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Article

Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors

by
Yang Yang
1,2,3,4,
Rui Wang
3,4,
Dixu Liu
1,2,5,
Lixin Wu
1,2,* and
Jingwen Su
3,4
1
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2
Laboratory of GeoHazards Perception, Cognition, and Predication, Central South University, Changsha 410083, China
3
Nanjing Center, China Geological Survey, Nanjing 210016, China
4
Engineering Innovation Center for Urban Underground Space Exploration and Evaluation, Ministry of Natural Resources of the People’s Republic of China, Nanjing 210016, China
5
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(10), 4046; https://doi.org/10.3390/app14104046
Submission received: 22 March 2024 / Revised: 3 May 2024 / Accepted: 7 May 2024 / Published: 10 May 2024
Browse Figures
Versions Notes

Abstract

:
With the rapid advancement of urbanization, the development and utilization of urban underground space resource (UUSR) has become one of the dominant features. However, in certain areas, the development of UUSR may cause disasters and accidents, such as ground collapse, settlements, and tunnel water gushing. Geological environmental factors (GEFs) are recognized as the fundamental constraining factor of UUSR development. In this paper, quality based on GEFs is defined to assess the development difficulty degree of UUSR. A 3D assessment framework is proposed based on 3D geological modelling and the interval continuous mathematical model (ICMM). The subjective and objective joint weight method of analytic hierarchy process and entropy weight method (AHP–EWM) is utilized to determine the weight of each indicator. The quality index (QI) of each spatial node of the 3D geological model is calculated by the ICMM mathematical model. A case study conducted in the Jiangbei New District of Nanjing, China, serves as a demonstration of the UUSR assessment. The results clearly illustrate the 3D distribution characteristics of the quality in the study area, offering valuable insights for future 3D urban underground space planning.

1. Introduction

Urban underground space resource (UUSR) is deemed as the second land resource for urban development. UUSR development is significant in achieving sustainable and resilient urban development. It serves various purposes, including mitigating the shortage of land resources, easing traffic congestion, reducing carbon emissions, and revitalizing surface ecological environments [1,2,3,4,5]. However, in certain areas, the development of UUSR may lead to disasters and accidents, such as ground collapse [6], settlements [7], and tunnel water gushing [8]. UUSR is normally developed in a certain geological body subsurface. The poor geological conditions are considered to be the fundamental constraining factor of underground space [9]. Neglecting the effect of UUSR development on the geological structure, groundwater flow, and stress field will cause an irreversible and unrecoverable risk to the city. Wu et al. defined UUSR quality as the difficulty degree of UUSR development due to the influence and restriction of various geological environmental factors (GEFs) under certain technical conditions [10]. Assessing the UUSR quality based on GEFs holds paramount significance for facilitating scientific urban underground space planning and preparing for construction activities.
An escalating number of scholars have acknowledged and undertaken systematic scientific assessments of UUSR, including suitability assessment [11,12,13,14], potential assessment [15,16], and quality assessment [17,18]. Current UUSR assessment mainly depends on the 2D-based fuzzy mathematical model [19,20], 3D GIS spatial data management, multi-factor overlay analysis, and 3D geological modelling technology [18,21,22]. The assessment indexes primarily comprise topographic features, engineering geological factors, hydrogeological characteristics, geological hazards, site conditions, and economic considerations. Topographic features will affect the connectivity between underground and above-ground spaces, as well as the excavation methods of underground spaces. Engineering geological factors include geological structures and soil and rock properties, which mainly affect the regional stability of underground spaces and the safety of engineering structures. Hydrogeological characteristics are mainly manifested in the depth of groundwater, types of water-bearing rock formations, and water abundance, and have an impact on the underground engineering construction. Geological hazards such as land subsidence and sand liquefaction can increase the difficulty of the underground engineering construction and pose safety hazards. Furthermore, site conditions, i.e., ground restrictions such as surface buildings, roads, squares, green areas, and reserve areas were considered in the assessment of urban underground space resources in Chongqing [23]. Economic considerations including population density, GDP per capita and ground price were considered in the suitability evaluation of underground space development in Wuhan [24]. The weights assigned to these assessment indicators are predominantly determined using the subjective analytic hierarchy process (AHP) using expert opinion [25,26,27], the objective entropy weight method (EWM) using the degree of dispersion of data in the indicators [18,28,29], and the combined weights method that considers subjectivity and objectivity [30], etc. Furthermore, the multi-objective linear weighting method [25], fuzzy comprehensive evaluation method [31], and multi-level grey evaluation method [32] are mainly utilized as the mathematical models for UUSR assessment. The multi-objective linear weighting method involves quantifying the indicators and linearly summing up the weights of the indicators. The fuzzy comprehensive evaluation method determines the membership degree of each evaluation indicator relative to each quality level through a membership function, and then determines the quality level through a fuzzy operator operation based on the maximum membership principle. The grey evaluation method determines the evaluation sample matrix and evaluation grey class, obtains the grey evaluation weight vector, and calculates the comprehensive evaluation value of the development potential of each evaluation object and sorts it. The above UUSR assessment, especially the 3D assessment, provides an overall visual view of the UUSR on the whole city scale.
In recent years, there has been a significant surge in the intensive development of UUSR in new development areas, particularly in the Yangtze River Alluvial Plain region. This trend has generated a growing demand for assessing UUSR quality in specific smaller scale areas with high resolution and precision. Some scholars have directed their focus towards conducting UUSR assessments for such relatively small areas [33,34,35,36]. Several novel weight assignment techniques have emerged, including the integration of rough set and conditional entropy, variable-weight methodology, and the method of combining analytic network process and criteria importance through intercriteria correlation. Additionally, various mathematical models such as the AHP-cloud model and improved TOPSIS model have been introduced. However, the assessment indicators still use the traditional regional indicators. The regional indicators will weaken the effect of some key indicators’ variation in the smaller scale area. Furthermore, the rating criteria of assessment indicators and UUSR quality classification may produce step-like features, resulting in the inability to continuously express UUSR quality in a 3D space. These limitations would result in the low resolution of the 3D distribution of UUSR quality. Consequently, the UUSR assessment continues to face two main shortcomings, namely, the absence of a tailored assessment index system for a small-scale area, and the challenge in developing a mathematical model that can continuously represent the UUSR result in a three-dimensional space.
In view of this, this research aims to develop a 3D assessment framework to assess the UUSR quality for shield tunnel construction in a relatively small-scale area covered by thick soil layers. To resolve the first shortcoming, specific indicators of GEFs tailored to suit the characteristics of a small-scale area were selected based on the knowledge of shield tunnel design. To address the second shortcoming, the interval continuous mathematical model (ICMM) was utilized to achieve the high resolution spatially continuous expression of UUSR quality. Furthermore, 3D geological modelling and the AHP-EWM weighting method were adopted to conduct the 3D assessment process. Finally, a quantitative assessment at the Jiangbei New District of Nanjing, China, expounds the case study.

2. Methodology for the 3D Quality Assessment of UUSR

The 3D quality assessment presented in this paper aims to depict the relative difficulty degree of developing UUSR in a three-dimensional space. The framework of the 3D quality assessment of UUSR comprises the following steps (Figure 1): (1) the assessment indicators are selected and integrated to build a 3D geological model using Earth Volumetric Studio software 2020.12 (EVS); (2) a subjective and objective joint weight method of AHP–EWM is utilized to determine the weights of assessment indicators; (3) the quality index (QI) of each spatial node inside the 3D geological model is calculated by the ICMM mathematical model; and (4) the 3D visualization of the assessment results is achieved based on the 3D geological model.

2.1. Assessment Indicators

2.1.1. Indicators Selection

Unlike traditional assessment indicators for large-scale areas, some GEFs may exhibit high similarity in small-scale areas, such as the earthquake intensity, groundwater type and pressure head, etc. However, to meet high-resolution demands, the geotechnical properties, groundwater quantity, and negative geological body are typically considered to vary both horizontally and vertically in areas covered by thick soil layers. Based on a comprehensive analysis of previous research findings [37,38,39], the first-level indexes of geotechnical parameter G, hydrogeological parameter H, and spatial distance to negative geological layers S are mainly considered. Furthermore, the secondary indexes are proposed based on the knowledge of five designers with extensive experience in shield tunnel design, as shown in Table 1.
Geotechnical parameter G is one of the dominant parameters to design and construct a tunnel. Among the indicators, the intensity parameters (cohesion c and internal friction angle φ) and permeability parameter (hydraulic conductivity k) are selected to be the key indicators. The higher the intensity of the soil, which means a larger c and φ, the easier it is to maintain tunnel stability. The lower the permeability of the soil, which means a smaller k, the easier it is to prevent tunnel water gushing. So, under certain conditions of other factors, the greater the intensity of the soil, the lower the permeability, and the higher the UUSR quality.
For hydrogeological parameter H in a specific construction area, the yield of single well (YSW) q, which refers to the output ability of groundwater, is a significant parameter to design the dewatering scheme during tunnel construction. The larger the q, the more difficult it is to dewater during the tunnel construction, and the lower the UUSR quality.
In addition to the traditional parameters mentioned above, the spatial distance parameter S is also considered to have a strong influence on the difficulty degree of shield tunnel construction. The depth h, and the distance ds and dg to the soft soil layer and gravel layer, which are the main negative geological layers in specific areas covered by thick soil layers, are proposed. The deeper and closer to the negative geological layers, the lower the UUSR quality.

2.1.2. Data Integration

To comprehensively integrate the assessment indicators proposed above, the data of GEFs from engineering geological surveys, hydrogeological surveys, and geotechnical investigations in the study area should be collected. The indicators can then be extracted based on the assignation methods and data sources, as detailed in Table 2.
Geotechnical parameters are mainly extracted from soil test reports of engineering geological surveys and geotechnical investigations, which can be obtained from the local geological and construction archives. The intensity parameters c and φ can be obtained from the data in soil direct shear tests conducted at different depths, and the permeability parameter k can be obtained using data from soil permeability tests conducted at different depths. The hydrogeological parameter is mainly extracted from the hydrogeology map, which should be a vector map with a scale greater than 1/50,000. The indicator q is assigned to the aquifer (sandy and gravel layers) with the zoning grade based on the value of YSW. For aquitard, the indicator q is assumed to be 0. Spatial distance parameters can be calculated based on the 3D geological model.

2.2. 3D Geological Modelling

In recent decades, the utilization of geological modelling has experienced significant growth, particularly in the presentation of 3D geological structures and the UUSR assessment. This has been made possible through the employment of various software solutions, such as Geomodeller [34,35], 3D-Mine [36], GOCAD [40], and EVS [41]. Among them, EVS is an advanced visualization and analysis software developed by C Tech for the field of earth sciences. Its open data architecture, continued 3D visualization capabilities, and user-friendly interface make it a go-to solution for urban areas with thick Quaternary sediment. Therefore, EVS [42] is employed in this paper to create 3D geological models attributed with the data of assessment indicators. The creation process involves two primary steps: geological structure modelling and the spatial interpolation of indicators.

2.2.1. Geological Structure Modelling

The EVS software offers support for two distinct methods: stratigraphic modelling and lithology modelling. In this paper, the former approach is employed due to the clear advantages in regions where Quaternary sedimentary conditions are well-defined, sediment layering is well-developed, and complex structural features are absent.
Stratigraphic modelling involves the process of partitioning drilling data into stratigraphic sequences, resulting in a data format that encompasses the stratigraphic layering. Firstly, the establishment of the standard stratigraphic sequence is carried out by considering factors such as the soil sedimentary environment, lithologies, and soil conditions, based on the findings of the engineering geological survey and geotechnical investigation. The stratigraphic sequences in each borehole are determined by identifying the 3D surfaces associated with the boundaries between different lithologies. These surfaces are then utilized to create stratigraphic interfaces. Finally, a 3D geological model is generated using the Kriging interpolation method, which is the most fundamental geological statistical method employed in EVS.

2.2.2. Spatial Interpolation of Indicators

Kriging interpolation is widely used in geological and geotechnical assessments considering both the distance and degree of variation between known data points when estimating values in unknown areas [43,44]. In order to create the 3D geological model attributed with the data of geotechnical indicators c, φ, k, the geostatistical method (Kriging) is employed to interpolate geotechnical test data onto the 3D geological structure model. Considering the significant differences in engineering properties between cohesive soil and sandy soil, it is not appropriate to perform a unified interpolation of their geotechnical parameters. Therefore, for cohesive and sandy soils, the lower boundary interfaces are divided into upper and lower parts, and attribute interpolation calculations are conducted independently for each part.
For hydrogeological indicator q, a spatially discontinuous variable parameter, is assigned by a zoning value based on the YSW, considering the spatial extent and geological constraints. The zoning value can be divided into several grades according to the specific YSW. Each node inside the 3D geological model is assigned by its zoning grade in an aquifer and 0 in an aquitard.
Regarding the spatial distance indicators ds and dg, taking the example of ds, if a node inside the model is located above the soft soil layer, the ds would be the vertical distance from that point to the soft soil layer. If the node is inside the soft soil layer, the ds would be 0. If the node is located below the soft soil layer, the ds could be assigned a value of 999, indicating that the presence of the upper soft soil layer has no influence on the tunnel for this node.
Finally, each node inside the geological model is associated with a dataset of indicators, including c, φ, k, q, h, dg, and ds.

2.3. AHP–EWM Weight Method

The weighting of indicators plays a crucial role in the UUSR assessment, and it is generally divided into subjective and objective weights. AHP is one of the most representative subjective weight methods and is widely used in the UUSR assessment. It is a multicriteria decision-making method introduced by Saaty [45]. The index weights are determined by the judgment matrix, which can be determined using the expert scoring method. EWM is more and more widely used due to its objectivity based on data, especially for sensitive data [46]. It is a commonly used weighting method that measures value dispersion in the decision making proposed by Shannon [47]. In each dataset associated with different indicators, a higher degree of dispersion signifies a greater degree of differentiation and yields more information. Consequently, the weight assigned to this particular dataset increases within the entire dataset.
In this study, the AHP for calculating subjective weights and the EWM for calculating objective weights are jointly employed, as shown in Figure 2. Firstly, the assessment indexes are divided into two levels. The AHP method is utilized to calculate the weight of the qualitive first-level indexes (wG, wH, wS). Then, the EWM method is utilized to calculate the weight of quantitative geotechnical parameters G (wc1, wφ1, wk1) due to its sensitivity to spatial variation. Meanwhile, the AHP method is utilized to determine the weight of qualitive spatial variables S (wh1, wdg1, wds1). Finally, the weight of each indicator is calculated by multiplying the weight of first-level and secondary indexes, i.e., w = (wG × wc1, wG × wφ1, wG × wk1, wH, wS × wh1, wS × wdg1, wS × wds1).

2.4. Interval Continuous Mathematical Model

The interval continuous mathematical model (ICMM) was proposed by Wu et al. [48] to evaluate the rock mass quality of slopes with the continuous expression of quality by removing the step-like features caused by the pre-set rating criteria. Considering its advantage in realizing the continuous expression of quality, the ICMM mathematical model is employed in this paper to calculate the quality index (QI) of each node (i) inside the 3D geological model (n nodes in total), as shown in Equation (1):
Q I = ∑ i = 1 n w i ( V q ) i
where i = 1, 2, ⋯, n; w i is the weight of the indicators of node i, which can be calculated by the AHP-EWM weight method; and ( V q ) i refers to the normalized indicators of node i, which can be calculated by Equation (2) for positive indicators and Equation (3) for negative indicators as follows:
( V q ) i = V i − V m i n V m a x − V m i n
( V q ) i = 1 − V i − V m i n V m a x − V m i n
where V i is the value of indicators of node i, and V m a x and V m i n are the maximum and minimum values of the indicators of node i.
Based on the calculation progress described above, the final assessment value of QI is expected to fall between 0 and 1. This signifies that the ICMM facilitates the continuous expression of UUSR assessment results.

3. Case Study

3.1. Study Area

Nanjing Jiangbei New District holds significant importance as a key junction connecting the state-level New District, the Yangtze River Economic Belt, and the eastern coastal economic belt. It serves as an extension and expansion of Nanjing’s primary urban functions in the Jiangbei area. In the central area of Jiangbei New District, the planned construction of underground space encompasses a total area ranging from 4.5 to 4.8 million square meters. This extensive area includes the development of three planned subways as well as multiple underground highway tunnels. The presence of these ambitious projects clearly highlights the significant demand for underground space development in the future. Therefore, this central area with 34.5 km2 is selected in this study, as shown in Figure 3.
The study area is located in the Yangtze River floodplain. Engineering geological survey, hydrological geological survey, environmental geological survey and a large number of geotechnical investigations have been carried out at different stages of urban construction. The relevant reports used in this study were provided by Nanjing Center, China Geological Survey and Jiangsu Bureau of Geology and Mineral Exploration, as shown in Table 3. The study area is mainly composed of thick layered Quaternary sediments, including clay, sand and gravel, and the sandstone beneath the soils. Furthermore, complex structural features such as active faults and ground fissures are absent in this area. In order to create the 3D geological model, a collection of boreholes and virtual boreholes, which are created to accurately determine the interface between rock and soil horizontally, was integrated, as shown in Figure 3.

3.2. Spatial Distribution of Indicators Based on the 3D Geological Model

3.2.1. 3D Geological Structure Model

Based on the findings of the engineering geological survey and geotechnical investigation from the collected reports, 10 sequences of the study area are established. The collected drilling data are partitioned into stratigraphic sequences. Furthermore, the virtual drilling data are created based on the geological map to control the horizontal interface between rock and soil. The 3D geological structure model is built based on the 3D Kriging interpolation method within the EVS software, as shown in Figure 4.

3.2.2. Spatial Distribution of Assessment Indicators

The geotechnical test dataset is integrated from the collected geotechnical investigation reports to create the 3D geological model attributed with the data of geotechnical indicators c, φ, k. The Kriging method is employed to interpolate the data onto the 3D geological structure model. For hydrogeological indicator q, it is assigned the zoning grades based on YSW in the study area, as shown in Figure 5. Thus, each node inside the 3D geological model is assigned by its zoning grade in the aquifer (sandy and gravel layers) and 0 in the aquitard (clayey layers). Soft soil and gravel with coarse sands are the main negative geological layers in the study area. The spatial distance indicators ds and dg are calculated based on the 3D geological structure model.
Finally, each node inside the 3D geological model is associated with the normalized value of indicators calculated by Equations (2) and (3), as shown in Figure 6.

3.3. UUSR Quality Assessment Result

3.3.1. Weight of Indicators

The AHP-EWM joint weight method is employed to determine the weights of the assessment indicators. Firstly, the weight of first-level indexes wG, wH, and wS is calculated based on the AHP method and knowledge from five designers with extensive experience in shield tunnel design. The weight vector of (wG, wH, wS) is (0.3196, 0.1220, 0.5584). Then, the EWM method is utilized to calculate the weight of c, φ, and k based on the data from the 3D geological model. The calculation results of the weight vector of (wc1, wφ1, wk1) are (0.4308, 0.3653, 0.2039). The AHP method is utilized to calculate the weight of h, dg, and ds based on the data from the 3D geological model. The calculation results of the weight vector of (wh1, wdg1, wds1) are (0.5396, 0.2970, 0.1634). Finally, the weight of each indicator is calculated by multiplying the weight of first-level and secondary indexes, as shown in Table 4.

3.3.2. Assessment Result

The UUSR quality assessment is carried out for the underground soils based on the calculated weights and the ICMM mathematical model. The QI value of each node inside the 3D geological model is computed, and its spatial distribution is visualized with the 3D geological model and profile grid, as shown in Figure 7. Numerical statistics of assessment results show that QI distribution ranges from 0.28 to 0.85, with a median of 0.54, as shown in Figure 8.

4. Discussion

From the assessment result, it is evident that the relatively high-quality space, characterized by higher QI values, is situated at depths ranging from 10 to 30 m in most areas, as shown in Figure 7. This space is composed of soil layers including silty sand (③), silty clay (④), and silty sand (⑤). Conversely, the low-quality space, exhibiting lower QI values, is found at depths of 5 to 10 m and below 60 m in the area with soil layers consisting of soft clay (②) and gravel with coarse sands (⑦, ⑨), which are the main negative soil layers for tunnel construction in the study area. Particularly in some areas, the soft soil (②) and its contact zone with silty sand (③) at the depth of 20 to 30 m contributes the lowest QI values. In our previous study [36], we conducted the 3D assessment of UUSR quality based on the traditional regional GEF indicators and voxel-based geological model. The assessment results showed that QI distribution ranges from 0.28 to 0.85, which is similar to this study. However, the QI distribution in the previous study exhibited a dispersed bimodal pattern, contrasting with the more concentrated unimodal pattern observed in this study. This variance may stem from the previous study’s inclusion of additional indicators, such as water content, pore ratio, and characteristic value of bearing capacity.
In order to apply the assessment result to support the design of tunnel construction, a planned subway section in the study area (Figure 9) was analyzed. The quality profile along the planned route was extracted from the 3D assessment result, as shown in Figure 10. There is a low-quality area with the QI value of 0.28–0.37, situated within a depth range of 20–30 m in the middle of the section. The strata at this depth are mainly composed of soft soil (②) and its contact zone with silt sand (③), according to the geological profile. Particularly, the upper thick, soft soil and the lower silt sand form a hard–soft heterogeneous formation, which is unfavorable for shield tunneling construction.
In this study, the assessment indicators selected focused on the geotechnical parameters (G), hydrogeological parameters (H), and 3D spatial position parameters (S) for shield tunnel construction in the area covered by thick soil layers. Considering the geological condition of the study area, the geotechnical parameters of c, φ, and k were selected. Thus, the framework proposed is only suitable for regions characterized by thick loose layers and may not be applicable to bedrock mountainous areas. Furthermore, the existing surface and subsurface facilities in urban areas that may influence the difficulty degree of UUSR development were not taken into consideration. Some cities with rich historical heritage often have numerous protected historical or archaeological sites. Development underneath such areas may be prohibited in order to preserve their integrity. Furthermore, the assessment result is the QI value of each independent node in the 3D model, which represents the relative quality of UUSR without any classification. It is more accurate to describe the 3D quality of UUSR, but less user-friendly for underground space planning applications. In future endeavors, it is crucial to incorporate the influence of existing surface and subsurface infrastructures, as well as the historical or archaeological significance, as pivotal indicators in forthcoming 3D assessments. Moreover, quality classification should be carried out based on the assessment results to be better applied in the urban planning area.

5. Conclusions

This paper proposed a comprehensive framework to assess the UUSR quality for shield tunnel construction in a relatively small-scale area covered by thick soil layers. The assessment index system incorporated a total of seven indicators based on the knowledge of designers with extensive experience in tunnel design, and can reflect the development difficulty well. A 3D geological model attributed with the data of assessment indicators was created based on geotechnical test data and the geostatistical method (Kriging). The AHP-EWM weighting method adopted can process both subjective and objective indicators. The continuous characteristics of the ICMM model successfully achieved the high-resolution 3D visualization of assessment results.
The results of the demonstration example in the Jiangbei New District of Nanjing, China, revealed a range of QI values from 0.28 to 0.85. The relatively high-quality space is located at depths ranging from 10 to 30 m in most areas, while the low-quality space is found at depths of 5 to 10 m and below 60 m. Particularly in some areas, the soft soil (②) and its contact zone with silty sand (③) at a depth of 20 to 30 m contributes to the lowest QI values. Furthermore, the application to a planned subway section shows that a low-quality area within a depth range of 20–30 m is mainly composed of soft soil and its contact zone with silt sand, which is unfavorable for shield tunneling construction.

Author Contributions

Conceptualization, Y.Y. and L.W.; methodology, Y.Y. and D.L.; software, R.W.; validation, Y.Y., D.L., R.W., L.W. and J.S.; formal analysis, Y.Y.; investigation, Y.Y. and R.W.; resources, L.W.; data curation, Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, L.W.; visualization, R.W.; supervision, L.W. and J.S.; project administration, L.W.; funding acquisition, Y.Y. and L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 42071256, and the Scientific Research Project of Jiangsu Bureau of Geology and Mineral Exploration, grant number 2019KY01.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the conclusions of this article will be made available upon request and with the permission of the Scientific Research Project of Jiangsu Bureau of Geology and Mineral Exploration.

Acknowledgments

The authors would like to thank Senior Engineer Yongqiang Yang from Shanghai Municipal Engineering Design Institute, Senior Engineer Yang Gu from China Coal Technology and Engineering Group Nanjing Design and Research Institute, Engineer Rui Huang from Shanghai Tunnel Engineering Co., Ltd., Senior Engineer Liuyuan Zhao from PowerChina Huadong Engineering Co., Ltd., and Ao Li from Jiangsu Transportation Research Institute Co., Ltd. for their advice in assessment indicators selection and weights assignment.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Framework of 3D quality assessment of UUSR based on GEFs.
Figure 1. Framework of 3D quality assessment of UUSR based on GEFs.
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Figure 2. AHP-EWM joint weight method.
Figure 2. AHP-EWM joint weight method.
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Figure 3. The location of the study area based on a Google Earth image.
Figure 3. The location of the study area based on a Google Earth image.
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Figure 4. Three-dimensional geological model and profile grid of the study area.
Figure 4. Three-dimensional geological model and profile grid of the study area.
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Figure 5. Zoning map of YSW in the study area.
Figure 5. Zoning map of YSW in the study area.
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Figure 6. Spatial distribution of normalized indicators based on 3D geological model: (a) Normalized c; (b) Normalized φ; (c) Normalized k; (d) Normalized q; (e) Normalized dg; (f) Normalized ds.
Figure 6. Spatial distribution of normalized indicators based on 3D geological model: (a) Normalized c; (b) Normalized φ; (c) Normalized k; (d) Normalized q; (e) Normalized dg; (f) Normalized ds.
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Figure 7. Spatial distribution of QI value based on 3D geological model and profile grid.
Figure 7. Spatial distribution of QI value based on 3D geological model and profile grid.
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Figure 8. Numerical statistics of assessment results.
Figure 8. Numerical statistics of assessment results.
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Figure 9. Layout of the planned subway section in the study area based on a Google Earth image.
Figure 9. Layout of the planned subway section in the study area based on a Google Earth image.
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Figure 10. The QI value profile along the planned subway section.
Figure 10. The QI value profile along the planned subway section.
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Table 1. Assessment indicators of UUSR quality.
Table 1. Assessment indicators of UUSR quality.
First-Level IndexSecondary Index
Gc, φ, k
Hq
Sh, dg, ds
Table 2. Indicator extraction methods and sources.
Table 2. Indicator extraction methods and sources.
First-Level IndexSecondary IndexAssignation MethodsData Sources
Gc, φDirect shear test data with depthEngineering geological survey and geotechnical investigation
kPermeability test data with depth
HqZoning value based on YSWHydrogeological map
Sh, dg, dsSpatial distance calculation3D geological model
Table 3. Collected geological and geotechnical reports.
Table 3. Collected geological and geotechnical reports.
No.Reports
1Report of Hydrogeological Survey in Nanjing (1:200,000)
2Comprehensive Survey Report on Hydrogeology, Engineering Geology, and Environmental Geology of Nanjing (1:50,000)
3Evaluation Report on Groundwater Resources in Nanjing, Jiangsu Province
4Environmental Geological Survey Report of Urban Planning Area along the Yangtze River from Nanjing to Zhenjiang (1:50,000)
5Geotechnical Investigation Report of Nanjing Metro Line 10
6Geotechnical Investigation Report of Nanjing Jiangbei New District Integrated Pipe Gallery Phase II Project
7Geotechnical Investigation Report of Pubin Road in Nanjing
8Geotechnical Investigation Report of other 30 construction sites
Table 4. The weights of assessment indicators.
Table 4. The weights of assessment indicators.
IndicatorsWeights of IndexesJoint Weights
First-LevelSecondaryFirst-LevelSecondary
Gc0.31960.43080.1377
φ0.36530.1168
k0.20390.0652
HQ0.12201.00000.1220
Sh0.55840.53960.3013
dg0.29700.1659
ds0.16340.0912
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Yang, Y.; Wang, R.; Liu, D.; Wu, L.; Su, J. Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors. Appl. Sci. 2024, 14, 4046. https://doi.org/10.3390/app14104046

AMA Style

Yang Y, Wang R, Liu D, Wu L, Su J. Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors. Applied Sciences. 2024; 14(10):4046. https://doi.org/10.3390/app14104046

Chicago/Turabian Style

Yang, Yang, Rui Wang, Dixu Liu, Lixin Wu, and Jingwen Su. 2024. "Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors" Applied Sciences 14, no. 10: 4046. https://doi.org/10.3390/app14104046

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