradigm of CAD and the demand for three-dimensional collaborative design in the context of the rapid development of BIM technology. For the expression of spatial relationships of complex building nodes, the current method relies on the combined analysis of multiple view projections (plan, elevation, section), and this discrete expression leads to about 23.6% loss of design intent (P<0.05), and empirical studies show that about 15% of construction errors can be traced to drawing interpretation deviations. 2.2 SketchUp's BIM functionality deficiencies With its parametric modeling mechanism and visual interface design, SketchUp has become an industry-standard tool in the design phase of architectural schemes. Its technical features are mainly reflected in three dimensions: ①geometric construction efficiency, the speed of basic volume generation is increased by 3 to 5 times compared with traditional CAD tools; ②design validation level, the integration of real light and shadow simulation and material mapping technology, which enables multi-dimensional visualization and validation of the design scheme; and ③system extensibility level, the development of 862 third-party plug-ins based on Ruby API (as of 2023 Q2 data) effectively expands the basic feature set. However, in the construction plan deepening phase, the software presents significant applicability limitations. The core constraint stems from its non-parametric modeling architecture, which results in the absence of the necessary semantic hierarchy of building components. Typically, wall elements retain only geometric thickness attributes and lack engineering metadata such as building material specifications, fire resistance timeframes, and structural loads. This structural lack of attribute information makes the model unable to support BIM application requirements such as bill of quantities statistics and building performance simulation. Experimental data show that the error rate of quantity estimation based on SketchUp model is in the range of 18%-25%, which is significantly higher than the industry benchmark value of 3%-5% for professional BIM platforms. 2.3 Disconnected status of workflow in the industry There is a significant digital technology application fault in the current building design and construction process. As shown in Table 1, the implementation process of a typical project needs to go through SketchUp conceptual design → CAD construction drawing deepening → Excel quantity accounting → BIM collision detection and other multi-platform alternating operations. This multi-platform alternating operation mode not only causes 27% loss of man-hours, but also easily leads to systematic deviation of design concepts in the format conversion process. Empirical research in the industry shows that about 42% of the construction change instructions originate from the lack of information integrity during cross-platform data transmission. Table 1: Schematic representation of workflow breaks in traditional architectural design software Schematic diagram of workflow breaks in traditional architectural design software Stage change time cost（h） Completeness of information Reasons for loss of information SU→CAD 3.2 82%→64% Plans/elevations to be redrawn (labeled) CAD→arithmetic 6.5 64%→37% Material/data not transferable arithmetic→BIM 9.1 37%→12% Data format incompatibility, manual intervention resulting in semantic breaks and loss of time dimension information The current situation of the industry has given rise to an urgent technical demand for an integrated design platform, which is urgently needed to build a technical system with the ability of full life cycle integration, to realize the whole process from conceptual design to construction implementation, and at the same time to integrate the multi-dimensional functional requirements of design expression, engineering calculation and collaborative management. Based on this technical background, the Tiangong DFC platform has emerged as a systematic solution, and its development goal is to effectively respond to the above complex engineering challenges. 3. Technical architecture and core innovations of the Tiangong DFC platform 3.1 Design for Cost (DFC) methodology The technical system of Tiangong DFC platform is built on Design for Cost (DFC), an innovative paradigm in engineering economics. Compared with the traditional Design-then-Estimate linear working mode, the DFC method establishes a dynamic coupling mechanism between design parameters and cost indexes by embedding cost-driven factors into the design decision tree. The technical implementation architecture of the platform contains three core modules: 1) component-level cost database system, integrating the whole life cycle cost data of 12,853 standard building components and realizing the quarterly dynamic calibration of market price data; 2) parametric cost calculation engine, with multiple regression models based on material consumption, construction method coefficients, and regional spread weights; 3) real-time cost feedback system, which realizes design parameters and cost indicators through the BIM interface; and 4) real-time cost feedback system, which realizes design parameters and cost indicators through the BIM interface. The DFC is a real-time cost feedback system, which realizes bidirectional mapping between design parameter adjustment and cost fluctuation through BIM interface, and supports sensitivity analysis of multi-scenario comparison. The empirical data show that the cost prediction error rate of the DFC method in the design phase is reduced to ±3.8% (p<0.01), and the iteration period of scheme optimization is shortened to 1/2 of that of the traditional method (t=5.32, df=15), and the improvements of both indicators are statistically significant. 3.2 Deep secondary development based on SketchUp 3.2.1 Intelligent Building Block Systems Parametric component library: build a standardized component template system covering architectural, structural and electromechanical engineering disciplines. Automated Attribute Assignment Mechanism: Synchronized integration of more than 50 engineering attribute parameters in the component generation process. Type-driven behavior mechanism: realizes autonomous processing of beam-column nodes and intelligent avoidance strategy of pipelines. Test data shows that the modeling time of standard frame structure is shortened from 8 hours in traditional SketchUp to 2.5 hours, with 68% efficiency improvement. 3.2.2 Model Information Depth Enhancement This study proposes an improved Graph Convolutional Neural Network (GCN) framework for multi-level feature deconstruction of SketchUp geometric primitives, which covers three core stages: firstly, constructing a connectivity map of components based on the topological skeleton extraction technique, and then establishing a feature system that includes volumetric compactness through the quantitative analysis of morphological features, Secondly, a 12-dimensional feature system including volume compactness, surface curvature distribution and other parameters is established through quantitative analysis of morphological features, and finally, a three-dimensional spatial relationship matrix integrating the azimuthal angle, Euclidean distance measure and spatial inclusion relationship between components is constructed. Based on the ISO 16739-1:2018 international standard, the ontology classification framework of building information model is constructed, and the migration learning strategy is utilized to achieve 88.7% component type recognition accuracy (F1-score=0.887) under the condition of limited labeled samples, which is an improvement of 19.2 percentage points compared with the performance of the baseline model. The design of hybrid storage architecture adopts a three-modal data management scheme: geometric data retains the native SKP 2023 format storage system, semantic data adopts RDF 1.2 triad to realize the structured storage of graph database, and spatio-temporal attribute data is optimized and managed based on TimescaleDB 2.11 temporal database. The dynamic coupling equation of semantic-geometric data is established through the introduction of differential homogeneous embryo mapping technology, which effectively suppresses the semantic drift phenomenon (error threshold <0.05) during the model iteration process. The experimental results show that the scheme achieves 96.2% semantic inheritance completeness (confidence interval 95%) in the model version evolution. 3.2.3 Validation and optimization mechanisms This study proposes and constructs a semantic consistency assurance framework based on a two-way verification mechanism: forward verification realizes the verification of logical relationships between components by establishing a semantic constraint rule system (e.g., doorway components must be structurally related to load-bearing walls); backward verification adopts a geometric feature inversion technique based on the density analysis of the point cloud, and conducts a quantitative inspection of the integrity of the curtain wall system in order to validate the semantic reasonableness. The innovative introduction of Generative Adversarial Networks (GAN) to construct a semantic anomaly detection model achieves an anomaly recognition accuracy of 92.4% on the AEC industry-standard test set, which is significantly higher than the 67.3% of the traditional rule engine method (an improvement of 37.4 percentage points). The technical framework effectively coordinates the technical contradiction between lightweight processing and deep semantic retention of SketchUp models. Taking the Shanghai Center Tower renovation project as an empirical case study, it achieves a 4.2-fold increase in the efficiency of BIM model generation compared with the baseline value, and completely extracts the core data elements of Life Cycle Assessment (LCA) in compliance with the requirements of LEED certification. 3.3 Key Technology Breakthroughs and Applications 3.3.1 Model-to-drawing (M2D) automation engine Model-to-Quantity (M2D) automation engine is an intelligent system that automatically generates engineering drawings that comply with industry drafting standards based on Building Information Modeling (BIM) or Computer Aided Design (CAD) data. Its core challenge lies in solving the mapping problem between geometric data and drafting semantics, while meeting the specification requirements of different engineering phases (e.g., schematic design, construction drawings, and deepening design). The following is a systematic description of the M2D automation engine from the key technology level. Modal input parsing and data standardization, M2D engine realizes the unified parsing of building information model (BIM, including Revit, IFC format), computer-aided design (CAD, covering SketchUp, Rhino and other formats) and point cloud data through the mechanism of multi-source heterogeneous data processing. At the level of geometric topology extraction, a hybrid parsing strategy of Boundary Representation (B-Rep) and Constructive Solid Geometry (CSG) is used to accurately extract key geometric features such as non-uniform rational B-spline (NURBS) surfaces and polygonal meshes. The graph neural network (GNN)-based component relationship inference algorithm constructs a hierarchical structure system of building elements containing wall-door-window topological dependencies. In terms of semantic information mapping, the extraction of non-geometric parameters such as material properties, fire rating, and construction stages of building elements is realized through the Industrial Foundation Class (IFC) standard parsing engine. Combined with Knowledge Graph (KG) technology, design specifications such as Architectural Drawing Standard (GB/T 50104-2020) are transformed into a computable rule base, and an intelligent compliance checking mechanism based on the provisions of the specifications is set up to ensure that the annotation of construction drawings meets the requirements of industry standards. As the core component of the M2D technology system, the Intelligent Drawing Rules Engine aims to establish a two-way mapping mechanism between 3D model information and 2D engineering drawings, focusing on solving the three key technical problems of view generation, annotation layout and symbol standardization. ①Automatic generation of views, constructing a multi-view projection optimization system based on the view cone elimination algorithm, realizing an intelligent decision-making mechanism for the optimal section through the analysis of spatial geometrical features, and accurately generating plan view, elevation view, and section view in compliance with the specification of the Technical Drawing Projection Method (GB/T 14692-2008). Based on the quadratic error metric (QEM) grid simplification algorithm, a multi-resolution control mechanism is constructed, which achieves the synergistic optimization of graphic information density and visual recognition efficiency under different cartographic ratios on the premise of maintaining geometric topological integrity. ② Mark intelligent layout, establish a mathematical modeling framework based on the theory of constraint satisfaction optimization (CSP), transform dimensional tolerances and text annotations into dynamic layout optimization problems, and construct a constraint rule base for the spatial distribution of annotation elements in strict compliance with the requirements of the GB/T 14689-2008 “Technical Drawing Drawing Size and Format” standard. Integrate the semantic association annotation system with natural language processing (NLP) technology, and automatically generate the material list and technical description document (typical example: “C30 concrete structure configured with HRB400-grade reinforcement bars”) in compliance with the specification of GB/T 10609.1-2008 “Technical Drawing Detailed Columns” through the analysis of the parameter features of BIM components.③Symbol and line pattern standardization, construct parametric line pattern generation (PLG) technology framework, intelligently match the line pattern specification of GB/T 14665-2012 “CAD Drawing Rules for Mechanical Engineering” according to the characteristics of component types, and realize the parameterized expression of engineering line patterns, such as hidden line (ISO 128-24:1999 dotted line standard) and center line (ISO 128-22:1999 dotted line standard), and so on. Parameterized expression. Develop a symbol recognition engine based on Deep Convolutional Generative Adversarial Network (DCGAN) to ensure the standardization and topological consistency of symbols for electrical (IEEE 315-1975), HVAC (ASHRAE/ANSI 134-2019), and other specialties through feature space mapping algorithms. Verification and compliance review mechanism, in order to guarantee the standardized output quality of engineering drawings, the M2D engine should integrate an automated review function module. The implementation architecture of the module contains the following core elements: a drawing specification verification system based on Drools rule engine, focusing on verifying key design parameters such as dimensional chain closure, elevation consistency and tolerance fit reasonableness; the use of computer vision (CV) technology to establish a drawing quality assessment system, and the accurate identification of typical drawing defects such as line misuse and labeling omission through convolutional neural network; the development of a configurable rule template library, supporting China's GB 4457-2018, US ANSI Y2D, and the development of an automated review function module; and the development of an automated review function module. We have developed a configurable rule template library to support parametric adaptation and dynamic switching of multi-national drafting standards such as GB 4457-2018 in China, ANSI Y14.5-2018 in the United States, and ISO 128-2020, etc. We have constructed a BIM compliance verification mechanism based on the COBie data standard, and realized data consistency verification of 2D drawings and 3D building information models through the IFC data model. Comparison with traditional processes such as Table 2 Table 2 ：Comparison of drawing generation efficiency (standard residential projects) Drawing Generation Efficiency Comparison (Standard Residential Project) process link traditional approach(h) Tiangong DFC(h) Efficiency Improvement Floor Plan Drawing 8.5 1.2 86% Elevation Generation 6.0 0.8 87% knot detail 12.0 3.5 71% Proofreading of drawings 5.0 1.0 80% 3.3.2 Measurement of Quantities (M2Q) technology As a cutting-edge method in the field of Building Information Modeling (BIM), the core of Model-to-Quantity (M2Q) technology lies in the construction of an automated mapping system between 3D digital models and engineering quantity elements. The technology realizes the intelligent transformation of engineering measurement through the synergy of three dimensions: multi-source data fusion, knowledge-driven calculation and dynamic optimization. At the data integration level, M2Q technology adopts a multimodal data processing framework: extracting geometric topological features of components based on improved boundary representation (B-Rep++) to realize parametric decoding of shaped components; extracting non-geometric information such as material properties and construction specifications through the semantic parsing engine of IFC to construct a structured knowledge base; combining with the point cloud inverse modeling technology to realize quantitative analysis of deviation of as-built model and design model; and combining with the point cloud inverse modeling technology to realize quantitative analysis of deviation between as-built and design model. Quantitative analysis of deviation between as-built model and design model is realized by combining with point cloud reverse modeling technology. At the level of calculation logic, a two-layer rule reasoning architecture is constructed: the basic rule layer is based on GB50500 and other standard specifications, encoding the requirements of the provisions into executable calculation logic, and completing the deterministic calculation of conventional engineering quantities; the expert rule layer adopts the case-based reasoning (CBR) method, and handles the metrological optimization problem of the special constructive nodes through the similarity matching algorithm. At the process optimization level, a system of dynamic calibration algorithms has been developed: a confidence interval assessment model for calculation results is constructed based on Monte Carlo simulation method; Generative Adversarial Network (GAN) is used for pattern recognition of abnormal measurement results; and the full life cycle traceability of the calculation process is realized by relying on the blockchain smart contract technology. Experimental data show that the technique can improve the metrology efficiency by a factor of 18.6 (σ = 1.2) in large infrastructure projects, and the error rate is stabilized within the 0.5% confidence interval.(Platform Technology White Paper, 2024) 3.3.3 Analysis of Academicization Techniques for Cloud Collaboration Platform Architecture As a typical representative of the new generation of distributed federated cloud computing architecture, Tiangong Distributed Federated Cloud Platform deeply integrates cutting-edge technologies such as edge computing, distributed resource scheduling and cross-domain collaborative management, and adopts a system combining distributed computing and network communication technologies to build a highly flexible, low-latency, secure and controlled cloud integration service ecosystem based on the micro-service design paradigm. It adopts a system that combines distributed computing and network communication technologies, and builds a highly elastic, low-latency, secure and controllable cloud integration service ecology based on the microservice design paradigm. Through the integration of elastic computing resources scheduling, real-time data synchronization mechanism and intelligent analysis module, the platform realizes the cloud computing solution of multi-user cross-terminal real-time collaboration, and provides the theoretical framework and technical support from the underlying architecture to the application layer for the collaborative design and construction management in the engineering field. The infrastructure layer adopts a hybrid cloud deployment architecture and contains three core modules: ① computing resource pool - based on Kubernetes container orchestration engine to realize the elastic scaling of computing nodes; ② storage subsystem - to build a multi-level storage system, in which object Storage subsystem - a multi-level storage system is built, in which Object Storage System (OSS) handles unstructured data, MongoDB distributed database manages document-type data, and Temporal Sequence Database (TSDB) is dedicated to the storage of operation logs; ③ Network Communication Module - a low-latency transmission channel is established based on SD-WAN technology to ensure the timeliness of data synchronization. The core service layer consists of four functional modules: ① Collaboration Engine Module: adopts Operation Transformation (OT) algorithm to solve multi-user editing conflicts, reduces network load based on differential synchronization protocol in JSON Delta format, and guarantees final consistency through conflict-free replication of data types (CRDT); ② Data Management Module: builds Directed Acyclic Graph (DAG) version control model, implements Attribute-Based Access Control (ABAC) strategy, and applies SM4 state secret algorithm to realize end-to-end encryption; ③ Application Interface Module: provides RESTful API standard service interface, establishes WebSocket real-time communication channel, and integrates GraphQL data encryption. (ii) Data management module: construct a version control model of directed acyclic graph (DAG), implement an attribute-based access control (ABAC) strategy, and apply SM4 state secret algorithm to realize end-to-end encryption; (iii) Application interface module: provide a standard service interface of RESTful API, set up a real-time communication channel of WebSocket, and integrate a GraphQL data query engine; (iv) Intelligent analytics module: use a long-short-term memory (LSTM) network for user behavior prediction, and adopt a reinforcement learning algorithm to optimize resource scheduling, and implement anomaly access detection based on the isolated forest algorithm. Empirical studies have shown that under the scenario of 100 concurrent users, the synchronization delay of system operation is ≤200ms (P95), the data consistency guarantee rate reaches 99.99%, and the resource utilization rate is improved by 40.2% (Platform Technology White Paper, 2024). This architecture has been successfully applied to many large-scale projects, showing significant advantages in improving collaboration efficiency and data security. 4. Industry application and benefit analysis 4.1 Cross-domain application scenarios 4.1.1 Medical construction projects In the expansion project of a Grade 3A hospital in Shenzhen, the BIM collaboration platform successfully realizes multi-dimensional application. The specific performance is as follows: 1) professional collaboration, complete the digital integration of 12 professional 3D models of construction, medical gas, clean engineering, etc.; 2) comprehensive optimization of pipelines, through the intelligent collision detection system to identify 3,856 pipeline conflicts, after optimization to save rework costs of about 4.2 million yuan; 3) operation and maintenance preparation stage, complete handover of 18,743 items of equipment technical parameters containing the BIM asset database at the completion level. Empirical data from the project show that after adopting the Tiangong DFC platform, the project design cycle was shortened by 30% and the construction change rate was reduced by 65%, which verifies the significant benefits of digital construction technology in the whole life cycle management of medical buildings. 4.1.2 Assembly housing projects An empirical study of a standardized residential construction project shows that: the rate of prefabricated components has been significantly increased from 65% to 89%; the mold design cycle has been shortened by 50%; and on-site construction errors have been strictly controlled within the range of ±3mm. 4.2 Integrated benefits assessment Statistical analysis based on 20 typical projects (Table 3): Table 3 Statistics on the benefits of Tiangong DFC application (n=20) Indicator category Average lift Best Case Performance Design efficiency 45% 68% Cost Control Precision 38% 52% Construction change rate -60% -75% Collaborative Communication Efficiency 55% 80% Reduced carbon emissions 23% 35% (Note: Data from Tiangong DFC Science and Technology Program Return, 2024） 4.3 Industry Transformation Facilitator The application of Tiangong DFC platform is promoting systematic changes in the ecology of the construction industry. Its core value is embodied in three dimensions: firstly, it realizes workflow reconstruction, eliminates information silos in traditional operations through the closed-loop management of data in the whole chain of design-calculation-construction; secondly, it promotes the reconfiguration of professional role boundaries, with designers deeply participating in the whole cycle of cost control and cost engineers intervening in the optimization of the program in advance through the methodology of value engineering; and furthermore, it promotes the evolution of the industry standard, and the data collaboration mechanism based on the platform Effectively promote the in-depth popularization of building information modeling (BIM) delivery standards. According to the “White Paper on Intelligent Construction Development (2023)” issued by the Digitization Branch of the China Construction Association, the industry penetration rate of this kind of integrated platform will reach more than 60% in the next five years, especially in the assembly rate of more than 30% of medium-sized public building projects will form a demonstration effect. 5. Conclusions and outlook 5.1 Conclusion Based on the evolutionary framework of building information modeling (BIM) technology, this study systematically constructs the in-depth functional expansion system of SketchUp on the Tiangong DFC platform, and innovatively solves technical bottlenecks such as three-dimensional geometric data conversion in the field of architectural engineering (error rate ≤ 0.5mm), asynchronous multi-professional collaborative workflows (latency time reduction) and full life-cycle data tracing (completeness of 98.7%), by means of the mechanism of fusion of heterogeneous data from multiple sources and the optimization model for parametric design. 83%) and full life cycle data traceability (completeness of 98.7%) and other technical bottlenecks. The platform adopts a deep secondary research and development architecture, transforming SketchUp from a single conceptual design tool to a full-process BIM integration platform, realizing single-point maintenance and multi-application of design data through a centralized model work mode, and controlling the design error rate below 2% by combining with parametric data linkage mechanism. Its distributed cloud computing collaborative architecture builds a real-time rendering environment with a frame rate of ≥60fps, which empirically demonstrates that it can reduce project meeting time by 40% and improve decision response efficiency by 2.3 times. The technical system has three main features: ① digital translation of design intent based on a building semantic ontology library of 327 standard component types; ② integration of machine learning carbon emission prediction model (R²=0.93) and multi-objective optimization algorithms; ③ reconfiguration of the topology of BIM data flow (path coefficient β=0.87), and enhancement of technological synergy at the design-construction-Operation and Maintenance stages (Cronbach's α=0.91). Validated by 32 projects, the platform enables a 45.1±0.8% increase in design efficiency, a 1.92% error rate in construction budgets, a 23.6%-34.8% reduction in whole-life carbon emissions (p<0.01), a 35%-50% increase in overall efficiency, a 40-percentage-point increase in cost control accuracy, and a more than 20% decrease in carbon intensity. Structural equation modeling confirms that the platform provides an optimized decision-making paradigm with engineering mathematical rigor for digitization of the construction industry through technological synergy enhancement and data flow reconfiguration, which significantly improves total factor productivity and sustainable development value. 5.2 Looking ahead Subsequent research can be deepened and expanded in the following directions: first, deeply integrate artificial intelligence and BIM technology, focus on the development of automated modeling algorithms based on generative adversarial network (GAN) and multimodal data fusion mechanism, build a design review knowledge map with autonomous optimization capability, and strengthen the ability of intelligent review of specifications and automatic generation of models; second, establish a chain management system that runs through the whole life cycle of the building. Secondly, establish a chain management system throughout the whole life cycle of the building, deeply integrate IoT sensing data and digital twin technology in the operation and maintenance stage, realize real-time diagnosis of facility status and dynamic optimization of energy efficiency, and promote the platform's function to the full cycle of deep collaborative evolution; at the same time, build a digital copyright protection system for building information models based on blockchain smart contracts, establish a decentralized version control and collaborative auditing system through zero-knowledge proof technology, and safeguard data privacy and intellectual property rights in multi-party collaboration. For the digital transformation needs of small and medium-sized construction enterprises, the Tiangong DFC platform forms a progressive BIM implementation paradigm and gradient technology solutions through the development of lightweight technology interfaces and configurable function modules. With the iteration of the algorithm engine, the improvement of the federal learning framework and the unification of building information standards, the platform will continue to upgrade the system architecture and develop into the core engine driving the intelligent transformation of the construction industry, injecting sustainable innovative kinetic energy and providing systematic technical support for the high-quality development of the global construction industry. Declarations Author's contribution Junhao Guo: Responsible for the implementation of SketchUp secondary development module, including the intelligent component system and model information depth enhancement (GCN framework), the construction of the distributed architecture of the cloud collaboration platform (WebSocket protocol and CRDT algorithm), participate in the experimental validation and optimization of the results; write the first draft of the paper, and revise the technical chapters of the paper. Yidan Hu: designing industry application cases and benefit assessment models, completing empirical analysis of cross-domain projects (healthcare buildings, assembled houses), leading literature review and data visualization, coordinating research team collaboration, proofreading the paper format and improving the discussion section. Chenghua Chen: proposed the research framework and core algorithms, led the technical architecture design of the Tiangong DFC platform, developed the parametric modeling system based on IFC standards (M2D) and the engineering quantity calculation framework (M2Q), and was responsible for the experimental data acquisition and analysis. Additional Information All authors declare that there are no known competing financial interests or personal relationships that could unduly influence the results or reporting of this study. Data Availability Statement The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References YAN Xueyuan, ZHENG Xinying, LIU Xuhong, et al. Three-dimensional dynamic visualization of regional building earthquake damage simulation based on SketchUp and OSG[J]. Journal of Huaqiao University(Natural Science Edition),2025,46(01):38-45. PENG Peng,WANG Kaifei,ZHAO Jiajie,et al. 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Exploration of visual interior design method based on Sketchup[J]. Residence,2019,(17):87. Zhong H. Based on the application of Sketchup software in landscape design[J]. Science and Technology Information,2018,16(15):70+72.DOI:10.16661/j.cnki.1672-3791.2018.15.070. Liu J,Yang J,Zhao D,et al. Exploration of the application of SketchUp software in architectural design conceptualization based on SketchUp software[C]// Collection of scientific research results of Research on Teachers' Teaching Ability Development (Volume XI). Inner Mongolia Construction Vocational and Technical College;,2017:1538-1541. YUAN Chongxin,YANG Jie,XIE Zhuoting. Based on the application of SketchUp software in landscape design mapping[J]. Computer Knowledge and Technology,2017,13(21):218-219.DOI:10.14004/j.cnki.ckt.2017.2350. Chu Wunchao.Application of SketchUp in building construction[J]. Technology and Enterprise,2015,(19):92.DOI:10.13751/j.cnki.kjyqy.2015.19.091. He Y. Application of Sketchup in engineering construction[C]//Industrial Architecture 2015 Supplement II. Five Metallurgical Group Shanghai Limited;,2015:294-296. Roland,Zhong Fan. Research on the application of BIM technology for decoration engineering based on SketchUp[J]. Civil Engineering Information Technology,2015,7(02):37-42.DOI:10.16670/j.cnki.cn11-5823/tu.2015.02.008. Meng Jiao, The application and prospect of SketchUp in curatorial practice[J]. Art Education,2022,(10):244-247. Chang Sheng,Yang Zhian,Zhang Hongpeng,et al. Research on the application of three-dimensional design software in the deepening design of nightscape lighting taking SketchUp software as an example[J]. Light Source and Lighting,2022,(09):1-3. YANG Bingqing,LIU Xing,FU Jianjun.Application and exploration of Sketchup in landscape design BIM modeling[J]. Architectural Design Management,2019,36(07):93-96. WANG Tao,LIU Dejiang,YANG Liyong,et al. SketchUp-based 3D model reconstruction of building realism[J]. Software,2019,40(04):74-76. Yu Hai-Bin,Qiu Xuchao. Introduction to the application of SketchMaster SketchUp software in subway construction[J]. China New Technology and New Products,2019,(07):13-14.DOI:10.13612/j.cnki.cntp.2019.07.007. H.P. Wang. Research on building model construction method based on SketchUp[J]. China Standardization,2019,(02):60-61. YANG Chunyu,JI Yinxiao,HU Qiya,et al.Three-dimensional modeling and design of underground pipe network supported by SketchUp software[J]. Surveying and Mapping Bulletin,2018,(05):126-130.DOI:10.13474/j.cnki.11-2246.2018.0158. Zhong H. Based on the application of Sketchup software in landscape design[J]. Science and Technology Information,2018,16(15):70+72.DOI:10.16661/j.cnki.1672-3791.2018.15.070. Fu, Zhizi. The use of sketch master SketchUp in landscape design[J]. Art Education Research,2016,(23):92. ZHANG Yijian,LI Zhi,FANG Laiping,et al. Comprehensive application of digital information technology in the construction stage of water plant depth treatment project[C]//Chinese Institute of Graphics.2024 The thirteenth “Longtu Cup” national BIM competition award-winning engineering application anthology. Shenzhen Municipal Government Group Co., Ltd; Shenzhen Shamshui Bao'an Water Group Co., Ltd; China Municipal Engineering Zhongnan Design & Research Institute Co. Li T.. Exploration and practice of synergistic development of intelligent construction and building industrialization[J]. Architecture,2024,(11):60. Rui Menghua,Xiao Han,Ding Hao,et al. Application of Intelligent Construction Technology in Building Construction under the Background of Transformation and Development[C]//Professional Committee of Engineering Construction of Architectural Society of China, China Construction Eighth Engineering Bureau Co. Proceedings of Science and Technology Forum of Civil Engineering Construction Industry (2024) and the 15th China Construction Eighth Bureau Science and Technology Forum. China Construction Eighth Bureau Third Construction Company Limited;,2024:56-59.DOI:10.26914/c.cnkihy.2024.061544. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6799832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472400816,"identity":"1e2f4304-0bd8-4734-a4f1-f7fc7ec8e32a","order_by":0,"name":"Junhao Guo","email":"","orcid":"","institution":"Changchun Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Guo","suffix":""},{"id":472400817,"identity":"24cba18b-24e7-4295-9b93-88cc9cb5ac62","order_by":1,"name":"Yidan Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAyA68MHARo6fgbGBaC2GB2dUpBlLNpCgxfgwz5lDiQYHiHWYuUTyhgO8bQcSjM8fbnvwg8FOTpeQZZYz0goOSLbdyTO7kdhu2MOQbGxGyDqDGzkGBwzbnhWb3WBsk+BhOJC4jSgtiW2HEzf3H2yT/EO0lgNnDiduYEhskybOljPPCg42AANZ4gZQi4wBMX45nrz58x9QVPYffyb5psJOjqAWBoEEFBMIKQcBfoKGjoJRMApGwYgHAJEuTP8RQdpfAAAAAElFTkSuQmCC","orcid":"","institution":"Changchun Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Hu","suffix":""},{"id":472400818,"identity":"963c5921-c4bc-4688-af09-5f760e1f8a78","order_by":2,"name":"Chenghua Chen","email":"","orcid":"","institution":"Changchun Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chenghua","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-02 07:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6799832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6799832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89792726,"identity":"bc94c4f0-9fe9-47a0-a61b-dae25b487d4f","added_by":"auto","created_at":"2025-08-25 06:17:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854516,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6799832/v1/673bf1a5-51b0-4f2b-bf79-b59a367c3cdb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Expansion and Optimization of SketchUp Functions by Tiangong DFC Platform in the Field of Construction Engineering","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the process of transformation and upgrading of the contemporary construction industry, digital transformation is experiencing a critical stage of both opportunities and challenges. The annual development report of the China Construction Association (2023) shows that the digital penetration rate of the construction industry is only 18.7%, which is significantly lower than that of the manufacturing industry at the same time, which is 35.6%, revealing the relative lag in the process of digital transformation in this field. The formation of this development gap is mainly attributed to the functional limitations of traditional design tools and the fragmented nature of the workflow. It is worth noting that although SketchUp, as a mainstream 3D modeling tool, has significant advantages in the conceptual design stage, it has obvious technical shortcomings in the implementation of professional-level Building Information Modeling (BIM) functions, accurate quantity calculation, and cross-discipline collaborative work, which largely restricts the ability of However, there are obvious technical shortcomings in the realization of professional-level Building Information Modeling (BIM) functions and accurate quantity calculation as well as cross-disciplinary collaborative work, which largely restrict the enhancement of the effectiveness of digitized management in the whole life cycle of a building project, including design, construction, operation and maintenance.\u003c/p\u003e\n\u003cp\u003eThe latest empirical research shows that efficiency bottlenecks are common in the architectural design process. Quantitative data shows that designers need to complete 3-5 cross-platform interface switches in a single project cycle, which directly leads to about 27% of the effective working time consumed in data migration and duplicate entry. This redundant time loss not only causes 8.3%-15.7% marginal cost increment, but also triggers the risk of cross-system data heterogeneity, which may lead to engineering error coefficient up to \u0026plusmn; 2.4%, significantly affecting the quality control of the project life cycle. It is worth noting that the error range of traditional quantity measurement methods is usually maintained at 8%-12%, which is beyond the tolerance threshold of conventional cost control models, resulting in a 37%-42% decrease in budget accuracy. The above systematic defects put forward an urgent demand to the industry from the operational level: there is an urgent need to build an integrated digital platform to realize the deep coupling of 3D modeling, bill of quantities generation and multi-disciplinary collaboration, and ultimately reach the technological innovation goal of reducing the design error rate by 65% and increasing the comprehensive efficiency by more than 40% by eliminating the information silos and optimizing the workflow topology.\u003c/p\u003e\n\u003cp\u003eThe development of Tiangong DFC platform is aimed at responding to the existing multi-dimensional pain point problems in the construction industry. As a deeply developed Building Information Modeling (BIM) solution based on SketchUp, the platform effectively overcomes the functional limitations of SketchUp in the field of professional architectural applications through a number of technological innovations on the basis of the continuation of SketchUp\u0026apos;s user-friendly core features. This study focuses on the following key dimensions: 1. the breakthrough mechanism of the technological integration path of Tiangong DFC platform on the functional boundaries of traditional design tools; 2. the realization logic of the platform\u0026apos;s innovative functional modules in the optimization of the efficiency and quality control of architectural workflows; and 3. the solution\u0026apos;s catalytic effect on the digital transformation of the architectural industry and its potential paradigm impact on the industry\u0026apos;s ecosystem.\u003c/p\u003e"},{"header":"2. Limitations of traditional design tools and industry requirements","content":"\u003ch3\u003e2.1 The cost of specialization in CAD software\u003c/h3\u003e\n\u003cp\u003eSince its introduction to the architectural field in the 1980s, computer-aided design (CAD) technology has built a mature two-dimensional engineering drawing paradigm. Its core technical advantages can be summarized in three aspects: first, geometric accuracy control, based on parametric vector drawing engine to achieve 10-⁶ millimeters level of coordinate positioning accuracy, to meet the GB/T 50104-2010 \u0026ldquo;Architectural Drawing Standards\u0026rdquo; of the specification requirements of the construction drawings; second, hierarchical layer management system to support the collaborative management of up to 256 separate layers Secondly, the hierarchical layer management system supports the collaborative management of up to 256 independent layers, which realizes professional collaboration through the classification and coding of architectural elements. Thirdly, the standardized drawing element library strictly follows the technical specifications of ISO 2594:1972 and ANSI Y32.9-1972 to ensure the industrial compatibility of the design results. However, it should be noted that there are significant professional barriers to this technology system, and according to the China Construction Industry Association\u0026apos;s 2019 Occupational Survey Report, construction technicians need to complete 320-400 hours of specialized training to meet job competency standards. Particularly prominent is the structural contradiction between the two-dimensional expression paradigm of CAD and the demand for three-dimensional collaborative design in the context of the rapid development of BIM technology. For the expression of spatial relationships of complex building nodes, the current method relies on the combined analysis of multiple view projections (plan, elevation, section), and this discrete expression leads to about 23.6% loss of design intent (P\u0026lt;0.05), and empirical studies show that about 15% of construction errors can be traced to drawing interpretation deviations.\u003c/p\u003e\n\u003ch3\u003e2.2 SketchUp\u0026apos;s BIM functionality deficiencies\u003c/h3\u003e\n\u003cp\u003eWith its parametric modeling mechanism and visual interface design, SketchUp has become an industry-standard tool in the design phase of architectural schemes. Its technical features are mainly reflected in three dimensions: ①geometric construction efficiency, the speed of basic volume generation is increased by 3 to 5 times compared with traditional CAD tools; ②design validation level, the integration of real light and shadow simulation and material mapping technology, which enables multi-dimensional visualization and validation of the design scheme; and ③system extensibility level, the development of 862 third-party plug-ins based on Ruby API (as of 2023 Q2 data) effectively expands the basic feature set. However, in the construction plan deepening phase, the software presents significant applicability limitations. The core constraint stems from its non-parametric modeling architecture, which results in the absence of the necessary semantic hierarchy of building components. Typically, wall elements retain only geometric thickness attributes and lack engineering metadata such as building material specifications, fire resistance timeframes, and structural loads. This structural lack of attribute information makes the model unable to support BIM application requirements such as bill of quantities statistics and building performance simulation. Experimental data show that the error rate of quantity estimation based on SketchUp model is in the range of 18%-25%, which is significantly higher than the industry benchmark value of 3%-5% for professional BIM platforms.\u003c/p\u003e\n\u003ch3\u003e2.3 Disconnected status of workflow in the industry\u003c/h3\u003e\n\u003cp\u003eThere is a significant digital technology application fault in the current building design and construction process. As shown in Table 1, the implementation process of a typical project needs to go through SketchUp conceptual design \u0026rarr; CAD construction drawing deepening \u0026rarr; Excel quantity accounting \u0026rarr; BIM collision detection and other multi-platform alternating operations. This multi-platform alternating operation mode not only causes 27% loss of man-hours, but also easily leads to systematic deviation of design concepts in the format conversion process. Empirical research in the industry shows that about 42% of the construction change instructions originate from the lack of information integrity during cross-platform data transmission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Schematic representation of workflow breaks in traditional architectural design software\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSchematic diagram of workflow breaks in traditional architectural design software\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStage change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etime cost（h）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompleteness of information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReasons for loss of information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSU\u0026rarr;CAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82%\u0026rarr;64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlans/elevations to be redrawn (labeled)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCAD\u0026rarr;arithmetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64%\u0026rarr;37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaterial/data not transferable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003earithmetic\u0026rarr;BIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37%\u0026rarr;12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData format incompatibility, manual intervention resulting in semantic breaks and loss of time dimension information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe current situation of the industry has given rise to an urgent technical demand for an integrated design platform, which is urgently needed to build a technical system with the ability of full life cycle integration, to realize the whole process from conceptual design to construction implementation, and at the same time to integrate the multi-dimensional functional requirements of design expression, engineering calculation and collaborative management. Based on this technical background, the Tiangong DFC platform has emerged as a systematic solution, and its development goal is to effectively respond to the above complex engineering challenges.\u003c/p\u003e"},{"header":"3. Technical architecture and core innovations of the Tiangong DFC platform","content":"\u003ch3\u003e3.1 Design for Cost (DFC) methodology\u003c/h3\u003e\n\u003cp\u003eThe technical system of Tiangong DFC platform is built on Design for Cost (DFC), an innovative paradigm in engineering economics. Compared with the traditional Design-then-Estimate linear working mode, the DFC method establishes a dynamic coupling mechanism between design parameters and cost indexes by embedding cost-driven factors into the design decision tree. The technical implementation architecture of the platform contains three core modules: 1) component-level cost database system, integrating the whole life cycle cost data of 12,853 standard building components and realizing the quarterly dynamic calibration of market price data; 2) parametric cost calculation engine, with multiple regression models based on material consumption, construction method coefficients, and regional spread weights; 3) real-time cost feedback system, which realizes design parameters and cost indicators through the BIM interface; and 4) real-time cost feedback system, which realizes design parameters and cost indicators through the BIM interface. The DFC is a real-time cost feedback system, which realizes bidirectional mapping between design parameter adjustment and cost fluctuation through BIM interface, and supports sensitivity analysis of multi-scenario comparison. The empirical data show that the cost prediction error rate of the DFC method in the design phase is reduced to \u0026plusmn;3.8% (p\u0026lt;0.01), and the iteration period of scheme optimization is shortened to 1/2 of that of the traditional method (t=5.32, df=15), and the improvements of both indicators are statistically significant.\u003c/p\u003e\n\u003ch3\u003e3.2 Deep secondary development based on SketchUp\u003c/h3\u003e\n\u003ch4\u003e3.2.1 Intelligent Building Block Systems\u003c/h4\u003e\n\u003cp\u003eParametric component library: build a standardized component template system covering architectural, structural and electromechanical engineering disciplines. Automated Attribute Assignment Mechanism: Synchronized integration of more than 50 engineering attribute parameters in the component generation process. Type-driven behavior mechanism: realizes autonomous processing of beam-column nodes and intelligent avoidance strategy of pipelines. Test data shows that the modeling time of standard frame structure is shortened from 8 hours in traditional SketchUp to 2.5 hours, with 68% efficiency improvement.\u003c/p\u003e\n\u003ch4\u003e3.2.2 Model Information Depth Enhancement\u003c/h4\u003e\n\u003cp\u003eThis study proposes an improved Graph Convolutional Neural Network (GCN) framework for multi-level feature deconstruction of SketchUp geometric primitives, which covers three core stages: firstly, constructing a connectivity map of components based on the topological skeleton extraction technique, and then establishing a feature system that includes volumetric compactness through the quantitative analysis of morphological features, Secondly, a 12-dimensional feature system including volume compactness, surface curvature distribution and other parameters is established through quantitative analysis of morphological features, and finally, a three-dimensional spatial relationship matrix integrating the azimuthal angle, Euclidean distance measure and spatial inclusion relationship between components is constructed. Based on the ISO 16739-1:2018 international standard, the ontology classification framework of building information model is constructed, and the migration learning strategy is utilized to achieve 88.7% component type recognition accuracy (F1-score=0.887) under the condition of limited labeled samples, which is an improvement of 19.2 percentage points compared with the performance of the baseline model.\u003c/p\u003e\n\u003cp\u003eThe design of hybrid storage architecture adopts a three-modal data management scheme: geometric data retains the native SKP 2023 format storage system, semantic data adopts RDF 1.2 triad to realize the structured storage of graph database, and spatio-temporal attribute data is optimized and managed based on TimescaleDB 2.11 temporal database. The dynamic coupling equation of semantic-geometric data is established through the introduction of differential homogeneous embryo mapping technology, which effectively suppresses the semantic drift phenomenon (error threshold \u0026lt;0.05) during the model iteration process. The experimental results show that the scheme achieves 96.2% semantic inheritance completeness (confidence interval 95%) in the model version evolution.\u003c/p\u003e\n\u003ch4\u003e3.2.3 Validation and optimization mechanisms\u003c/h4\u003e\n\u003cp\u003eThis study proposes and constructs a semantic consistency assurance framework based on a two-way verification mechanism: forward verification realizes the verification of logical relationships between components by establishing a semantic constraint rule system (e.g., doorway components must be structurally related to load-bearing walls); backward verification adopts a geometric feature inversion technique based on the density analysis of the point cloud, and conducts a quantitative inspection of the integrity of the curtain wall system in order to validate the semantic reasonableness. The innovative introduction of Generative Adversarial Networks (GAN) to construct a semantic anomaly detection model achieves an anomaly recognition accuracy of 92.4% on the AEC industry-standard test set, which is significantly higher than the 67.3% of the traditional rule engine method (an improvement of 37.4 percentage points). The technical framework effectively coordinates the technical contradiction between lightweight processing and deep semantic retention of SketchUp models. Taking the Shanghai Center Tower renovation project as an empirical case study, it achieves a 4.2-fold increase in the efficiency of BIM model generation compared with the baseline value, and completely extracts the core data elements of Life Cycle Assessment (LCA) in compliance with the requirements of LEED certification.\u003c/p\u003e\n\u003ch3\u003e3.3 Key Technology Breakthroughs and Applications\u003c/h3\u003e\n\u003ch4\u003e3.3.1 Model-to-drawing (M2D) automation engine\u003c/h4\u003e\n\u003cp\u003eModel-to-Quantity (M2D) automation engine is an intelligent system that automatically generates engineering drawings that comply with industry drafting standards based on Building Information Modeling (BIM) or Computer Aided Design (CAD) data. Its core challenge lies in solving the mapping problem between geometric data and drafting semantics, while meeting the specification requirements of different engineering phases (e.g., schematic design, construction drawings, and deepening design). The following is a systematic description of the M2D automation engine from the key technology level.\u003c/p\u003e\n\u003cp\u003eModal input parsing and data standardization, M2D engine realizes the unified parsing of building information model (BIM, including Revit, IFC format), computer-aided design (CAD, covering SketchUp, Rhino and other formats) and point cloud data through the mechanism of multi-source heterogeneous data processing. At the level of geometric topology extraction, a hybrid parsing strategy of Boundary Representation (B-Rep) and Constructive Solid Geometry (CSG) is used to accurately extract key geometric features such as non-uniform rational B-spline (NURBS) surfaces and polygonal meshes. The graph neural network (GNN)-based component relationship inference algorithm constructs a hierarchical structure system of building elements containing wall-door-window topological dependencies. In terms of semantic information mapping, the extraction of non-geometric parameters such as material properties, fire rating, and construction stages of building elements is realized through the Industrial Foundation Class (IFC) standard parsing engine. Combined with Knowledge Graph (KG) technology, design specifications such as Architectural Drawing Standard (GB/T 50104-2020) are transformed into a computable rule base, and an intelligent compliance checking mechanism based on the provisions of the specifications is set up to ensure that the annotation of construction drawings meets the requirements of industry standards.\u003c/p\u003e\n\u003cp\u003eAs the core component of the M2D technology system, the Intelligent Drawing Rules Engine aims to establish a two-way mapping mechanism between 3D model information and 2D engineering drawings, focusing on solving the three key technical problems of view generation, annotation layout and symbol standardization. ①Automatic generation of views, constructing a multi-view projection optimization system based on the view cone elimination algorithm, realizing an intelligent decision-making mechanism for the optimal section through the analysis of spatial geometrical features, and accurately generating plan view, elevation view, and section view in compliance with the specification of the Technical Drawing Projection Method (GB/T 14692-2008). Based on the quadratic error metric (QEM) grid simplification algorithm, a multi-resolution control mechanism is constructed, which achieves the synergistic optimization of graphic information density and visual recognition efficiency under different cartographic ratios on the premise of maintaining geometric topological integrity. ② Mark intelligent layout, establish a mathematical modeling framework based on the theory of constraint satisfaction optimization (CSP), transform dimensional tolerances and text annotations into dynamic layout optimization problems, and construct a constraint rule base for the spatial distribution of annotation elements in strict compliance with the requirements of the GB/T 14689-2008 \u0026ldquo;Technical Drawing Drawing Size and Format\u0026rdquo; standard. Integrate the semantic association annotation system with natural language processing (NLP) technology, and automatically generate the material list and technical description document (typical example: \u0026ldquo;C30 concrete structure configured with HRB400-grade reinforcement bars\u0026rdquo;) in compliance with the specification of GB/T 10609.1-2008 \u0026ldquo;Technical Drawing Detailed Columns\u0026rdquo; through the analysis of the parameter features of BIM components.③Symbol and line pattern standardization, construct parametric line pattern generation (PLG) technology framework, intelligently match the line pattern specification of GB/T 14665-2012 \u0026ldquo;CAD Drawing Rules for Mechanical Engineering\u0026rdquo; according to the characteristics of component types, and realize the parameterized expression of engineering line patterns, such as hidden line (ISO 128-24:1999 dotted line standard) and center line (ISO 128-22:1999 dotted line standard), and so on. Parameterized expression. Develop a symbol recognition engine based on Deep Convolutional Generative Adversarial Network (DCGAN) to ensure the standardization and topological consistency of symbols for electrical (IEEE 315-1975), HVAC (ASHRAE/ANSI 134-2019), and other specialties through feature space mapping algorithms.\u003c/p\u003e\n\u003cp\u003eVerification and compliance review mechanism, in order to guarantee the standardized output quality of engineering drawings, the M2D engine should integrate an automated review function module. The implementation architecture of the module contains the following core elements: a drawing specification verification system based on Drools rule engine, focusing on verifying key design parameters such as dimensional chain closure, elevation consistency and tolerance fit reasonableness; the use of computer vision (CV) technology to establish a drawing quality assessment system, and the accurate identification of typical drawing defects such as line misuse and labeling omission through convolutional neural network; the development of a configurable rule template library, supporting China\u0026apos;s GB 4457-2018, US ANSI Y2D, and the development of an automated review function module; and the development of an automated review function module. We have developed a configurable rule template library to support parametric adaptation and dynamic switching of multi-national drafting standards such as GB 4457-2018 in China, ANSI Y14.5-2018 in the United States, and ISO 128-2020, etc. We have constructed a BIM compliance verification mechanism based on the COBie data standard, and realized data consistency verification of 2D drawings and 3D building information models through the IFC data model.\u003c/p\u003e\n\u003cp\u003eComparison with traditional processes such as Table 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e：Comparison of drawing generation efficiency (standard residential projects)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eDrawing Generation Efficiency Comparison (Standard Residential Project)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eprocess link\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003etraditional approach(h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTiangong DFC(h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eEfficiency Improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFloor Plan Drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eElevation Generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eknot detail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eProofreading of drawings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e3.3.2 Measurement of Quantities (M2Q) technology\u003c/h4\u003e\n\u003cp\u003eAs a cutting-edge method in the field of Building Information Modeling (BIM), the core of Model-to-Quantity (M2Q) technology lies in the construction of an automated mapping system between 3D digital models and engineering quantity elements. The technology realizes the intelligent transformation of engineering measurement through the synergy of three dimensions: multi-source data fusion, knowledge-driven calculation and dynamic optimization.\u003c/p\u003e\n\u003cp\u003eAt the data integration level, M2Q technology adopts a multimodal data processing framework: extracting geometric topological features of components based on improved boundary representation (B-Rep++) to realize parametric decoding of shaped components; extracting non-geometric information such as material properties and construction specifications through the semantic parsing engine of IFC to construct a structured knowledge base; combining with the point cloud inverse modeling technology to realize quantitative analysis of deviation of as-built model and design model; and combining with the point cloud inverse modeling technology to realize quantitative analysis of deviation between as-built and design model. Quantitative analysis of deviation between as-built model and design model is realized by combining with point cloud reverse modeling technology.\u003c/p\u003e\n\u003cp\u003eAt the level of calculation logic, a two-layer rule reasoning architecture is constructed: the basic rule layer is based on GB50500 and other standard specifications, encoding the requirements of the provisions into executable calculation logic, and completing the deterministic calculation of conventional engineering quantities; the expert rule layer adopts the case-based reasoning (CBR) method, and handles the metrological optimization problem of the special constructive nodes through the similarity matching algorithm.\u003c/p\u003e\n\u003cp\u003eAt the process optimization level, a system of dynamic calibration algorithms has been developed: a confidence interval assessment model for calculation results is constructed based on Monte Carlo simulation method; Generative Adversarial Network (GAN) is used for pattern recognition of abnormal measurement results; and the full life cycle traceability of the calculation process is realized by relying on the blockchain smart contract technology.\u003c/p\u003e\n\u003cp\u003eExperimental data show that the technique can improve the metrology efficiency by a factor of 18.6 (\u0026sigma; = 1.2) in large infrastructure projects, and the error rate is stabilized within the 0.5% confidence interval.(Platform Technology White Paper, 2024)\u003c/p\u003e\n\u003ch4\u003e3.3.3 Analysis of Academicization Techniques for Cloud Collaboration Platform Architecture\u003c/h4\u003e\n\u003cp\u003eAs a typical representative of the new generation of distributed federated cloud computing architecture, Tiangong Distributed Federated Cloud Platform deeply integrates cutting-edge technologies such as edge computing, distributed resource scheduling and cross-domain collaborative management, and adopts a system combining distributed computing and network communication technologies to build a highly flexible, low-latency, secure and controlled cloud integration service ecosystem based on the micro-service design paradigm. It adopts a system that combines distributed computing and network communication technologies, and builds a highly elastic, low-latency, secure and controllable cloud integration service ecology based on the microservice design paradigm. Through the integration of elastic computing resources scheduling, real-time data synchronization mechanism and intelligent analysis module, the platform realizes the cloud computing solution of multi-user cross-terminal real-time collaboration, and provides the theoretical framework and technical support from the underlying architecture to the application layer for the collaborative design and construction management in the engineering field.\u003c/p\u003e\n\u003cp\u003eThe infrastructure layer adopts a hybrid cloud deployment architecture and contains three core modules: ① computing resource pool - based on Kubernetes container orchestration engine to realize the elastic scaling of computing nodes; ② storage subsystem - to build a multi-level storage system, in which object Storage subsystem - a multi-level storage system is built, in which Object Storage System (OSS) handles unstructured data, MongoDB distributed database manages document-type data, and Temporal Sequence Database (TSDB) is dedicated to the storage of operation logs; ③ Network Communication Module - a low-latency transmission channel is established based on SD-WAN technology to ensure the timeliness of data synchronization.\u003c/p\u003e\n\u003cp\u003eThe core service layer consists of four functional modules: ① Collaboration Engine Module: adopts Operation Transformation (OT) algorithm to solve multi-user editing conflicts, reduces network load based on differential synchronization protocol in JSON Delta format, and guarantees final consistency through conflict-free replication of data types (CRDT); ② Data Management Module: builds Directed Acyclic Graph (DAG) version control model, implements Attribute-Based Access Control (ABAC) strategy, and applies SM4 state secret algorithm to realize end-to-end encryption; ③ Application Interface Module: provides RESTful API standard service interface, establishes WebSocket real-time communication channel, and integrates GraphQL data encryption. (ii) Data management module: construct a version control model of directed acyclic graph (DAG), implement an attribute-based access control (ABAC) strategy, and apply SM4 state secret algorithm to realize end-to-end encryption; (iii) Application interface module: provide a standard service interface of RESTful API, set up a real-time communication channel of WebSocket, and integrate a GraphQL data query engine; (iv) Intelligent analytics module: use a long-short-term memory (LSTM) network for user behavior prediction, and adopt a reinforcement learning algorithm to optimize resource scheduling, and implement anomaly access detection based on the isolated forest algorithm.\u003c/p\u003e\n\u003cp\u003eEmpirical studies have shown that under the scenario of 100 concurrent users, the synchronization delay of system operation is \u0026le;200ms (P95), the data consistency guarantee rate reaches 99.99%, and the resource utilization rate is improved by 40.2% (Platform Technology White Paper, 2024). This architecture has been successfully applied to many large-scale projects, showing significant advantages in improving collaboration efficiency and data security.\u003c/p\u003e"},{"header":"4. Industry application and benefit analysis","content":"\u003ch2\u003e4.1 Cross-domain application scenarios\u003c/h2\u003e\n\u003cp\u003e4.1.1 Medical construction projects\u003c/p\u003e\n\u003cp\u003eIn the expansion project of a Grade 3A hospital in Shenzhen, the BIM collaboration platform successfully realizes multi-dimensional application. The specific performance is as follows: 1) professional collaboration, complete the digital integration of 12 professional 3D models of construction, medical gas, clean engineering, etc.; 2) comprehensive optimization of pipelines, through the intelligent collision detection system to identify 3,856 pipeline conflicts, after optimization to save rework costs of about 4.2 million yuan; 3) operation and maintenance preparation stage, complete handover of 18,743 items of equipment technical parameters containing the BIM asset database at the completion level. Empirical data from the project show that after adopting the Tiangong DFC platform, the project design cycle was shortened by 30% and the construction change rate was reduced by 65%, which verifies the significant benefits of digital construction technology in the whole life cycle management of medical buildings.\u003c/p\u003e\n\u003ch4\u003e4.1.2 Assembly housing projects\u003c/h4\u003e\n\u003cp\u003eAn empirical study of a standardized residential construction project shows that: the rate of prefabricated components has been significantly increased from 65% to 89%; the mold design cycle has been shortened by 50%; and on-site construction errors have been strictly controlled within the range of \u0026plusmn;3mm.\u003c/p\u003e\n\u003ch3\u003e4.2 Integrated benefits assessment\u003c/h3\u003e\n\u003cp\u003eStatistical analysis based on 20 typical projects (Table 3):\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTable 3 Statistics on the benefits of Tiangong DFC application (n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage lift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBest Case Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDesign efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCost Control Precision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConstruction change rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCollaborative Communication Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReduced carbon emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Note: Data from Tiangong DFC Science and Technology Program Return, 2024）\u003c/p\u003e\n\u003ch3\u003e4.3 Industry Transformation Facilitator\u003c/h3\u003e\n\u003cp\u003eThe application of Tiangong DFC platform is promoting systematic changes in the ecology of the construction industry. Its core value is embodied in three dimensions: firstly, it realizes workflow reconstruction, eliminates information silos in traditional operations through the closed-loop management of data in the whole chain of design-calculation-construction; secondly, it promotes the reconfiguration of professional role boundaries, with designers deeply participating in the whole cycle of cost control and cost engineers intervening in the optimization of the program in advance through the methodology of value engineering; and furthermore, it promotes the evolution of the industry standard, and the data collaboration mechanism based on the platform Effectively promote the in-depth popularization of building information modeling (BIM) delivery standards. According to the \u0026ldquo;White Paper on Intelligent Construction Development (2023)\u0026rdquo; issued by the Digitization Branch of the China Construction Association, the industry penetration rate of this kind of integrated platform will reach more than 60% in the next five years, especially in the assembly rate of more than 30% of medium-sized public building projects will form a demonstration effect.\u003c/p\u003e"},{"header":"5. Conclusions and outlook","content":"\u003ch3\u003e5.1 Conclusion\u003c/h3\u003e\n\u003cp\u003eBased on the evolutionary framework of building information modeling (BIM) technology, this study systematically constructs the in-depth functional expansion system of SketchUp on the Tiangong DFC platform, and innovatively solves technical bottlenecks such as three-dimensional geometric data conversion in the field of architectural engineering (error rate ≤ 0.5mm), asynchronous multi-professional collaborative workflows (latency time reduction) and full life-cycle data tracing (completeness of 98.7%), by means of the mechanism of fusion of heterogeneous data from multiple sources and the optimization model for parametric design. 83%) and full life cycle data traceability (completeness of 98.7%) and other technical bottlenecks. The platform adopts a deep secondary research and development architecture, transforming SketchUp from a single conceptual design tool to a full-process BIM integration platform, realizing single-point maintenance and multi-application of design data through a centralized model work mode, and controlling the design error rate below 2% by combining with parametric data linkage mechanism. Its distributed cloud computing collaborative architecture builds a real-time rendering environment with a frame rate of ≥60fps, which empirically demonstrates that it can reduce project meeting time by 40% and improve decision response efficiency by 2.3 times.\u003c/p\u003e\n\u003cp\u003eThe technical system has three main features: ① digital translation of design intent based on a building semantic ontology library of 327 standard component types; ② integration of machine learning carbon emission prediction model (R²=0.93) and multi-objective optimization algorithms; ③ reconfiguration of the topology of BIM data flow (path coefficient β=0.87), and enhancement of technological synergy at the design-construction-Operation and Maintenance stages (Cronbach's α=0.91). Validated by 32 projects, the platform enables a 45.1±0.8% increase in design efficiency, a 1.92% error rate in construction budgets, a 23.6%-34.8% reduction in whole-life carbon emissions (p\u0026lt;0.01), a 35%-50% increase in overall efficiency, a 40-percentage-point increase in cost control accuracy, and a more than 20% decrease in carbon intensity. Structural equation modeling confirms that the platform provides an optimized decision-making paradigm with engineering mathematical rigor for digitization of the construction industry through technological synergy enhancement and data flow reconfiguration, which significantly improves total factor productivity and sustainable development value.\u003c/p\u003e\n\u003ch3\u003e5.2 Looking ahead\u003c/h3\u003e\n\u003cp\u003eSubsequent research can be deepened and expanded in the following directions: first, deeply integrate artificial intelligence and BIM technology, focus on the development of automated modeling algorithms based on generative adversarial network (GAN) and multimodal data fusion mechanism, build a design review knowledge map with autonomous optimization capability, and strengthen the ability of intelligent review of specifications and automatic generation of models; second, establish a chain management system that runs through the whole life cycle of the building. Secondly, establish a chain management system throughout the whole life cycle of the building, deeply integrate IoT sensing data and digital twin technology in the operation and maintenance stage, realize real-time diagnosis of facility status and dynamic optimization of energy efficiency, and promote the platform's function to the full cycle of deep collaborative evolution; at the same time, build a digital copyright protection system for building information models based on blockchain smart contracts, establish a decentralized version control and collaborative auditing system through zero-knowledge proof technology, and safeguard data privacy and intellectual property rights in multi-party collaboration. For the digital transformation needs of small and medium-sized construction enterprises, the Tiangong DFC platform forms a progressive BIM implementation paradigm and gradient technology solutions through the development of lightweight technology interfaces and configurable function modules. With the iteration of the algorithm engine, the improvement of the federal learning framework and the unification of building information standards, the platform will continue to upgrade the system architecture and develop into the core engine driving the intelligent transformation of the construction industry, injecting sustainable innovative kinetic energy and providing systematic technical support for the high-quality development of the global construction industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch4\u003eAuthor's contribution\u003c/h4\u003e\n\u003cp\u003eJunhao Guo: Responsible for the implementation of SketchUp secondary development module, including the intelligent component system and model information depth enhancement (GCN framework), the construction of the distributed architecture of the cloud collaboration platform (WebSocket protocol and CRDT algorithm), participate in the experimental validation and optimization of the results; write the first draft of the paper, and revise the technical chapters of the paper.\u003c/p\u003e\n\u003cp\u003eYidan Hu: designing industry application cases and benefit assessment models, completing empirical analysis of cross-domain projects (healthcare buildings, assembled houses), leading literature review and data visualization, coordinating research team collaboration, proofreading the paper format and improving the discussion section.\u003c/p\u003e\n\u003cp\u003eChenghua Chen: proposed the research framework and core algorithms, led the technical architecture design of the Tiangong DFC platform, developed the parametric modeling system based on IFC standards (M2D) and the engineering quantity calculation framework (M2Q), and was responsible for the experimental data acquisition and analysis.\u003c/p\u003e\n\u003ch4\u003eAdditional Information\u003c/h4\u003e\n\u003cp\u003eAll authors declare that there are no known competing financial interests or personal relationships that could unduly influence the results or reporting of this study.\u003c/p\u003e\n\u003ch4\u003eData Availability Statement\u003c/h4\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYAN Xueyuan, ZHENG Xinying, LIU Xuhong, et al. 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Application of Intelligent Construction Technology in Building Construction under the Background of Transformation and Development[C]//Professional Committee of Engineering Construction of Architectural Society of China, China Construction Eighth Engineering Bureau Co. Proceedings of Science and Technology Forum of Civil Engineering Construction Industry (2024) and the 15th China Construction Eighth Bureau Science and Technology Forum. China Construction Eighth Bureau Third Construction Company Limited;,2024:56-59.DOI:10.26914/c.cnkihy.2024.061544.\u003c/li\u003e\n\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":"Building Technology, Design for Cost (DFC), Digital Transformation, Model to Drawing (M2D), Quantity Computing (M2Q), Cloud Collaborative Architecture","lastPublishedDoi":"10.21203/rs.3.rs-6799832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6799832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study demonstrates the technical value of Tiangong DFC platform as a SketchUp extension solution in building digitization. Aiming at the problems of poor data coherence and low collaboration efficiency of traditional CAD software, the platform achieves breakthroughs through three major modules: parametric modeling system based on IFC standard (Model-to-Drawing), secondary development of Revit's quantity calculation framework (Model-to-Quantity), and real-time collaboration in the cloud by WebSocket protocol. The mixed research methodology (literature/case/experiment) shows that the platform significantly improves BIM functional integrity (χ\u0026sup2;=18.34), data management dimensions (d\u0026thinsp;=\u0026thinsp;2.17) and collaboration efficiency (Z\u0026thinsp;=\u0026thinsp;3.42). Empirical data showed that through semantic modeling and data closure, design efficiency was improved by 45% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cost error was \u0026le;\u0026thinsp;2% (R\u0026sup2;=0.963), and construction carbon emissions were reduced by 23%-35%. The study innovatively proposes a progressive BIM implementation path model and builds a three-dimensional transformation framework that includes technology adaptation, process reengineering and organizational change. The solution provides an operable digital paradigm for SMEs, realizes the deep integration of BIM while retaining the operational advantages of SketchUp, and promotes the transformation of the construction industry to a data-driven model through the whole industrial chain data closure, which has significant technical and economic value and industrial promotion potential.\u003c/p\u003e","manuscriptTitle":"Research on the Expansion and Optimization of SketchUp Functions by Tiangong DFC Platform in the Field of Construction Engineering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 16:40:43","doi":"10.21203/rs.3.rs-6799832/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":"a0ad7ac0-3fbe-4674-a750-6ee08dec2840","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50165248,"name":"Physical sciences/Engineering"},{"id":50165249,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-08-25T06:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 16:40:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6799832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6799832","identity":"rs-6799832","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}