Design and implementation of 3D geological suitability evaluation system for underground space development

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Addressing the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimension using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, enabling the integration and analytical assessment of multivariate geoscientific data. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Hangzhou demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing underground space 3D geological environment evaluation quantitative software system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 0. Introduction The scientific and rational development of urban underground space (UUS) is one of the most effective approaches to improve urban land use efficiency, alleviate spatial congestion, and mitigate "urban syndromes"(Broere 2016 ). However, the feasibility and cost of UUS development are highly susceptible to geological conditions, and the construction process may trigger a series of engineering, hydrogeological, and environmental geological issues, posing significant risks to public safety and property. Therefore, conducting a comprehensive evaluation of geological suitability prior to large-scale UUS development holds substantial practical value and promising prospects (Lu et al.,2016;Tan et al.,2021). Traditional geological suitability evaluations for UUS are predominantly based on 2D planar analyses, characterized by poor vertical resolution and limited applicability, which restrict the generalization and in-depth application of evaluation results. In recent years, advancements in 3D spatial analysis and comprehensive quantitative evaluation methodologies have provided new tools for UGEE. Current 3D UGEE methods have been applied to urban planning and engineering projects in multiple regions, yielding notable outcomes (Hou et al.,2016; Fang et al.,2017; Dou et al.,2021,2022). GIS-based software systems offer efficient tools and rapid solutions for UGEE. An integrated quantitative evaluation software system can streamline spatial analysis, quantitative assessment, and resource estimation. Most existing software systems for UGEE, such as the Urban Underground Resource Evaluation System (URE) developed by Nanjing University, the CEFUS-2D by Tsinghua University’s Underground Engineering Institute, the USP-DSS by Tongji University’s Underground Space Research Center, and GIS platform-based secondary development modules (Hu et al., 2016; Peng et al., 2018), operate primarily in two dimension. Despite their widespread use, these systems lack functionalities for 3D UGEE. To address the urgent need for 3D UGEE, researchers and practitioners have made progress in developing relevant functionalities (Wu et al., 2007 ;Xue et al., 2021; Xi et al., 2022). However, these efforts remain limited in integrating 3D spatial analysis methods and quantitative evaluation models. Commercial 3D geological software systems (e.g., DepthInsight™, SKUA-GOCAD™) focus primarily on constructing and visualizing high-precision 3D geological structure and attribute models, with minimal emphasis on 3D quantitative evaluation. Consequently, there is a pressing need to design and develop a dedicated software system that integrates 3D spatial analysis methods and comprehensive evaluation models to advance research and better support UUS planning and engineering. This study proposes a 3D UGEE system for UUS development (3D-UGEE v1.0), built on a mature methodological framework and implemented using Surpac™ 3D geological modeling software without reliance on GIS components. The system incorporates cutting-edge 3D evaluation techniques and has been validated through a case study in coastal area. Results demonstrate that the system transcends traditional 2D limitations, effectively extracts 3D evaluation insights from multidimensional geoscientific data, and significantly enhances vertical resolution. The software not only facilitates the adoption of 3D evaluation methodologies but also provides valuable references for urban underground engineering planning, reduces development risks, and safeguards public safety and property. 1. 3D UGEE methodology The 3D UGEE methodology is a systematic framework of quantitative methods and workflows designed to assess geological suitability for underground projects. This methodology encompasses five interconnected components: data collection and processing, 3D geological modeling, 3D indicator system construction, 3D spatial analysis, and 3D comprehensive quantitative evaluation. (Dou et al.,2021,2022). Depending on evaluation requirements, these components can be executed sequentially to achieve a holistic quantitative assessment. The 3D UGEE system proposed in this study is designed and developed based on this methodology, aiming to integrate multivariate geoscientific data, accommodate diverse analytical functions, and fulfill the demands of 3D UGEE. 2. System architecture and development approach 2.1 Overall architecture The 3D UGEE system is structured to align with the aforementioned methodology. It requires robust 3D visualization and interactive capabilities for real-time querying of multidimensional geoscientific data to support further analysis. Additionally, the system incorporates multiple 3D spatial analysis functions to extract evaluation factors, synergizing with quantitative evaluation methods to assess and compare geological suitability across target areas. The system architecture is divided into five core modules: (1) 3D data conversion and interaction module: Facilitates data interoperability; (2) 3D evaluation index screening module: Identifies critical evaluation parameters;(3) 3D spatial analysis module: includes submodules for 3D spatial statistical analysis, geological surface modeling, spatial distance analysis, and spatial interpolation; (4)3D comprehensive evaluation module: Integrates analysis results for final suitability scoring;(5) Underground space resource estimation module: Quantifies exploitable resources. Each module comprises multiple functionalities, as illustrated in Fig.1. 2.2 Development approach GIS-based development typically follows three modes: integrated secondary development, independent development, and pure secondary development (Wang and Liu, 2020). Compared to the high complexity and risks of independent development or the limited capability of pure secondary development for handling complex models, integrated secondary development balances efficiency and functionality (Smith et al,.2019;Li and Wu,2021). It reduces development risks, enhances efficiency, and effectively manages complex models and large-scale datasets. The Surpac software developed by France's Dassault Systèmes stands as one of the most widely used geological and mining software in its category globally. Characterized by mature architecture, stable operation, and user-friendly interface, it has been employed in over 100 countries for open-pit and underground exploration and mining projects. Although it offers the powerful Surpac Command Language (SCL) for internal scripting, the relatively low efficiency in writing and debugging SCL scripts limits its application to basic functionalities or batch operations, rendering it inadequate for conducting effective 3D analysis and quantitative evaluation of massive multidimensional and multivariate data. (Brown and Wilson,2018;Wu and Zhao,2020) To address these limitations, this study leverages the widely adopted Surpac software through an integrated secondary development approach to design and develop the 3D UGEE for UUS development. The development process fully utilizes Surpac's robust 3D graphics capabilities as the foundational platform, while implementing extended functionalities externally using the C# programming language. This integrated development strategy not only ensures the creation of a software system with powerful and stable 3D visualization and interactive capabilities but also enables efficient processing of multisource geoscientific data in Surpac-compatible formats for 3D UGEE. The main interface of the developed system is illustrated in Fig.2. 3. System functional module design and implementation 3.1 Multidimensional data conversion module The Multidimensional data conversion module is designed to transform geological data of varying formats and structures to meet the software’s workflow requirements for heterogeneous data inputs. This module comprises two submodules: (1) 2D-to-3D conversion: Converts 2D geoscientific data into 3D spatial representations; (2) 3D discretization transformation: Processes vector geological data (points, lines, surfaces, and volumes) into discrete units. Boolean assignment is applied to map vector data onto 3D cubic grids. 3.2 3D evaluation index screening module This module refines initially selected 3D evaluation indices to establish a more scientifically robust and rational evaluation system. It integrates two advanced screening methodologies: (1) maximum dissimilarity analysis: identifies minimally correlated indices; (2) rough set theory: optimizes index selection through attribute reduction. 3.3 3D spatial analysis module Spatial analysis encompasses a suite of methods and techniques designed to analyze spatial data based on the location and morphology of geographic objects. In the context of quantitatively evaluating geological suitability for underground space development, spatial analysis methods are instrumental in processing multidimensional data, including geological, remote sensing, and geotechnical test data. These methods facilitate the quantitative extraction of suitability evaluation factors, thereby supporting comprehensive data integration and analytical assessments. Traditional spatial analysis methods for geological suitability evaluation have predominantly focused on two-dimensional space. Key techniques include planar spatial overlay, planar data interpolation, and planar buffer analysis. These methods are typically applied to 2D geological data, such as topographic surfaces within designated work areas, borehole stratigraphic layering information, and soil testing data. While effective for 2D applications, these methods are limited in their ability to capture the full complexity of 3D geological structures. In contrast, 3D spatial analysis methods offer a more advanced and comprehensive approach. These methods include 3D spatial interpolation for discrete point data (e.g., soil testing data), 3D distance field analysis for characterizing the influence scope and degree of evaluation factors, and stratigraphic thickness analysis based on 3D geological models. The 3D spatial analysis module is structured into several sub-modules, each addressing specific aspects of spatial analysis: (1)3D spatial statistical analysis sub-module: this sub-module employs mathematical statistical models to analyze and extract features of 3D geological bodies, enabling the identification of spatial patterns and trends.(2) 3D geological body surface analysis sub-module: focused on the morphological characteristics of geological body surfaces, this sub-module extracts and analyzes 3D surface information, such as slope, curvature, and aspect.(3)3D spatial distance analysis sub-module:This sub-module quantitatively characterizes the degree and scope of the influence of 3D geological information on the development of underground space resources, providing critical insights for resource planning and risk assessment. (4)3D spatial overlay analysis sub-module:designed to integrate diverse spatial data in various forms, this sub-module facilitates the combination of multiple data layers to create comprehensive spatial models.(5)3D spatial interpolation analysis sub-module:this sub-module interpolates discrete geological data to analyze and study its global spatial distribution, enabling the creation of continuous 3D models from sparse data points. Each functional module is further subdivided into specialized sub-modules. 3.3.1 3D spatial statistical analysis module 3D spatial statistical analysis module is designed to employ various statistical models for calculating comprehensive values of block models. This module addresses the need to analyze the statistical characteristics of geological bodies, with key application scenarios including the calculation and extraction of stratigraphic layer complexity, the thickness of unfavorable geological bodies, and the burial depth of confined aquifers. Within this system, the 3D spatial statistical analysis module is structured into three sub-modules, each tailored to specific analytical requirements: (1) Geological body complexity analysis sub-module: this sub-module addresses the mechanism by which the construction difficulty of UUS increases with the complexity of engineering geological formations, both in shallow and deep underground space. It provides an effective representation of the relative magnitude of geological complexity within a given region, enabling the identification of areas that may pose greater challenges for underground construction.(2)3D geological body thickness analysis sub-module:Designed specifically for unfavorable geological bodies in underground space, this sub-module operates on the principle that thicker geological bodies are more detrimental to the development and construction of underground spaces. It effectively characterizes the distribution features of geological body thickness, providing critical insights for risk assessment and mitigation strategies.(3)3D geological body depth analysis sub-module:This sub-module is used to statistically calculate the burial depth of target geological bodies in the vertical direction. It supports the quantitative analysis of geological features at varying depths, facilitating the identification of key geological constraints and opportunities for UUS development. For example, the Fig.3 demonstrates the calculation results of the total number of stratigraphic horizons along the depth direction within a specified depth range in a study area. 3.3.2 3D geological surface analysis module 3D geological surface analysis module is designed to address the need for analyzing the spatial distribution and morphological characteristics of geological bodies. Its primary purpose is to evaluate the spatial distribution and morphological features of geoscientific elements. Within this system, the module is structured into four sub-modules: (1) 3D geological body surface extraction sub-module: this sub-module extracts surface units from the 3D geological body model, which is represented by cubic units. The extracted surface units can be utilized for further spatial analysis or directly incorporated as evaluation factors in comprehensive assessments. This capability is critical for understanding the external geometry of geological bodies and their spatial relationships.(2) 3D differential geological interface analysis sub-module:this sub-module identifies and extracts cubic units that represent the locations of differential stratigraphic contact interfaces within a specified depth range. By pinpointing these interfaces, the function facilitates the analysis of stratigraphic boundaries and their spatial variability, which is essential for assessing geological heterogeneity.(3) 3D slope analysis sub-module:this sub-module quantitatively evaluates the degree of inclination of geological structural surfaces. It can be applied to topographic surfaces or other geological body surfaces, with user-defined thresholds to identify structures or locations that significantly impact underground space development. This analysis is particularly valuable for assessing slope stability and its implications for construction and resource development.(4) 3D relief analysis sub-module:this sub-module provides a quantitative analysis of the relief degree of geological body surfaces. By characterizing surface roughness and elevation variations, it offers insights into the topographic complexity of geological formations, which is crucial for evaluating their suitability for engineering projects. For example, the Fig.4 demonstrates the calculation results of extraction results of interface between upper soft and lower hard soil. 3.3.3 3D spatial distance analysis module 3D Spatial distance analysis module is designed to quantify the extent to which geological bodies influence their surrounding geological environment through various analytical methods. This smodule serves as a critical tool for determining the influence distance of evaluation factors. It operates by calculating the Euclidean distance in 3D space between all discrete block units within the working area and a specified target block unit. Users can select different attribute variables to perform 3D Distance Field Analysis, enabling the derivation of distance values within a defined influence range of the geological body. This functionality is essential for assessing the spatial impact of geological features on their surroundings. Through this module, users can obtain Euclidean distance values associated with evaluation factors, facilitating a comprehensive assessment of the spatial influence of geological bodies (Fig.5). 3.3.4 3D spatial overlay analysis module 3D spatial overlay analysis module is designed to perform a series of set-based analyses and calculations on 3D geological body units within underground spaces. This function is particularly relevant in practical evaluations of underground spatial geological environments, where the analysis objects may include 3D cubic block models representing diverse geoscientific information or 3D solid models characterizing existing underground conditions. The primary objective of the 3D spatial overlay analysis function is to conduct spatial overlay analyses and calculations on 3D geological body units. The overlay objects can encompass geological solid models, cubic units representing various geoscientific attributes, or different spatial extents. Additionally, the overlay process supports a range of logical operations, including "AND," "OR," and "NOT," enabling flexible and comprehensive spatial analyses.For example, the Fig.6 demonstrates the calculation results of 3D overlay. 3.3.5 3D spatial interpolation module 3D spatial interpolation module is specifically designed to perform interpolation analysis and processing of discrete data derived from soil tests, rock mass tests, in-situ experiments, and other relevant sources. This module enables the analysis and study of the spatial distribution of such data, providing critical insights into the engineering properties of soil and rock masses within the area of interest. To address practical requirements, the module integrates four widely used interpolation methods: Inverse Distance Weighting (IDW), Ordinary Kriging, Simple Kriging, and Nearest Neighbor Interpolation. 3.4 3D comprehensive evaluation module 3D comprehensive evaluation module represents the final stage in the evaluation process, where the analyzed and extracted 3D evaluation factor information from other system modules undergoes data fusion to produce comprehensive evaluation results. This module is pivotal in enabling tasks such as geological suitability evaluation and development analysis for underground spaces. It consists of several key functional components, each designed to address specific aspects of the evaluation process: (1)Weight determination model: This model calculates the weights of evaluation factors within each indicator system, quantifying their relative importance in the context of UUS development. It incorporates both subjective and objective weight determination methods, including the Analytic Hierarchy Process (AHP) for subjective weighting and the Entropy weight method for objective weighting. This dual approach ensures a balanced and scientifically robust determination of factor significance.(2) Comprehensive evaluation model: this module calculates the suitability level of each cubic block unit based on the derived weights. It integrates a variety of evaluation methodologies, including index overlay method, set pair analysis, extenics Model and cloud model. 3.5 Underground space resources estimation Underground space resources estimation constitutes an indispensable component of the comprehensive evaluation of the geological environment of underground spaces. This module not only enables the construction of 3D current status models, such as building pile foundation solid models, but also facilitates a comprehensive calculation and analysis of the volume of already developed underground space resources. This process enhances the understanding of the utilization status across various underground development horizons and depths, thereby better supporting planning and construction efforts. Users can sequentially execute the following steps—line extension-based 3D modeling, 3D model value assignment, and underground space resource calculation—to achieve applications including the construction of 3D current status solid models, discrete visualization of models with varying numerical attributes, and statistical analysis of underground space resources based on different parameters. (1) Line extension-based 3D modeling sub-module: this sub-module is designed for 2D contour line models with depth information, targeting application objects such as building pile foundations, ecological protection zones, main roads, and surface water systems. It constructs 3D current status models by integrating line models with their corresponding depth attributes. Taking a 2D line model of a building pile foundation as an example, the function generates a planar model for each imported linear pile foundation model. Utilizing the software’s line extension capability, each underground pile foundation is vertically stretched to its specified depth in compliance with relevant standards and specifications. The top and bottom surfaces of the pile foundation are then delineated, and the side surfaces are enclosed to form multiple independent and sealed 3D solid models. The entire process is fully automated, ensuring operational efficiency and precision. (2) 3D model value assignment sub-module: this sub-module is tailored for preconstructed 3D current status solid models, such as pile foundation models. It assigns user-defined numerical values to these models and discretizes them into 3D block models, laying the groundwork for subsequent resource estimation and analytical workflows. (3) Underground space resource estimation sub-module:this sub-module performs statistical calculations and analyses of the volume of developed underground space resources. Users can define target attributes and storage paths based on practical requirements, select parameters such as attribute values and depth ranges for computation, and export results in CSV format. The output effectively summarizes the resource volumes corresponding to all current condition attributes within the "underground space resource volume" category. The remaining available underground space resource volume is derived by subtracting the sub-item results from the total volume, providing a clear quantification of resource utilization and availability. For example, the Fig.7 demonstrates the calculation results of 3D solid model construction for underground pile foundations of buildings. 4. Case study The study area is located in the coastal area, covering approximately 3.2 square kilometers. The landform of the study area was formed through the combined effects of tectonic, erosional, denudational, and depositional processes, as well as internal and external geological forces, since the Cenozoic era. Its distribution is closely related to the geological structures along the route, with the main landform units being tectonic-erosional landforms, piedmont slope depositional landforms, and river erosion-depositional landforms. The terrain of the site fluctuates significantly, with an overall trend of being higher in the east and north, and lower in the west and south. The surface is covered with modern buildings and roads. As a key development and planning area within coastal area, the study area has great potential for future development. Therefore, the development and utilization of underground space in the study area will further meet the needs of future urban development. However, the geological structure of the study area is relatively complex, and the development and utilization of underground space are constrained by the quality of geological conditions. The ubiquitous fill layers, which are uneven in thickness and shallowly buried, exhibit high compressibility and low strength, and are prone to compressive deformation. During construction, slight subsidence of the ground may occur due to the consolidation and settlement of the fill itself. The weathered deep troughs formed by fully and strongly weathered granite can also adversely affect tunnel excavation, foundation stability, and uniformity. Currently, the development and utilization of underground space in the area mainly focuses on shallow layers (0-15 m). Based on multi-dimensional and multi-source data, including borehole data, DEM (Digital Elevation Model), planar geological maps of the study area, the 3D engineering geological model was constructed with Geomodeller TM software. This model reveals the 3D spatial distribution of strata, rock masses, and geological structures within the study area (Fig.8). The development of underground space in the study area is significantly constrained by factors such as fill layers and completely weathered bedrock. Based on the data and information available in the study area, as well as the visualization and analysis capabilities of the 3D geological model and statistical results of different stratigraphic layers, a 3D evaluation index system for the geological suitability of UUS development in the area was constructed, considering four aspects: topography and landform, geotechnical engineering properties, hydrogeological conditions, and unfavorable geological conditions (Table 1). This was tailored to the actual geological background of the area, and multiple data analysis methods in the software were employed to extract the 3D evaluation index (Fig.9). Table 1 3D evaluation index system and the extraction methods of the study area Evaluation index 3D spatial analysis method Data source Ground elevation（C 1 ） Depth / & Distance analysis DEM Geological structure complexity（C 2 ） Complexity / & Distance analysis 3D geological model Bedrock surface depth（C 3 ） Depth / & Distance analysis 3D geological model Distance to confined aquifer（C 4 ） Distance analysis 3D geological model Artificial fill thickness（C 5 ） Thickness & Distance analysis 3D geological model Completely weathered layer thickness（C 6 ） Thickness analysis 3D geological model Distance to fault（C 7 ） Distance analysis 3D geological model Building pile foundation（C 8 ） Overlay & Distance analysis Planning data The subjective and objective weights were calculated using the Analytic Network Process, ANP (Satty 1996, 2004) within the game theory (Zhu et al. 2013) combined weight model and an improved CRITIC method ( Liu et al 2021) (Table.2). The optimal weight values were then determined by integrating the subjective and objective weights based on game theory. The calculated weight coefficients for the combined weights were 0.7 and 0.3, respectively. These coefficients were incorporated into game theory to derive the final combined weights, as presented in Table 2. Based on the extracted 3D evaluation index information and evaluation index combined weight, this study employed a multi-level index superposition method to integrate multidisciplinary geological data, enabling the quantitative calculation of 3D geological suitability across the study area. Subsequent analysis focused on the shallow subsurface layer (0–15 m depth), with evaluation results for geological suitability illustrated in Fig.10. Table 2 Combination weight of 3D evaluation indexes of the study area Evaluation index AHP Improved CRITIC Game theory Rank Ground elevation（C 1 ） 0.082 0.121 0.094 8 Geological structure complexity（C 2 ） 0.074 0.203 0.113 6 Bedrock surface depth（C 3 ） 0.079 0.152 0.101 7 Distance to confined aquifer（C 4 ） 0.095 0.079 0.090 5 Artificial fill thickness（C 5 ） 0.091 0.113 0.098 4 Completely weathered layer thickness（C 6 ） 0.147 0.073 0.125 3 Distance to fault（C 7 ） 0.195 0.103 0.167 2 Building pile foundation（C 8 ） 0.237 0.156 0.213 1 The shallow layer ranges from the surface to a depth of 0-15 meters. According to the statistical results, approximately 21.62% of the total volume in the shallow layer is already constrained or developed, with the remaining underground space resource capacity accounting for about 78.38%. Among them, Class I accounts for 7.03% of the total volume, Class II accounts for 65.42%, Class III accounts for 5.32%, and Class IV accounts for 0.61%. The 3D comprehensive evaluation results (Fig.10) indicate that the development of underground space in this layer is mainly affected by the influence range of faulted structural zones and the thickness of artificial fill. As the depth increases, the overall evaluation results of underground space change little. Among them, Classes III and IV areas are mainly affected by the superposition of nearby faulted structural zones and thicker artificial fill, concentrated in the southwest of the working area and mainly distributed along the spatial morphology of the faulted structural zones. Due to the poor quality of underground space resources in these areas, the UUS development is relatively unfavorable, requiring more costs. Moreover, when conducting underground space engineering construction, special attention should be paid to a series of engineering problems caused by faulted zones. In the remaining areas of the working zone, the shallow layer has good conditions for development and utilization, and is generally suitable for the UUS development. It can be used as a key area for shallow layer utilization in underground space development. However, attention should also be paid to thicker fill layers in some local areas. The above results fully demonstrate that the 3D evaluation system for UGEE development designed and developed in this paper can be well applied to the quantitative evaluation of geological suitability for underground space development. The evaluation results obtained in this paper have 3D attributes, which facilitate the convenient acquisition of comprehensive geological suitability evaluation results for any spatial position and interval (as shown in Fig.10), allowing for better viewing and analysis of the variation characteristics of geological suitability within underground space. Furthermore, the 3D spatial analysis module makes it easier to mine 3D evaluation information for various evaluation index. Therefore, compared with traditional 2D planar evaluation results, it has better accuracy and practicability, and its presentation is more intuitive. It is suitable for the 3D planning and evaluation of UUS, and can effectively provide detailed evaluation results and basis for underground space planning, saving development costs and reducing development risks. 5. Conclusion In response to the practical demands for quantitative evaluation methods in 3D UGEE, this study developed a software system based on the Surpac TM , capable of conducting comprehensive 3D UGEE within three dimension. The system integrates specialized functional modules, including multidimensional data conversion, 3D spatial statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, encompassing the entire workflow of 3D UGEE. Compared to conventional 2D evaluation software system, this system achieves seamless integration of 3D geological information technology and quantitative suitability evaluation methodologies. Its innovative 3D spatial analysis algorithms and multidimensional evaluation framework enable advanced data mining and suitability assessments for underground space development, leveraging large-scale geoscientific datasets. This approach effectively addresses limitations inherent in traditional 2D evaluations, such as low depth resolution and the loss of critical 3D spatial information. The system’s efficacy was validated through a case study on UUS geological suitability evaluation in coastal area, China. Results demonstrated its robust practicality and adaptability, significantly enhancing the depth resolution of evaluation outcomes while enabling in-depth extraction of 3D evaluation factors. The software provides a scientifically grounded methodology for detailed underground space planning and utilization, offering actionable insights for urban development. Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author Meijun Xu on reasonable request via e-mail [email protected] . Author contributions Conceptualization: D. F. F and X.M.J.; Methodology: G. Y and Z.H.; Formal analysis and investigation: L.L.; Writing - original draft preparation: L.Y.M.; Writing - review and editing: Y.B.K.; Funding acquisition: D.F.F. and G. Y.; Resources: Y. P. and G. Y. Funding This study was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China, grant number 23KJD170001, the Open project of Key Laboratory of Geological Safety of Coastal Urban Underground Space, Ministry of Natural Resources, grant number BHKF2023Y03 and the Project of Research on Karst Exploration and Risk Prevention in Typical Areas of Huzhou, grant number 2024ZJDZ023. Declarations Competing interests The authors declare no competing interests. References Broere,W., Urban underground space: solving the problems of today’s cities[J]. Tunnelling and Underground Space Technology, 2016, 55: 245-248 Brown, K., & Wilson, R. (2018). Limitations of SCL scripting in Surpac for advanced geospatial analysis. Computers & Geosciences, 111, 182-190. Dou F.F., Xing H.X., Li X.H., Y F., Lu Z.T., Li X.L. 3D geological suitability evaluation for urban underground space development based on combined weighting and improved TOPSIS[J]. Natural Resources Research, 2022, 31: 693–711. Dou, F.F., Li, X.H., Xing, H.X. (2021). 3D geological suitability evaluation for urban underground space development–A case study of Qianjiang Newtown in Hangzhou, Eastern China. Tunnelling and Underground Space Technology, 115, 104052. Fang, Y.C., Gong, R.X,. Li, S.F., Pan, S.Y., Gu, M.G,. Huang, W.P (2017) Suitability evaluation of underground space development based on a three-dimensionalgeological model, using the Jiaxing urban geological survey as an example. Shanghai Land & Resources. 38, 43-45. Hou W.S., Yang L., Deng D.C., YL., Keith Clarke., YZ.J (2016) Assessing quality of urban underground spaces by coupling 3D geological models: The case study of Foshan city, South China. Computers & Geosciences, 89:1-11. Hu Xuexiang ,Liu ganbin Tao Haibin.Research on evaluation suitability for the development of underground space in Ningbo city based on ArcGIS. Chinese Journal of Underground Space and Engineering, 2016, 12(6)1439-1444. Li, M., & Wu, J. (2021). Efficiency optimization in GIS-based mining software development. Transactions of Nonferrous Metals Society of China, 31(2), 401-410. Liu Y., Hu Y., Hu Y.., Hu Y.M., Cao Y.Q., Liu Z.Y (2021) Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory[J]. Journal of environmental sciences, 104: 40-52. Lu, Z., Wu, L., Zhuang, X., Rabczuk, T. (2016). Quantitative assessment of engineering geological suitability for multilayer Urban Underground Space. Tunnelling and Underground Space Technology, 59, 65–76. Peng J., Peng F.L (2018) A GIS-based evaluation method of underground space resources for urban spatial planning: Part 2 application. Tunnelling and Underground Space Technology, 77: 142–165. Saaty, T. L (2004) Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network. Journal of Systems science and Systems engineering, 13, 129-157. Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh: RWS publications. Smith, A., Chen, L., & Zhang, Q. (2019). Integrated secondary development frameworks for geospatial software. International Journal of Mining Science and Technology, 29(4), 579-586. Tan, F., Wang, J., Jiao, Y. Y., M, B. C., He, L.L. (2021). Suitability evaluation of underground space based on finite interval cloud model and genetic algorithm combination weighting. Tunnelling and Underground Space Technology, 108, 103743. Wang, Y., & Liu, X. (2020). Comparative analysis of GIS-based development modes in mining applications. Journal of Geospatial Engineering, 12(3), 45-62. Wu Lixin, Jiang Yun,Che Defu, et al. Fuzzy synthesis evaluation and 3D visualization for resource quality of urban underground space. Journal of China University of Mining Technology, 2007, 36( 1) : 97-102 Wu, Q., & Zhao, L. (2020). Hybrid development strategies for 3D geovisualization tools. ISPRS International Journal of Geo-Information, 9(7), 432. Xi Yue, Zhang Wanbin, Xu Wenyun Xu Ben Liu Baolin.Safety risk Evaluation of Underground Space Development in Yuzhong Peninsula of Chongging.Chinese Journal of Underground Space and Engineering. 2022，18(2)：359-365. Xue Tao, Shi Yujin，Zhu Xiaodi, et al. Research on 3D modeling method for evaluation of urban underground space resources: A case study in Shanghai. Earth Science Frontiers， 2021， 28(4): 373- 382 Zhu, Y., Li, L (2014) A regional competitive water resources security evaluation model based on Nash equilibrium restrictions. Water Policy, 16(4), 690-703. Additional Declarations No competing interests reported. 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Dou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3QsUoDQRCA4TkW1maStHMo6iOMBBKElTyIzUjgOttgkeJAWMu0+hZXCXYnC1uJtnZZG+vYCIKIm5TCnrGz2L/ej50ZgFzuP+buAwHsA6gWQC4IB0q50En8lCMZAmgBCA9mr7zSFW9NihdbGX7CQ+oSfa+K5zfLB2OtXoNoh0OHwDA3pylSeqWObywf3Vk9ZkGHI9drA/jqvE4QXi78bs9+Fs2yHpHQmvSFi9qliVc6Ep40fuedhONgl8i0DTlrPMZfpEJWv5DNLtePPI1kRtIaJBePLB27bC62mvFJHOy2/PiiyWDhXFjNTZIkkr89z+VyudyPvgH2jlk2FmXwbgAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Second Normal University","correspondingAuthor":true,"prefix":"","firstName":"Fanfan","middleName":"","lastName":"Dou","suffix":""},{"id":462815654,"identity":"77603760-bced-4824-a376-6da73d7ce75e","order_by":1,"name":"Meijun Xu","email":"","orcid":"","institution":"Qingdao Geological Exploration Development 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Bureau","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-04-24 08:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83648447,"identity":"7b5f156c-9b66-4401-b841-21f0b867d7d7","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282699,"visible":true,"origin":"","legend":"\u003cp\u003eStructure chart of 3D-UGEE (version 1.0)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/283649cf0d0be099d72cbd64.png"},{"id":83648450,"identity":"cff2d248-88d9-4cfe-906a-c14aa6691638","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":925400,"visible":true,"origin":"","legend":"\u003cp\u003eMain interface of 3D-UGEE (version 1.0)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/38547d47dda0e884f78be00e.png"},{"id":83648451,"identity":"2db1529d-ae3f-4c71-a5d2-0bc4b1db9af1","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":542543,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation results with depth analysis generated using 3D-UGEE (version 1.0)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/ab18956b3a2beb7480c852eb.png"},{"id":83648452,"identity":"8b2faf84-fb02-4dd2-acc8-9a05a24e6687","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228872,"visible":true,"origin":"","legend":"\u003cp\u003eExtraction results of interface between upper soft and lower hard soil generated using 3D-UGEE (version 1.0)\u003c/p\u003e\n\u003cp\u003e（a）Upper soft and lower hard soil,（b）Extraction results of interface\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/a1b4fb0bc0b75d8724e27346.png"},{"id":83648453,"identity":"4eb9e4cb-e428-4e19-af69-5e107e356e82","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":315440,"visible":true,"origin":"","legend":"\u003cp\u003e3D buffering result generated using 3D-UGEE (version 1.0)\u003c/p\u003e\n\u003cp\u003e(a) Surface water line model, (b) Surface water buffering result\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/129e1b11176ebe8a07f7543f.png"},{"id":83648456,"identity":"48000728-5bb2-44fc-987f-433e0efc4f8f","added_by":"auto","created_at":"2025-05-30 06:21:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":674163,"visible":true,"origin":"","legend":"\u003cp\u003eResult after 3D overlay generated using 3D-UGEE (version 1.0)\u003c/p\u003e\n\u003cp\u003e(a) Bedrock 3D model, (b) Pile foundation 3D model,\u003c/p\u003e\n\u003cp\u003e(c) “or” operations overlay result, (d) “ and”operations overlay result\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/6f16fc2f71954e382d505a4a.png"},{"id":83649037,"identity":"bcc87dc9-5b62-4620-80dd-999c937677a6","added_by":"auto","created_at":"2025-05-30 06:29:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":268841,"visible":true,"origin":"","legend":"\u003cp\u003e3D solid model construction for underground pile foundations of buildings generated using 3D-UGEE (version 1.0)\u003c/p\u003e\n\u003cp\u003e(a) 2D wireframe model of the building with depth Information, (b) 3D solid model of the pile foundation.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/05be52f282600cada498f400.png"},{"id":83648455,"identity":"27f3ca6e-c088-43f6-ab6c-6af99bc4d660","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":567287,"visible":true,"origin":"","legend":"\u003cp\u003e3D engineer geological structure model of the study area\u003c/p\u003e\n\u003cp\u003eThe development of underground space in the study area is significantly constrained\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/096790e3331529b49920ab21.png"},{"id":83648454,"identity":"92c1e2e5-44c8-4167-ae31-7ddebb9ae80b","added_by":"auto","created_at":"2025-05-30 06:21:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1630710,"visible":true,"origin":"","legend":"\u003cp\u003e3D spatial analysis results of 3D evaluation factors generated using 3D-UGEE (version 1.0)\u003c/p\u003e\n\u003cp\u003e(a) ground elevation, (b) geological structure complexity, (c) bedrock surface depth, (d) distance to confined aquifer, (e) artificial fill thickness, (f) completely weathered layer thickness, (g) distance to fault, (h) building pile foundation\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/d6a52539c771c69a20f94702.png"},{"id":83648458,"identity":"ec427c69-cd24-4006-84e9-d13fe83fa311","added_by":"auto","created_at":"2025-05-30 06:21:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":990266,"visible":true,"origin":"","legend":"\u003cp\u003e3D comprehensive evaluation results of shallow layer in the study area generated using 3D-UGEE (version 1.0)\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/7e1b301acf50e71a4c264a85.png"},{"id":90293559,"identity":"3803d4b0-09f1-475d-91a5-cd749d37d2ac","added_by":"auto","created_at":"2025-09-01 07:48:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6264437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518793/v1/7172f81b-11be-442d-8eb3-f75de7571142.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and implementation of 3D geological suitability evaluation system for underground space development","fulltext":[{"header":"0. Introduction","content":"\u003cp\u003eThe scientific and rational development of urban underground space (UUS) is one of the most effective approaches to improve urban land use efficiency, alleviate spatial congestion, and mitigate \"urban syndromes\"(Broere \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the feasibility and cost of UUS development are highly susceptible to geological conditions, and the construction process may trigger a series of engineering, hydrogeological, and environmental geological issues, posing significant risks to public safety and property. Therefore, conducting a comprehensive evaluation of geological suitability prior to large-scale UUS development holds substantial practical value and promising prospects (Lu et al.,2016;Tan et al.,2021). Traditional geological suitability evaluations for UUS are predominantly based on 2D planar analyses, characterized by poor vertical resolution and limited applicability, which restrict the generalization and in-depth application of evaluation results. In recent years, advancements in 3D spatial analysis and comprehensive quantitative evaluation methodologies have provided new tools for UGEE. Current 3D UGEE methods have been applied to urban planning and engineering projects in multiple regions, yielding notable outcomes (Hou et al.,2016; Fang et al.,2017; Dou et al.,2021,2022).\u003c/p\u003e \u003cp\u003eGIS-based software systems offer efficient tools and rapid solutions for UGEE. An integrated quantitative evaluation software system can streamline spatial analysis, quantitative assessment, and resource estimation. Most existing software systems for UGEE, such as the Urban Underground Resource Evaluation System (URE) developed by Nanjing University, the CEFUS-2D by Tsinghua University\u0026rsquo;s Underground Engineering Institute, the USP-DSS by Tongji University\u0026rsquo;s Underground Space Research Center, and GIS platform-based secondary development modules (Hu et al., 2016; Peng et al., 2018), operate primarily in two dimension. Despite their widespread use, these systems lack functionalities for 3D UGEE.\u003c/p\u003e \u003cp\u003eTo address the urgent need for 3D UGEE, researchers and practitioners have made progress in developing relevant functionalities (Wu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e;Xue et al., 2021; Xi et al., 2022). However, these efforts remain limited in integrating 3D spatial analysis methods and quantitative evaluation models. Commercial 3D geological software systems (e.g., DepthInsight\u0026trade;, SKUA-GOCAD\u0026trade;) focus primarily on constructing and visualizing high-precision 3D geological structure and attribute models, with minimal emphasis on 3D quantitative evaluation. Consequently, there is a pressing need to design and develop a dedicated software system that integrates 3D spatial analysis methods and comprehensive evaluation models to advance research and better support UUS planning and engineering.\u003c/p\u003e \u003cp\u003eThis study proposes a 3D UGEE system for UUS development (3D-UGEE v1.0), built on a mature methodological framework and implemented using Surpac\u0026trade; 3D geological modeling software without reliance on GIS components. The system incorporates cutting-edge 3D evaluation techniques and has been validated through a case study in coastal area. Results demonstrate that the system transcends traditional 2D limitations, effectively extracts 3D evaluation insights from multidimensional geoscientific data, and significantly enhances vertical resolution. The software not only facilitates the adoption of 3D evaluation methodologies but also provides valuable references for urban underground engineering planning, reduces development risks, and safeguards public safety and property.\u003c/p\u003e"},{"header":"1. 3D UGEE methodology","content":"\u003cp\u003eThe 3D UGEE methodology is a systematic framework of quantitative methods and workflows designed to assess geological suitability for underground projects. This\u0026nbsp;methodology encompasses five interconnected components: data collection and processing, 3D geological modeling, 3D indicator system construction, 3D spatial analysis, and 3D comprehensive quantitative evaluation. (Dou et al.,2021,2022).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Depending on evaluation requirements, these components can be executed sequentially to achieve a holistic quantitative assessment. The 3D UGEE system proposed in this study is designed and developed based on this methodology, aiming to integrate multivariate geoscientific data, accommodate diverse analytical functions, and fulfill the demands of 3D UGEE.\u003c/p\u003e"},{"header":"2. System architecture and development approach","content":"\u003cp\u003e\u003cstrong\u003e2.1 Overall architecture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D UGEE system is structured to align with the aforementioned methodology. It requires robust 3D visualization and interactive capabilities for real-time querying of multidimensional geoscientific data to support further analysis. Additionally, the system incorporates multiple 3D spatial analysis functions to extract evaluation factors, synergizing with quantitative evaluation methods to assess and compare geological suitability across target areas. The system architecture is divided into five core modules: (1) 3D data conversion and interaction module: Facilitates data interoperability; (2) 3D evaluation index screening module: Identifies critical evaluation parameters;(3) 3D spatial analysis module: includes submodules for 3D spatial statistical analysis, geological surface modeling, spatial distance analysis, and spatial interpolation; (4)3D comprehensive evaluation module: Integrates analysis results for final suitability scoring;(5) Underground space resource estimation module: Quantifies exploitable resources. Each module comprises multiple functionalities, as illustrated in Fig.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Development approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGIS-based development typically follows three modes: integrated secondary development, independent development, and pure secondary development (Wang and Liu,\u0026nbsp;2020). Compared to the high complexity and risks of independent development or the limited capability of pure secondary development for handling complex models, integrated secondary development balances efficiency and functionality (Smith et al,.2019;Li and Wu,2021). It reduces development risks, enhances efficiency, and effectively manages complex models and large-scale datasets.\u003c/p\u003e\n\u003cp\u003eThe Surpac software developed by France\u0026apos;s Dassault Syst\u0026egrave;mes stands as one of the most widely used geological and mining software in its category globally. Characterized by mature architecture, stable operation, and user-friendly interface, it has been employed in over 100 countries for open-pit and underground exploration and mining projects. Although it offers the powerful Surpac Command Language (SCL) for internal scripting, the relatively low efficiency in writing and debugging SCL scripts limits its application to basic functionalities or batch operations, rendering it inadequate for conducting effective 3D analysis and quantitative evaluation of massive multidimensional and multivariate data. (Brown and Wilson,2018;Wu and Zhao,2020)\u003c/p\u003e\n\u003cp\u003eTo address these limitations, this study leverages the widely adopted Surpac software through an integrated secondary development approach to design and develop the 3D UGEE for UUS development. The development process fully utilizes Surpac\u0026apos;s robust 3D graphics capabilities as the foundational platform, while implementing extended functionalities externally using the C# programming language. This integrated development strategy not only ensures the creation of a software system with powerful and stable 3D visualization and interactive capabilities but also enables efficient processing of multisource geoscientific data in Surpac-compatible formats for 3D UGEE. The main interface of the developed system is illustrated in Fig.2.\u003c/p\u003e"},{"header":"3. System functional module design and implementation","content":"\u003cp\u003e\u003cstrong\u003e3.1 Multidimensional data conversion module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Multidimensional data conversion module is designed to transform geological data of varying formats and structures to meet the software\u0026rsquo;s workflow requirements for heterogeneous data inputs. This module comprises two submodules: (1) 2D-to-3D conversion: Converts 2D geoscientific data into 3D spatial representations; (2) 3D discretization transformation: Processes vector geological data (points, lines, surfaces, and volumes) into discrete units. Boolean assignment is applied to map vector data onto 3D cubic grids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 3D evaluation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eindex\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;screening module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis module refines initially selected 3D evaluation indices to establish a more scientifically robust and rational evaluation system. It integrates two advanced screening methodologies: (1) maximum dissimilarity analysis: identifies minimally correlated indices; (2) rough set theory: optimizes index selection through attribute reduction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 3D spatial analysis module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial analysis encompasses a suite of methods and techniques designed to analyze spatial data based on the location and morphology of geographic objects. In the context of quantitatively evaluating geological suitability for underground space development, spatial analysis methods are instrumental in processing multidimensional data, including geological, remote sensing, and geotechnical test data. These methods facilitate the quantitative extraction of suitability evaluation factors, thereby supporting comprehensive data integration and analytical assessments.\u003c/p\u003e\n\u003cp\u003eTraditional spatial analysis methods for geological suitability evaluation have predominantly focused on two-dimensional space. Key techniques include planar spatial overlay, planar data interpolation, and planar buffer analysis. These methods are typically applied to 2D geological data, such as topographic surfaces within designated work areas, borehole stratigraphic layering information, and soil testing data. While effective for 2D applications, these methods are limited in their ability to capture the full complexity of 3D geological structures.\u003c/p\u003e\n\u003cp\u003eIn contrast, 3D spatial analysis methods offer a more advanced and comprehensive approach. These methods include 3D spatial interpolation for discrete point data (e.g., soil testing data), 3D distance field analysis for characterizing the influence scope and degree of evaluation factors, and stratigraphic thickness analysis based on 3D geological models. The 3D spatial analysis module is structured into several sub-modules, each addressing specific aspects of spatial analysis: (1)3D spatial statistical analysis sub-module: this sub-module employs mathematical statistical models to analyze and extract features of 3D geological bodies, enabling the identification of spatial patterns and trends.(2) 3D geological body surface analysis sub-module: focused on the morphological characteristics of geological body surfaces, this sub-module extracts and analyzes 3D surface information, such as slope, curvature, and aspect.(3)3D spatial distance analysis sub-module:This sub-module quantitatively characterizes the degree and scope of the influence of 3D geological information on the development of underground space resources, providing critical insights for resource planning and risk assessment. (4)3D spatial overlay analysis sub-module:designed to integrate diverse spatial data in various forms, this sub-module facilitates the combination of multiple data layers to create comprehensive spatial models.(5)3D spatial interpolation analysis sub-module:this sub-module interpolates discrete geological data to analyze and study its global spatial distribution, enabling the creation of continuous 3D models from sparse data points. Each functional module is further subdivided into specialized sub-modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 3D spatial statistical analysis module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D spatial statistical analysis module is designed to employ various statistical models for calculating comprehensive values of block models. This module addresses the need to analyze the statistical characteristics of geological bodies, with key application scenarios including the calculation and extraction of stratigraphic layer complexity, the thickness of unfavorable geological bodies, and the burial depth of confined aquifers. Within this system, the 3D spatial statistical analysis module is structured into three sub-modules, each tailored to specific analytical requirements: (1) Geological body complexity analysis sub-module: this sub-module addresses the mechanism by which the construction difficulty of UUS increases with the complexity of engineering geological formations, both in shallow and deep underground space. It provides an effective representation of the relative magnitude of geological complexity within a given region, enabling the identification of areas that may pose greater challenges for underground construction.(2)3D geological body thickness analysis sub-module:Designed specifically for unfavorable geological bodies in underground space, this sub-module operates on the principle that thicker geological bodies are more detrimental to the development and construction of underground spaces. It effectively characterizes the distribution features of geological body thickness, providing critical insights for risk assessment and mitigation strategies.(3)3D geological body depth analysis sub-module:This sub-module is used to statistically calculate the burial depth of target geological bodies in the vertical direction. It supports the quantitative analysis of geological features at varying depths, facilitating the identification of key geological constraints and opportunities for UUS development.\u003c/p\u003e\n\u003cp\u003eFor example, the Fig.3 demonstrates the calculation results of the total number of stratigraphic horizons along the depth direction within a specified depth range in a study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 3D geological surface analysis module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D geological surface analysis module is designed to address the need for analyzing the spatial distribution and morphological characteristics of geological bodies. Its primary purpose is to evaluate the spatial distribution and morphological features of geoscientific elements. Within this system, the module is structured into four sub-modules:\u003c/p\u003e\n\u003cp\u003e(1) 3D geological body surface extraction sub-module: this sub-module extracts surface units from the 3D geological body model, which is represented by cubic units. The extracted surface units can be utilized for further spatial analysis or directly incorporated as evaluation factors in comprehensive assessments. This capability is critical for understanding the external geometry of geological bodies and their spatial relationships.(2) 3D differential geological interface analysis sub-module:this sub-module identifies and extracts cubic units that represent the locations of differential\u0026nbsp;stratigraphic contact interfaces within a specified depth range. By pinpointing these interfaces, the function facilitates the analysis of stratigraphic boundaries and their spatial variability, which is essential for assessing geological heterogeneity.(3) 3D slope analysis sub-module:this sub-module quantitatively evaluates the degree of inclination of geological structural surfaces. It can be applied to topographic surfaces or other geological body surfaces, with user-defined thresholds to identify structures or locations that significantly impact underground space development. This analysis is particularly valuable for assessing slope stability and its implications for construction and resource development.(4) 3D relief analysis sub-module:this sub-module provides a quantitative analysis of the relief degree of geological body surfaces. By characterizing surface roughness and elevation variations, it offers insights into the topographic complexity of geological formations, which is crucial for evaluating their suitability for engineering projects.\u003c/p\u003e\n\u003cp\u003eFor example, the Fig.4 demonstrates the calculation results of extraction results of interface between upper soft and lower hard soil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.3 3D spatial distance analysis module\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3D Spatial distance analysis module is designed to quantify the extent to which geological bodies influence their surrounding geological environment through various analytical methods. This smodule serves as a critical tool for determining the influence distance of evaluation factors. It operates by calculating the Euclidean distance in 3D space between all discrete block units within the working area and a specified target block unit. Users can select different attribute variables to perform 3D Distance Field Analysis, enabling the derivation of distance values within a defined influence range of the geological body. This functionality is essential for assessing the spatial impact of geological features on their surroundings. Through this module, users can obtain Euclidean distance values associated with evaluation factors, facilitating a comprehensive assessment of the spatial influence of geological bodies (Fig.5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4 3D spatial overlay analysis module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D spatial overlay analysis module is designed to perform a series of set-based analyses and calculations on 3D geological body units within underground spaces. This function is particularly relevant in practical evaluations of underground spatial geological environments, where the analysis objects may include 3D cubic block models representing diverse geoscientific information or 3D solid models characterizing existing underground conditions.\u003c/p\u003e\n\u003cp\u003eThe primary objective of the 3D spatial overlay analysis function is to conduct spatial overlay analyses and calculations on 3D geological body units. The overlay objects can encompass geological solid models, cubic units representing various geoscientific attributes, or different spatial extents. Additionally, the overlay process supports a range of logical operations, including \u0026quot;AND,\u0026quot; \u0026quot;OR,\u0026quot; and \u0026quot;NOT,\u0026quot; enabling flexible and comprehensive spatial analyses.For example, the Fig.6 demonstrates the calculation results of 3D overlay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.5 3D spatial interpolation module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D spatial interpolation module is specifically designed to perform interpolation analysis and processing of discrete data derived from soil tests, rock mass tests, in-situ experiments, and other relevant sources. This module enables the analysis and study of the spatial distribution of such data, providing critical insights into the engineering properties of soil and rock masses within the area of interest. To address practical requirements, the module integrates four widely used interpolation methods: Inverse Distance Weighting (IDW), Ordinary Kriging, Simple Kriging, and Nearest Neighbor Interpolation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 3D comprehensive evaluation module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3D comprehensive evaluation module represents the final stage in the evaluation process, where the analyzed and extracted 3D evaluation factor information from other system modules undergoes data fusion to produce comprehensive evaluation results. This module is pivotal in enabling tasks such as geological suitability evaluation and development analysis for underground spaces. It consists of several key functional components, each designed to address specific aspects of the evaluation process: (1)Weight determination model: This model calculates the weights of evaluation factors within each indicator system, quantifying their relative importance in the context of UUS development. It incorporates both subjective and objective weight determination methods, including the Analytic Hierarchy Process (AHP) for subjective weighting and the Entropy weight method for objective weighting. This dual approach ensures a balanced and scientifically robust determination of factor significance.(2) Comprehensive evaluation model: this module calculates the suitability level of each cubic block unit based on the derived weights. It integrates a variety of evaluation methodologies, including index overlay method, set pair analysis, extenics Model and cloud model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Underground space resources estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderground space resources estimation constitutes an indispensable component of the comprehensive evaluation of the geological environment of underground spaces. This module not only enables the construction of 3D current status models, such as building pile foundation solid models, but also facilitates a comprehensive calculation and analysis of the volume of already developed underground space resources. This process enhances the understanding of the utilization status across various underground development horizons and depths, thereby better supporting planning and construction efforts. Users can sequentially execute the following steps\u0026mdash;line extension-based 3D modeling, 3D model value assignment, and underground space resource calculation\u0026mdash;to achieve applications including the construction of 3D current status solid models, discrete visualization of models with varying numerical attributes, and statistical analysis of underground space resources based on different parameters. (1) Line extension-based 3D modeling sub-module: this sub-module is designed for 2D contour line models with depth information, targeting application objects such as building pile foundations, ecological protection zones, main roads, and surface water systems. It constructs 3D current status models by integrating line models with their corresponding depth attributes. Taking a 2D line model of a building pile foundation as an example, the function generates a planar model for each imported linear pile foundation model. Utilizing the software\u0026rsquo;s line extension capability, each underground pile foundation is vertically stretched to its specified depth in compliance with relevant standards and specifications. The top and bottom surfaces of the pile foundation are then delineated, and the side surfaces are enclosed to form multiple independent and sealed 3D solid models. The entire process is fully automated, ensuring operational efficiency and precision. (2) 3D model value assignment sub-module: this sub-module is tailored for preconstructed 3D current status solid models, such as pile foundation models. It assigns user-defined numerical values to these models and discretizes them into 3D block models, laying the groundwork for subsequent resource estimation and analytical workflows. (3) Underground space resource estimation sub-module:this sub-module performs statistical calculations and analyses of the volume of developed underground space resources. Users can define target attributes and storage paths based on practical requirements, select parameters such as attribute values and depth ranges for computation, and export results in CSV format. The output effectively summarizes the resource volumes corresponding to all current condition attributes within the \u0026quot;underground space resource volume\u0026quot; category. The remaining available underground space resource volume is derived by subtracting the sub-item results from the total volume, providing a clear quantification of resource utilization and availability. For example, the Fig.7 demonstrates the calculation results of 3D solid model construction for underground pile foundations of buildings.\u003c/p\u003e"},{"header":"4. Case study","content":"\u003cp\u003eThe study area is located in the coastal area, covering approximately 3.2 square kilometers. The landform of the study area was formed through the combined effects of tectonic, erosional, denudational, and depositional processes, as well as internal and external geological forces, since the Cenozoic era. Its distribution is closely related to the geological structures along the route, with the main landform units being tectonic-erosional landforms, piedmont slope depositional landforms, and river erosion-depositional landforms. The terrain of the site fluctuates significantly, with an overall trend of being higher in the east and north, and lower in the west and south. The surface is covered with modern buildings and roads. As a key development and planning area within coastal area, the study area has great potential for future development. Therefore, the development and utilization of underground space in the study area will further meet the needs of future urban development. However, the geological structure of the study area is relatively complex, and the development and utilization of underground space are constrained by the quality of geological conditions. The ubiquitous fill layers, which are uneven in thickness and shallowly buried, exhibit high compressibility and low strength, and are prone to compressive deformation. During construction, slight subsidence of the ground may occur due to the consolidation and settlement of the fill itself. The weathered deep troughs formed by fully and strongly weathered granite can also adversely affect tunnel excavation, foundation stability, and uniformity. Currently, the development and utilization of underground space in the area mainly focuses on shallow layers (0-15 m).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on multi-dimensional and multi-source data, including borehole data, DEM (Digital Elevation Model), planar geological maps of the study area, the 3D engineering geological model was constructed with Geomodeller\u003csup\u003eTM\u003c/sup\u003e software. This model reveals the 3D spatial distribution of strata, rock masses, and geological structures within the study area (Fig.8).\u003c/p\u003e\n\u003cp\u003eThe development of underground space in the study area is significantly constrained by factors such as fill layers and completely weathered bedrock. Based on the data and information available in the study area, as well as the visualization and analysis capabilities of the 3D geological model and statistical results of different stratigraphic layers, a 3D evaluation index system for the geological suitability of UUS development in the area was constructed, considering four aspects: topography and landform, geotechnical engineering properties, hydrogeological conditions, and unfavorable geological conditions (Table 1). This was tailored to the actual geological background of the area, and multiple data analysis methods in the software were employed to extract the 3D evaluation index (Fig.9).\u003c/p\u003e\n\u003cp\u003eTable 1 3D evaluation index system and the extraction methods of the study area\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eEvaluation index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e3D spatial analysis method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eGround elevation（C\u003csub\u003e1\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eDepth\u0026nbsp;/\u0026nbsp;\u0026amp; Distance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eGeological structure complexity（C\u003csub\u003e2\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eComplexity\u0026nbsp;/\u0026nbsp;\u0026amp; Distance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eBedrock surface depth（C\u003csub\u003e3\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eDepth\u0026nbsp;/\u0026nbsp;\u0026amp; Distance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eDistance to confined aquifer（C\u003csub\u003e4\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eDistance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eArtificial fill thickness（C\u003csub\u003e5\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eThickness\u0026nbsp;\u0026amp; Distance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eCompletely weathered layer thickness（C\u003csub\u003e6\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eThickness analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eDistance to fault（C\u003csub\u003e7\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eDistance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3D geological model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eBuilding pile foundation（C\u003csub\u003e8\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eOverlay \u0026amp; Distance analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003ePlanning data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe subjective and objective weights were calculated using the Analytic Network Process, ANP (Satty 1996, 2004) within the game theory (Zhu et al. 2013) combined weight model and an improved CRITIC method ( Liu et al 2021) (Table.2). The optimal weight values were then determined by integrating the subjective and objective weights based on game theory. The calculated weight coefficients for the combined weights were 0.7 and 0.3, respectively. These coefficients were incorporated into game theory to derive the final combined weights, as presented in Table 2.\u003c/p\u003e\n\u003cp\u003eBased on the extracted 3D evaluation index information and evaluation index combined weight, this study employed a multi-level index superposition method to integrate multidisciplinary geological data, enabling the quantitative calculation of 3D geological suitability across the study area. Subsequent analysis focused on the shallow subsurface layer (0\u0026ndash;15 m depth), with evaluation results for geological suitability illustrated in Fig.10.\u003c/p\u003e\n\u003cp\u003eTable 2 Combination weight of 3D evaluation indexes of the study area\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluation index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAHP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImproved CRITIC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGame theory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGround elevation（C\u003csub\u003e1\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeological structure complexity（C\u003csub\u003e2\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.113\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBedrock surface depth（C\u003csub\u003e3\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.101\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDistance to confined aquifer（C\u003csub\u003e4\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eArtificial fill thickness（C\u003csub\u003e5\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCompletely weathered layer thickness（C\u003csub\u003e6\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.125\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDistance to fault（C\u003csub\u003e7\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.167\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBuilding pile foundation（C\u003csub\u003e8\u003c/sub\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.213\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe shallow layer ranges from the surface to a depth of 0-15 meters. According to the statistical results, approximately 21.62% of the total volume in the shallow layer is already constrained or developed, with the remaining underground space resource capacity accounting for about 78.38%. Among them, Class I accounts for 7.03% of the total volume, Class II accounts for 65.42%, Class III accounts for 5.32%, and Class IV accounts for 0.61%. The 3D comprehensive evaluation results (Fig.10) indicate that the development of underground space in this layer is mainly affected by the influence range of faulted structural zones and the thickness of artificial fill. As the depth increases, the overall evaluation results of underground space change little. Among them, Classes III and IV areas are mainly affected by the superposition of nearby faulted structural zones and thicker artificial fill, concentrated in the southwest of the working area and mainly distributed along the spatial morphology of the faulted structural zones. Due to the poor quality of underground space resources in these areas, the UUS development is relatively unfavorable, requiring more costs. Moreover, when conducting underground space engineering construction, special attention should be paid to a series of engineering problems caused by faulted zones. In the remaining areas of the working zone, the shallow layer has good conditions for development and utilization, and is generally suitable for the UUS development. It can be used as a key area for shallow layer utilization in underground space development. However, attention should also be paid to thicker fill layers in some local areas.\u003c/p\u003e\n\u003cp\u003eThe above results fully demonstrate that the 3D evaluation system for UGEE development designed and developed in this paper can be well applied to the quantitative evaluation of geological suitability for underground space development. The evaluation results obtained in this paper have 3D attributes, which facilitate the convenient acquisition of comprehensive geological suitability evaluation results for any spatial position and interval (as shown in Fig.10), allowing for better viewing and analysis of the variation characteristics of geological suitability within underground space. Furthermore, the 3D spatial analysis module makes it easier to mine 3D evaluation information for various evaluation index. Therefore, compared with traditional 2D planar evaluation results, it has better accuracy and practicability, and its presentation is more intuitive. It is suitable for the 3D planning and evaluation of UUS, and can effectively provide detailed evaluation results and basis for underground space planning, saving development costs and reducing development risks.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn response to the practical demands for quantitative evaluation methods in 3D UGEE, this study developed a\u0026nbsp;software system based on the Surpac\u003csup\u003eTM\u003c/sup\u003e, capable of conducting comprehensive 3D UGEE within three dimension. The system integrates specialized functional modules, including multidimensional data conversion, 3D spatial statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, encompassing the entire workflow of 3D UGEE. Compared to conventional 2D evaluation software system, this system achieves seamless integration of 3D geological information technology and quantitative suitability evaluation methodologies. Its innovative 3D spatial analysis algorithms and multidimensional evaluation framework enable advanced data mining and suitability assessments for underground space development, leveraging large-scale geoscientific datasets. This approach effectively addresses limitations inherent in traditional 2D evaluations, such as low depth resolution and the loss of critical 3D spatial information.\u003c/p\u003e\n\u003cp\u003eThe system\u0026rsquo;s efficacy was validated through a case study on UUS geological suitability evaluation in coastal area, China. Results demonstrated its robust practicality and adaptability, significantly enhancing the depth resolution of evaluation outcomes while enabling in-depth extraction of 3D evaluation factors. The software provides a scientifically grounded methodology for detailed underground space planning and utilization, offering actionable insights for urban development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author Meijun Xu on reasonable request via e-mail [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e Conceptualization: D. F. F and X.M.J.; Methodology: G. Y and Z.H.; Formal analysis and investigation: L.L.; Writing - original draft preparation: L.Y.M.; Writing - review and editing: Y.B.K.; Funding acquisition: D.F.F. and G. Y.; Resources: Y. P. and G. Y.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e This study was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China, grant number 23KJD170001, the Open project of Key Laboratory of Geological Safety of Coastal Urban Underground Space, Ministry of Natural Resources, grant number BHKF2023Y03 and the Project of Research on Karst Exploration and Risk Prevention in Typical Areas of Huzhou, grant number 2024ZJDZ023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests \u0026nbsp;The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBroere,W., Urban underground space: solving the problems of today\u0026rsquo;s cities[J]. Tunnelling and Underground Space Technology, 2016, 55: 245-248\u003c/li\u003e\n \u003cli\u003eBrown, K., \u0026amp; Wilson, R. (2018). Limitations of SCL scripting in Surpac for advanced geospatial analysis. Computers \u0026amp; Geosciences, 111, 182-190.\u003c/li\u003e\n \u003cli\u003eDou F.F., Xing H.X., Li X.H., Y F., Lu Z.T., Li X.L. 3D geological suitability evaluation for urban underground space development based on combined weighting and improved TOPSIS[J]. Natural Resources Research, 2022, 31: 693\u0026ndash;711.\u003c/li\u003e\n \u003cli\u003eDou, F.F., Li, X.H., Xing, H.X. (2021). 3D geological suitability evaluation for urban underground space development\u0026ndash;A case study of Qianjiang Newtown in Hangzhou, Eastern China. Tunnelling and Underground Space Technology, 115, 104052.\u003c/li\u003e\n \u003cli\u003eFang, Y.C., Gong, R.X,. Li, S.F., Pan, S.Y., Gu, M.G,. Huang, W.P (2017) Suitability evaluation of underground space development based on a three-dimensionalgeological model, using the Jiaxing urban geological survey as an example. Shanghai Land \u0026amp; Resources. 38, 43-45.\u003c/li\u003e\n \u003cli\u003eHou W.S., Yang L., Deng D.C., YL., Keith Clarke., YZ.J (2016) Assessing quality of urban underground spaces by coupling 3D geological models: The case study of Foshan city, South China. Computers \u0026amp; Geosciences, 89:1-11.\u003c/li\u003e\n \u003cli\u003eHu Xuexiang ,Liu ganbin Tao Haibin.Research on evaluation suitability for the development of underground space in Ningbo city based on ArcGIS. Chinese Journal of Underground Space and Engineering, 2016, 12(6)1439-1444.\u003c/li\u003e\n \u003cli\u003eLi, M., \u0026amp; Wu, J. (2021). Efficiency optimization in GIS-based mining software development. Transactions of Nonferrous Metals Society of China, 31(2), 401-410.\u003c/li\u003e\n \u003cli\u003eLiu Y., Hu Y., Hu Y.., Hu Y.M., Cao Y.Q., Liu Z.Y (2021) Water quality characteristics and assessment of Yongding New River by improved comprehensive water quality identification index based on game theory[J]. Journal of environmental sciences, 104: 40-52.\u003c/li\u003e\n \u003cli\u003eLu, Z., Wu, L., Zhuang, X., Rabczuk, T. (2016). Quantitative assessment of engineering geological suitability for multilayer Urban Underground Space. Tunnelling and Underground Space Technology, 59, 65\u0026ndash;76.\u003c/li\u003e\n \u003cli\u003ePeng J., Peng F.L (2018) A GIS-based evaluation method of underground space resources for urban spatial planning: Part 2 application. Tunnelling and Underground Space Technology, 77: 142\u0026ndash;165.\u003c/li\u003e\n \u003cli\u003eSaaty, T. L (2004) Fundamentals of the analytic network process\u0026mdash;Dependence and feedback in decision-making with a single network. Journal of Systems science and Systems engineering, 13, 129-157.\u003c/li\u003e\n \u003cli\u003eSaaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh: RWS publications.\u003c/li\u003e\n \u003cli\u003eSmith, A., Chen, L., \u0026amp; Zhang, Q. (2019). Integrated secondary development frameworks for geospatial software. International Journal of Mining Science and Technology, 29(4), 579-586.\u003c/li\u003e\n \u003cli\u003eTan, F., Wang, J., Jiao, Y. Y., M, B. C., He, L.L. (2021). Suitability evaluation of underground space based on finite interval cloud model and genetic algorithm combination weighting. Tunnelling and Underground Space Technology, 108, 103743.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang, Y., \u0026amp; Liu, X. (2020). Comparative analysis of GIS-based development modes in mining applications. Journal of Geospatial Engineering, 12(3), 45-62.\u003c/li\u003e\n \u003cli\u003eWu Lixin, Jiang Yun,Che Defu, et al. Fuzzy synthesis evaluation and 3D visualization for resource quality of urban underground space. Journal of China University of Mining Technology, 2007, 36( 1) : 97-102\u003c/li\u003e\n \u003cli\u003eWu, Q., \u0026amp; Zhao, L. (2020). Hybrid development strategies for 3D geovisualization tools. ISPRS International Journal of Geo-Information, 9(7), 432.\u003c/li\u003e\n \u003cli\u003eXi Yue, Zhang Wanbin, Xu Wenyun Xu Ben Liu Baolin.Safety risk Evaluation of Underground Space Development in Yuzhong Peninsula of Chongging.Chinese Journal of Underground Space and Engineering. 2022，18(2)：359-365.\u003c/li\u003e\n \u003cli\u003eXue Tao, Shi Yujin，Zhu Xiaodi, et al. Research on 3D modeling method for evaluation of urban underground space resources: A case study in Shanghai. Earth Science Frontiers，\u0026nbsp;2021，\u0026nbsp;28(4): 373- 382\u003c/li\u003e\n \u003cli\u003eZhu, Y., Li, L (2014) A regional competitive water resources security evaluation model based on Nash equilibrium restrictions. Water Policy, 16(4), 690-703.\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":"underground space, 3D geological environment evaluation, quantitative, software system","lastPublishedDoi":"10.21203/rs.3.rs-6518793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. Addressing the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimension using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, enabling the integration and analytical assessment of multivariate geoscientific data. This study elaborates on the system\u0026rsquo;s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Hangzhou demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE.\u003c/p\u003e","manuscriptTitle":"Design and implementation of 3D geological suitability evaluation system for underground space development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 06:21:47","doi":"10.21203/rs.3.rs-6518793/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":"1dd3fe54-7144-45a8-ba8c-86a1e35b23d3","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49141987,"name":"Earth and environmental sciences/Environmental sciences"},{"id":49141988,"name":"Earth and environmental sciences/Natural hazards"},{"id":49141989,"name":"Earth and environmental sciences/Solid earth sciences"},{"id":49141990,"name":"Physical sciences/Energy science and technology"},{"id":49141991,"name":"Physical sciences/Engineering"},{"id":49141992,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-09-01T07:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 06:21:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6518793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518793","identity":"rs-6518793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}