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Abstract
applied sciences Article Environmental Risk Source Analysis and Classification of Zones: Subway Construction 1 , 2 1 , 2 , 3 1 , 2 1 1 Yangchun Yuan , Yongjun Qin * , Yongkang Zhang , Liangfu Xie , Xin Meng and Zheyi Guo School of Civil Engineering and Architecture, Xinjiang University, Urumqi 830047, China; yangchun1668@163.com (Y.Y.); xieliangfu@xju.edu.cn (L.X.); mengxin@stu.xju.edu.cn (X.M.); 875715741@stu.xju.edu.cn (Z.G.) Xinjiang Civil Engineering Technology Research Center, Urumqi 830016, China CCFEB Civil Engineering Co., Ltd., Changsha 410004, China; zhangyongkangzjwj@163.com * Correspondence: qyjjg@xju.edu.cn Abstract: Examining the environmental risk sources of regional subway construction is crucial for ensuring construction safety and providing guidance for future subway line planning. This study focused on Urumqi’s main urban area and used SBAS-InSAR analysis technology to extract the settlement rate field within 600 m of Urumqi Metro Line 1 and investigate these risk sources. Results showed that the environmental risk sources affecting subway construction in the study area could be classified into four categories: geological conditions, distribution of high-rise buildings, density of road networks, and density of clustered buildings. The study further analyzed the spatial distribution of each risk source and developed a comprehensive impact zoning evaluation model for environmental risk sources in the study area. The model was then used to assess the risk of the currently planned subway lines (1–7), revealing that the largest area of subway construction environmental risk sources (1444 partitions) was associated with soil layer, IV high-rise building risk, IV road network risk, and IV building density risk. Additionally, the study found that environmental risk sources had the most significant impact on Metro Line 6, emphasizing the importance of closely monitoring risk factors during future construction. Keywords: subway construction; risk source; distribution characteristics; comprehensive impact zoning Citation: Yuan, Y.; Qin, Y.; Zhang, Y.; Xie, L.; Meng, X.; Guo, Z. Environmental Risk Source Analysis and Classification of Zones: Subway 1. Introduction Construction. Appl. Sci. 2023, 13, 5831. https://doi.org/10.3390/ The subway plays a crucial role in guiding and coordinating urban development and app13105831 construction in China, and it has gradually become a vital component of the country’s urban traffic development. However, the risk of subway construction is closely related to Academic Editor: Francesco urban development due to the characteristics of subway use. The complex and constantly Colangelo changing construction environment of the city and certain human activities have resulted Received: 9 March 2023 in various types of environmental risk sources for subway construction, which can cause Revised: 7 April 2023 different levels of settlement during construction [1,2]. This can significantly impact the Accepted: 10 April 2023 safety of people’s lives and property. Published: 9 May 2023 The extended duration of subway construction exposes it to various risks, underscor- ing the need to assess these risks comprehensively. Researchers worldwide have pursued various analytical approaches to assess subway construction risk. For instance, Zhang et al. [3] adopted an analytical hierarchy process (AHP) model that incorporated factors Copyright: © 2023 by the authors. such as rock mass comprehensive degree, lithology characteristics, geological structure Licensee MDPI, Basel, Switzerland. characteristics, weathering degree, seismic intensity, slope, rainfall, and construction factors This article is an open access article to identify and rank tunnel portal slope stability. Yu et al. [4] proposed a probabilistic distributed under the terms and risk analysis technique that uses a layered simulation model to analyze risk factors at the conditions of the Creative Commons tunnel construction operation level. Similarly, Zhou et al. [5] advocated for analyzing sub- Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ way construction safety risks by establishing a database of accidents encountered during 4.0/). the construction process. Liu et al. [6] proposed a novel risk assessment technique that Appl. Sci. 2023, 13, 5831. https://doi.org/10.3390/app13105831 https://www.mdpi.com/journal/applsci Appl. Sci. 2023, 13, 5831 2 of 18 combines AHP and an expert classification method (EGM) to evaluate how geological con- ditions and the surrounding environment affect subway construction. Finally, Fu et al. [7] used the Apriori algorithm to mine robust association rules between risks in subway deep foundation pit engineering. Their findings suggest that key risk groups emanate from factors related to contractors, structures, and the natural environment, whereas the core sources of risk are attributable to owners, designers, and the social environment. However, these studies reveal that the construction environment exerts a significant influence on subway construction risk, but the analytical techniques employed incorporate subjective factors that do not reflect the environmental impact objectively. The subway construction environment and risk are interrelated, and several scholars worldwide have studied the risks that arise from the subway construction environment [8–11]. For example, Xiao [12] conducted numerical simulation analysis to summarize how high- rise buildings affect subway stations and station ancillary facilities. The study established that the excavation of tunnels disrupts the original stress balance state, making the new tun- nel structure vulnerable to the large load of super high-rise buildings. Shen [13] employed finite element analysis software to study the impact of super high-rise building structures on adjacent subway tunnels. The research found that during the structural construction stage, the tunnel mainly experiences vertical settlement, with minimal horizontal displace- ment. However, during the use stage of super high-rise buildings, the tunnel’s settlement and horizontal deformation change. XUE et al. [14] investigated how geological and hydro- logical conditions impact tunnels, and they established that groundwater conditions and rock integrity are the main factors leading to soft rock tunnel collapse. Xu et al. [15] used surrounding rock lithology, topography, excavation span, buried depth, groundwater, and rainfall as rating indicators to evaluate the collapse risk of loess tunnels. Meng et al. [16] pro- posed a cloud model and fuzzy analytic hierarchy process-based risk assessment method that established a risk assessment index system comprising four first-level indicators and seventeen second-level indicators, such as karst geological conditions, hydrogeological conditions, tunnel design, and shield construction. Adverse geological conditions were found to have the most significant impact on shield construction risk. Zhou et al. [17] studied the risk of urban subway tunnels crossing existing bridge pile foundations, and they identified pile foundation settlement as the primary risk. Li et al. [18] investigated the response of subway tunnels to dynamic vehicle loads and found that subway shield tunnel displacement amplitude and soil maximum settlement were significantly affected by vehicle load. Shi et al. [19] analyzed the displacement changes of underground tunnels and building foundation pit excavation, and they established that the connection area between the tunnel and the foundation pit experiences minimal horizontal displacement, while the tunnel experiences significant vertical displacement, and settlement at the corner of the building adjacent to the foundation pit is relatively high. Xu et al. [20] analyzed subway construction settlement when crossing existing railways and identified train live load as the main factor leading to subway construction settlement. While these studies highlight risk sources associated with individual subways in different regions of China, the actual subway construction environment typically faces multiple risk sources, necessitating the development of a method that considers the comprehensive impact of risk sources in the study area and accurately evaluates each risk source’s risk level. Based on the preceding analysis and discussion, it is apparent that the distribution of environmental risk sources in subway construction varies regionally. Therefore, iden- tifying risk sources based on regional characteristics can enhance the accuracy of results. Risk identification involves investigating and recognizing potential risks in the subway construction process and categorizing them based on risk types and impact levels. An analysis of various types of subway construction accidents reveals that most accidents occur due to surface subsidence caused by the subway construction process [21,22]. Regional surface subsidence can be regarded as an indication of the distribution of environmental risk sources to a certain degree [23–27]. Appl. Sci. 2023, 13, 5831 3 of 18 The research area for this study is Urumqi, which is situated at the center of Eurasia, at the northern foot of the Tianshan Mountains, and at the southern edge of the Junggar Basin. As the capital of the Xinjiang Uygur Autonomous Region, Urumqi is the political, economic, and cultural hub of the region and serves as the western bridgehead of China, playing a significant role in China’s Belt and Road Initiative. With the development of Urumqi as the core economic zone, the demand for rail transit has been on the rise. According to the “Urumqi Rail Transit Network Planning”, the planned length of Urumqi’s rail transit line is 261.8 kilometers, with Line 1~7 being the main city planning line. In 2019, Urumqi Metro Line 1 was completed and put into operation, while Line 2 began construction in 2015, and the first phase of Metro Line 3 and Line 4 began construction in 2016. Conventional methods for monitoring subway settlement do not provide a compre- hensive overview. Interferometric Synthetic Aperture Radar (InSAR) is a satellite-based technology that offers several advantages, including high resolution, high precision, wide range, and all-weather, all-day monitoring [28–31]. It has proven successful in monitoring surface deformation in various regions. This study employs the SBAS-InSAR [32–34] analy- sis method to derive the surface deformation rate field during the construction of Urumqi’s existing subway line 1. Examining surface deformation in the area along subway line 1 during construction can identify the environmental factors responsible for such deforma- tion and uncover potential risk sources associated with subway construction [35–38]. By calibrating these environmental risk sources, we analyze the geometric distribution of each risk source using ArcGIS software. We then overlay the distribution characteristics of each risk source to assess and partition the environmental risks associated with subway con- struction. This study offers insight into future Urumqi subway line planning and subway construction risk assessment. 2. Urumqi Engineering Geological Survey Urumqi is situated in the heartland of Eurasia, at the northern foot of the Northern Tianshan Mountains and the southern edge of the Junggar Basin. The city is surrounded by mountains on its east, south, and west sides. The terrain is high in the southeast and low 0 00 0 00 0 00 0 00 in the northwest, located at 86 37 33 –88 58 24 E and 42 45 32 –44 08 00 N, with an altitude ranging from 580–920 m and a natural slope of 12‰–15‰. The city comprises seven districts and one county (Tianshan District, Shayibake District, Xinshi District, Shuimogou District, Toutunhe District, Dabancheng District, Midong District, and Urumqi County) 2 2 and covers a total area of 14,216 km , of which 2813 km is the built-up area. Subway construction predominantly occurs in urban centers. To gain an understand- ing of the study area’s city planning and subway line development, we obtained Urumqi’s 2020 land-use data from ESA (Figure 1a) and Urumqi’s rail transit planning data (Figure 1b) from the Urumqi Metro Group. Figure 1a was generated by Esri, Impact Observatory, and Microsoft based on Sentinel-2 10 m resolution satellite data. Figure 1b represents the “Urumqi Rail Transit Construction Plan 2012–2019”, which the China National De- velopment and Reform Commission approved in 2005. As of 2023, the construction and utilization of Urumqi Metro Line 1 and the first phase of Urumqi Metro Line 2, 3, and 4 have been completed. To identify the research area, we drew the contour grid map of the primary urban region using the cadastral processing function of Arc GIS, combined with Urumqi’s land- use planning map. We excluded scattered building areas and confined the study area to Urumqi’s northwest, covering 2813 km . Appl. Sci. 2023, 13, x FOR PEER REVIEW 4 of 17 Appl. Sci. 2023, 13, 5831 4 of 18 Appl. Sci. 2023, 13, x FOR PEER REVIEW 4 of 17 Figure 1. Study area coverage and land use data map. (a) Distribution of land use data in the study area in 2021; (b) planning map of Urumqi subway line, including Lines 1, 2, 3, 4, 5, 6, and 7. Figure 1. Study area coverage and land use data map. (a) Distribution of land use data in the study Figure 1. Study area coverage and land use data map. (a) Distribution of land use data in the study area in 2021; (b) planning map of Urumqi subway line, including Lines 1, 2, 3, 4, 5, 6, and 7. area in 2021; (b) planning map of Urumqi subway line, including Lines 1, 2, 3, 4, 5, 6, and 7. 3. Risk Source Analysis of Subway Construction 3. Risk Source Analysis of Subway Construction 3. Risk Source Analysis of Subway Construction 3.1. Introduction of SBAS-InSAR 3.1. Introduction of SBAS-InSAR 3.1. Introd Small B uction of SB ase Line Subset AS-InSAR Interferometr ic Synthetic Aperture Radar (SBAS-InSAR) is a Small Base Line Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is a satellite-based monitoring technique that offers all-weather, all-day, high-resolution, Small Base Line Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is a satellite-based monitoring technique that offers all-weather, all-day, high-resolution, high- high-precision, wide-range, and convenient data acquisition advantages. This technology satellite-based monitoring technique that offers all-weather, all-day, high-resolution, precision, wide-range, and convenient data acquisition advantages. This technology can can accurately measure surface deformation at a millimeter level, with high spatial reso- high-precision, wide-range, and convenient data acquisition advantages. This technology accurately measure surface deformation at a millimeter level, with high spatial resolution lution and wide coverage. Given its unique benefits, it has considerable potential in mon- can accurately measure surface deformation at a millimeter level, with high spatial reso- and itoring wide land coverage. subsidence ov Given er larg its unique e arebenefits, as and long it has -timconsiderable e periods andpotential has beenin suc monitoring cessfully lution and wide coverage. Given its unique benefits, it has considerable potential in mon- land subsidence over large areas and long-time periods and has been successfully applied applied in various regions for surface deformation monitoring. itoring land subsidence over large areas and long-time periods and has been successfully in various Comp regions ared tofor trad surface itionaldeformation geodetic memonitoring. thods, such as leveling and topographic sur- applied in various regions for surface deformation monitoring. Compared to traditional geodetic methods, such as leveling and topographic surveys, veys, SBAS-InSAR technology overcomes limitations such as manual distribution, low Compared to traditional geodetic methods, such as leveling and topographic sur- SBAS-InSAR spatial resolu technology tion, and hiover gh cost. comes SBA limitations S-InSAR techn such ology can as manual provide distribution, a complow rehensive spatial veys, SBAS-InSAR technology overcomes limitations such as manual distribution, low resolution, and high cost. SBAS-InSAR technology can provide a comprehensive view of view of the subsidence process along the subway and subsequently identify changes in spatial resolution, and high cost. SBAS-InSAR technology can provide a comprehensive the ground d subsidence eformpr ation in time and sp ocess along the subway ace. The flow c and subsequently hart of SBA identify S-InSAchanges R is presented in in ground view of the subsidence process along the subway and subsequently identify changes in deformation Figure 2. in time and space. The flow chart of SBAS-InSAR is presented in Figure 2. ground deformation in time and space. The flow chart of SBAS-InSAR is presented in Figure 2. Figure 2. SBAS-InSAR workflow flow chart. Appl. Sci. 2023, 13, x FOR PEER REVIEW 5 of 17 Figure 2. SBAS-InSAR workflow flow chart. Urumqi Metro Line 1 starts at Santunbei Station in Tianshan District and passes Appl. Sci. 2023, 13, 5831 5 of 18 through Tianshan District, Shayibake District, Shuimogou District, and Xinshi District, with stops at Santunbei, Xinjiang University, International Bazaar, Nanhu Square (Peo- ple’s Government of Urumqi City), and the international airport station line in Xinshi Dis- Urumqi Metro Line 1 starts at Santunbei Station in Tianshan District and passes trict. The line covers a distance of 27.615 km and commenced operation on 28 June 2019. through Tianshan District, Shayibake District, Shuimogou District, and Xinshi District, It is an underground line with 21 stations and runs roughly in a north–south direction. with stops at Santunbei, Xinjiang University, International Bazaar, Nanhu Square (People’s The study area focuses on surface deformation during the subway construction. This Government of Urumqi City), and the international airport station line in Xinshi District. study used surface deformation driving factors during the construction of Urumqi Metro The line covers a distance of 27.615 km and commenced operation on 28 June 2019. It is an underground line with 21 stations and runs roughly in a north–south direction. Line 1 to determine and identify environmental risk sources during subway construction. The study area focuses on surface deformation during the subway construction. This The study obtained 22 satellite images from Sentinel-1A between 2017 and 2018 to extract study used surface deformation driving factors during the construction of Urumqi Metro the deformation rate field along the 600 m range of Metro Line 1 using SBAS-InSAR tech- Line 1 to determine and identify environmental risk sources during subway construction. nology [39]. By studying the regional differences in deformation rate during the construc- The study obtained 22 satellite images from Sentinel-1A between 2017 and 2018 to extract tion of Urumqi Metro Line 1 and combining this data with the characteristic environmen- the deformation rate field along the 600 m range of Metro Line 1 using SBAS-InSAR tal risk sources of each region, the study compared the surface deformation performance technology [39]. By studying the regional differences in deformation rate during the of different construction areas to determine environmental risk sources of subway con- construction of Urumqi Metro Line 1 and combining this data with the characteristic struction in the study area [40]. environmental risk sources of each region, the study compared the surface deformation performance of different construction areas to determine environmental risk sources of subway construction in the study area [40]. 3.2. Geological Conditions Surface subsidence associated with subway construction is closely related to regional 3.2. Geological Conditions geological conditions, affecting the construction methods used for the subway sections. Surface subsidence associated with subway construction is closely related to regional Different construction methods result in varying degrees of surface subsidence during geological conditions, affecting the construction methods used for the subway sections. subway construction, leading to varying degrees of subway construction risk [41–44]. Ac- Different construction methods result in varying degrees of surface subsidence during cording to the report from the Urumqi Muni subway construction, leading to varying degreescof ipal Bureau of Geolo subway constructiongy, the strat risk [41–44].igraphic According to the report from the Urumqi Municipal Bureau of Geology, the stratigraphic conditions in the study area can be divided into three categories: a soil layer, a soil–rock conditions in the study area can be divided into three categories: a soil layer, a soil–rock composite layer, and a rock layer (Figure 3). The geological structure data come from the composite layer, and a rock layer (Figure 3). The geological structure data come from the Urumqi Municipal Bureau of Geology. Most of the subway construction settlement occurs in Urumqi Municipal Bureau of Geology. Most of the subway construction settlement occurs the first two geological conditions, particularly in the soil area, where the soil is easily com- in the first two geological conditions, particularly in the soil area, where the soil is easily pacted by the upper load during subway construction, resulting in surface settlement. There- compacted by the upper load during subway construction, resulting in surface settlement. fore, a spatial correlation exists between surface subsidence and geological conditions in the Therefore, a spatial correlation exists between surface subsidence and geological conditions Urumqi subway construction. As a result, the distribution of stratum conditions is categorized in the Urumqi subway construction. As a result, the distribution of stratum conditions is as the first type of risk source for the subway construction environment. categorized as the first type of risk source for the subway construction environment. Figure 3. Figure 3. Geological Geological strstru uctur ctu e r of e of the the study stuar dy ea area inclu includes the des soil the soil layer, lay rock er, layer rock , and laysoil–r er, and ock soil–rock composite composite layer laye . r. Appl. Sci. 2023, 13, 5831 6 of 18 Appl. Sci. 2023, 13, x FOR PEER REVIEW 6 of 17 3.3. Super High-Rise Building Distribution The construction of subways can disturb the underground soil and disrupt the original 3.3. Super High-Rise Building Distribution stress balance of the soil, which can cause surface subsidence. Additionally, when subway The construction of subways can disturb the underground soil and disrupt the orig- tunnels pass through super high-rise buildings, the impact on the construction of the inal stress balance of the soil, which can cause surface subsidence. Additionally, when subway tunnel cannot be generalized with ordinary buildings due to the higher load of subway tunnels pass through super high-rise buildings, the impact on the construction of super high-rise buildings compared to other buildings [45–48]. According to the “Code the subway tunnel cannot be generalized with ordinary buildings due to the higher load for Design of Civil Buildings” GB50352–2005, super high-rise buildings are defined as of super high-rise buildings compared to other buildings [45–48]. According to the “Code buildings with more than 40 stories and a height of more than 100 meters [49]. for Design of Civil Buildings” GB50352–2005, super high-rise buildings are defined as buildIn ing Ur s w umqi’s ith more t Tianshan han 40 st District, ories and which a height is the of m political, ore than 1 economic, 00 meters [cultural, 49]. and financial center In Urum of the qi’ city s T , ither anshan D e are iseveral strict, which subway is the polit stations ical, e that conomic, c have been ultura opened, l, and fin including ancial Line center of the city, there are several subway stations that have been opened, including Line 1 North Gate Station, South Gate Station, Erdaoqiao Station, Xinjiang University Station, 1 North Gate Station, South Gate Station, Erdaoqiao Station, Xinjiang University Station, and Santunbei Station. According to the “Construction Environment Investigation Report and Santunbei Station. According to the “Construction Environment Investigation Report of Urumqi Metro Line 1”, there are 127 super high-rise building sites in this area, making of Urumqi Metro Line 1,” there are 127 super high-rise building sites in this area, making it the most densely populated area of super high-rise buildings in the study area. The it the most densely populated area of super high-rise buildings in the study area. The dashed boxes b and c in Figure 4a represent the two subsidence areas of Line 1, which dashed boxes b and c in Figure 4a represent the two subsidence areas of Line 1, which are are the International Airport Station-Xuanrendun Station, with a maximum displacement the International Airport Station-Xuanrendun Station, with a maximum displacement of of less than 5 mm/year (Figure 4b), and from Erdaoqiao to Nanmen Station, where the less than 5 mm/year (Figure 4b), and from Erdaoqiao to Nanmen Station, where the max- maximum displacement exceeds 10 mm/year (Figure 4c). Comparing the settlements imum displacement exceeds 10 mm/year (Figure 4c). Comparing the settlements of the of the two areas shows that the distribution of super high-rise buildings is the main two areas shows that the distribution of super high-rise buildings is the main factor in- factor inducing the deformation of subway construction in this section. Therefore, the ducing the deformation of subway construction in this section. Therefore, the distribution distribution of super highof -risuper se build high-rise ings is cons buildings idered th is e second consider risk ed sou therce o second f the subw risk sour ay cce onst ofruc- the subway tion environment. construction environment. Figure 4. (a) SBAS-InSAR deformation rate map for the 600 m range of Metro Line 1. Regions b and Figure 4. (a) SBAS-InSAR deformation rate map for the 600 m range of Metro Line 1. Regions c, marked with white dotted rectangles, are two sinking zones along Line 1, as shown in (b,c), re- b and c, marked with white dotted rectangles, are two sinking zones along Line 1, as shown spectively. in (b) and (c), respectively. 3.4. Road Network Distribution 3.4. Road Network Distribution The urban road network is a crucial component of urban functionality, and subway The urban road network is a crucial component of urban functionality, and subway lines often overlap with it. However, during subway construction, when the construction lines often overlap with it. However, during subway construction, when the construction of the subway section passes under the existing urban road network, the dynamic load of the subway section passes under the existing urban road network, the dynamic load brought by the road network can aggravate the development of land subsidence. brought by the road network can aggravate the development of land subsidence. According to the report obtained following the analysis of the Urumqi subway line construction settlement method, the larger settlement area of the Urumqi subway line construction is located at the intersection of the urban road network and elevated urban areas. Roads Appl. Sci. 2023, 13, x FOR PEER REVIEW 7 of 17 Appl. Sci. 2023, 13, 5831 7 of 18 According to the report obtained following the analysis of the Urumqi subway line con- struction settlement method, the larger settlement area of the Urumqi subway line con- struction is located at the intersection of the urban road network and elevated urban areas. with different functions (high-speed, national, and ordinary roads) pose different risks to Roads with different functions (high-speed, national, and ordinary roads) pose different subway construction. risks to subway construction. To examine the impact of different road network densities on subway construction, To examine the impact of different road network densities on subway construction, we selected four measurement points with varying road network densities between Balou we selected four measurement points with varying road network densities between Balou Station and Wangjialiang Station of Metro Line 1. We conducted a comparative analy- Station and Wangjialiang Station of Metro Line 1. We conducted a comparative analysis sis of deformation rates at these points. Figure 5a shows that Point A has no road net- of deformation rates at these points. Figure 5a shows that Point A has no road network work nearby, and the road network density of Points B, C, and D increases in the order nearby, and the road network density of Points B, C, and D increases in the order of B < C of B < C < D. Figure 5b reveals that the maximum settlement rate is the smallest at Point A, < D. Figure 5b reveals that the maximum settlement rate is the smallest at Point A, while while the deformation rates at Points B, C, and D increase with the road network density. the deformation rates at Points B, C, and D increase with the road network density. This This suggests that there is a positive correlation between surface deformation and road net- suggests that there is a positive correlation between surface deformation and road net- work density during subway construction. Hence, road network distribution is considered work density during subway construction. Hence, road network distribution is consid- a type III risk source for subway construction environments. ered a type III risk source for subway construction environments. Figure 5. (a) Location of monitoring points under varying road network densities. (b) Shows the Figure 5. (a) Location of monitoring points under varying road network densities. (b) Shows the settlement rate duration curve for the measuring points under varying road network densities dur- settlement rate duration curve for the measuring points under varying road network densities during ing the monitoring period. The black dotted box below the graph indicates the maximum settlement the monitoring period. The black dotted box below the graph indicates the maximum settlement rate rate values for Points A, B, C, and D. values for Points A, B, C, and D. 3.5. Cluster Building Distribution 3.5. Cluster Building Distribution Cluster buildings refer to a group of buildings formed by several adjacent buildings Cluster buildings refer to a group of buildings formed by several adjacent buildings in in a city that are closely connected in spatial organization. Cluster buildings primarily a city that are closely connected in spatial organization. Cluster buildings primarily refer to refer to buildings arranged in a group. The impact of cluster buildings on subway con- buildings arranged in a group. The impact of cluster buildings on subway construction is struction is mainly as follows: the additional stress on the base has a deeper influence on mainly as follows: the additional stress on the base has a deeper influence on depth and a depth and a slower attenuation speed, and it causes larger settlements during the subway slower attenuation speed, and it causes larger settlements during the subway construction construction process [50–52]. We quantified the distribution of cluster buildings based on process [50–52]. We quantified the distribution of cluster buildings based on building building density and calculated the ratio of the total base area of the building to the occu- density and calculated the ratio of the total base area of the building to the occupied area pied area within a certain range [53]. within a certain range [53]. Building Density = Total Base Area/Land Area (1) Building Density = Total Base Area/Land Area (1) To avoid deviations in the results of environmental risk source analysis caused by the combined influence of geological conditions, super high-rise building distribution, To avoid deviations in the results of environmental risk source analysis caused by and road network distribution, this study used the control variable method and selected the combined influence of geological conditions, super high-rise building distribution, four continuous subsidence areas (a, b, c, d in Figure 6e). These four subsidence areas are and road network distribution, this study used the control variable method and selected located in the overlapping area of Beijing Road Main Road and Metro Line 1 in Urumqi. four continuous subsidence areas (a, b, c, d in Figure 6e). These four subsidence areas are Except for different building densities, there is no significant difference in the subway located in the overlapping area of Beijing Road Main Road and Metro Line 1 in Urumqi. construction environment and construction methods in the four areas (Table 1). As shown Except for different building densities, there is no significant difference in the subway in the green part of Table 1, with the increase in regional building density, the maximum construction environment and construction methods in the four areas (Table 1). As shown in the green part of Table 1, with the increase in regional building density, the maximum surface displacement during subway construction in the region also increases, and the increasing trend is positively correlated with the building density. Therefore, building density is considered the fourth risk source of subway construction environments. Appl. Sci. 2023, 13, 5831 8 of 18 Appl. Sci. 2023, 13, x FOR PEER REVIEW 8 of 17 Appl. Sci. 2023, 13, x FOR PEER REVIEW 8 of 17 surface displacement during subway construction in the region also increases, and the increasing trend is positively correlated with the building density. Therefore, building surface displacement during subway construction in the region also increases, and the density is considered the fourth risk source of subway construction environments. increasing trend is positively correlated with the building density. Therefore, building density is considered the fourth risk source of subway construction environments. Figure 6. Comparative map for the settlement area of subway construction. (e) SBAS-InSAR defor- Figure 6. Comparative map for the settlement area of subway construction. (e) SBAS-InSAR defor- Figure 6. Comparative map for the settlement area of subway construction. (e) SBAS-InSAR defor- mation rate map for the 600 m range of Sangong Station-Railway Bureau Station of Metro Line 1. mation rate map for the 600 m range of Sangong Station-Railway Bureau Station of Metro Line 1. The mation rate map for the 600 m range of Sangong Station-Railway Bureau Station of Metro Line 1. The areas a, b, c, and d are marked with dotted rectangles, representing the four control variable areas a, b, c, and d are marked with dotted rectangles, representing the four control variable areas The areas a, b, c, and d are marked with dotted rectangles, representing the four control variable areas along Line 1. The enlarged figures are shown as (a), (b), (c), and (d), respectively. areas along Lin along Line 1.eThe 1. The enlarg enlarged ed figures are figures are shown shown as ( as (a), a), ( (b), b), ( (c), c), and ( and (dd ),), respectively. respectively. Table 1. a, b, c, and d regional displacement range and environmental variable table. The green Table 1. Table 1. a, b, c, and d regional displa a, b, c, and d regional displacement cement range range and en and vironmental v environmental ariable tab variable le. T table. he green The green padding indicates the area displacement range, and the red triangles present the maximum dis- padding indicates the area displacement range, and the red triangles present the maximum dis- padding indicates the area displacement range, and the red triangles present the maximum displace- placement monitored during area construction. placement monitored during area construction. ment monitored during area construction. Data Data Environment Variable Environment Variable Data Environment Variable 2 0 −2 −4 −6 −8 −10 −12 2 0 −2 −4 −6 −8 −10 −12 2 0 2 4 6 8 10 12 Building Den- Super High-Rise Geological Con- Building Super High-Rise Geological Building Den- Super High-Rise Geological Con- Area Area Area si Density ty (%) (%) Build Building ing dition Condition sity (%) Building dition a 1.08 −7.89 23.7 None soil horizon aa 1.08 −7.89 23.7 23.7 None None soil hori soil horizonzon b 28.9 None soil horizon b 1.24 −8.38 28.9 None soil horizon b 1.24 −8.38 28.9 None soil horizon c c 0.87 0.87 − 11 −.78 11.78 323.6 2.6 None None soisoill horizhorizoon n c 32.6 None soil horizon d 48.2 None soil horizon d d −0.52 −0.52 −12 −.23 12.23 484.2 8.2 None None soisoill horizhorizoon n 4. Distribution Characteristics of Environmental Risk Sources of Subway 4. Distribution Characteristics of Environmental Risk Sources of Subway 4. Distribution Characteristics of Environmental Risk Sources of Subway Construction in Study Area Construction in Study Area Construction in Study Area Based on the analysis of the environmental risk sources of subway construction in Based on the analysis of the environmental risk sources of subway construction in Based on the analysis of the environmental risk sources of subway construction in Chapter 2, we obtained the distribution data of the geological conditions in the study area, Chapter 2, we obtained the distribution data of the geological conditions in the study area, Chapter 2, we obtained the distribution data of the geological conditions in the study area, the POI data of the super high-rise buildings, the distribution data of the road network, the POI data of the super high-rise buildings, the distribution data of the road network, the POI data of the super high-rise buildings, the distribution data of the road network, and the vector data of the building contours. Using the ArcGIS spatial 3D analysis func- and the vector data of the building contours. Using the ArcGIS spatial 3D analysis func- and the vector data of the building contours. Using the ArcGIS spatial 3D analysis function, tion, we analyzed the spatial distribution characteristics of the environmental risk sources tion, we we an analyzed alyzed the the sp spatial atial d distribution istribution cha characteristics racteristics of of the the envi envir ronm onmental ental risrisk k source sours ces of of subway construction in the study area and classified the risk levels of various risk of susubway bway co constr nstruc uction tion in in th the e sstudy tudy a ar re ea a a and nd clas classified sified the the r risk isk levels levels of v of various arious r riskisour sk ces sources according to the analysis results. according to the analysis results. sources according to the analysis results. 4.1. Spatial Distribution of Geological Conditions 4.1. Spatial Distribution of Geological Conditions 4.1. Spatial Distribution of Geological Conditions The data on geological conditions in Urumqi were collected from the Urumqi Mu- The data on geological conditions in Urumqi were collected from the Urumqi Mu- The data on geological conditions in Urumqi were collected from the Urumqi Mu- nicipal Bureau of Geology. The distribution of the geological conditions is shown in Figure nicipal Bureau of Geology. The distribution of the geological conditions is shown in nicipal Bureau of Geology. The distribution of the geological conditions is shown in Figure 3. The geological conditions are divided into three grades based on the distribution of Figure 3. The geological conditions are divided into three grades based on the distribution 3. The geological conditions are divided into three grades based on the distribution of geological conditions in the study area (Table 2). of geological conditions in the study area (Table 2). geological conditions in the study area (Table 2). Appl. Sci. 2023, 13, 5831 9 of 18 Table 2. Risk grade and distribution statistics of geological conditions in the study area. Geological Conditions Risk Level Geological Condition Area (km ) Proportion (%) Level I Soil horizon 1771.3 63 Level II Earth-rock composite layer 429.1 15.3 Level III Lithosphere 612.6 21.7 Statistical analysis reveals that the study area is primarily composed of soil layers, which are primarily distributed in the northern part of the study area. A small area is present in the north and south. The rest consists of the soil–rock composite layer. A staggered distribution of the layer of rock is present in the southwest and southeast region of the study area. 4.2. High-Rise Building Distribution The POI data of super high-rise buildings in the study area were obtained from the Gaode POI spatial data in the first quarter of 2021. Combined with the environmental survey report of Urumqi, the data were preprocessed, and finally, we obtained 350 POI data of super high-rise buildings. In this study, we utilized the ArcGIS kernel density analysis method to analyze the Point of Interest (POI) data for super high-rise buildings in the study area. The search radius for the kernel density analysis was employed to replace the influence range of the super high-rise buildings on the region. The kernel density analysis value indicates the impact of the super high-rise buildings on the region, with a higher value indicating a greater impact. A distance threshold of 500 meters was set, and the pixel size was set to 200. Based on the kernel density analysis value, we classified the influence range of the super high-rise buildings into four grades (Table 3) and obtained the spatial distribution characteristics of the high-rise buildings in the study area (Figure 7). Table 3. Statistical table of risk level and influence range of super high-rise buildings in the study area. High-Rise Building Risk Level The Influence Range of Nuclear Density Influence Area (km ) Level I 0.00–13.43 2779.28 Level II 13.43–26.87 24.92 Level III 26.87–40.31 6.96 Level IV 40.31–53.75 1.84 Appl. Sci. 2023, 13, x FOR PEER REVIEW 9 of 17 Table 2. Risk grade and distribution statistics of geological conditions in the study area. Geological Conditions Risk Level Geological Condition Area (km) Proportion (%) Level I Soil horizon 1771.3 63 Level II Earth-rock composite layer 429.1 15.3 Level III Lithosphere 612.6 21.7 Statistical analysis reveals that the study area is primarily composed of soil layers, which are primarily distributed in the northern part of the study area. A small area is present in the north and south. The rest consists of the soil–rock composite layer. A stag- gered distribution of the layer of rock is present in the southwest and southeast region of the study area. 4.2. High-Rise Building Distribution The POI data of super high-rise buildings in the study area were obtained from the Gaode POI spatial data in the first quarter of 2021. Combined with the environmental survey report of Urumqi, the data were preprocessed, and finally, we obtained 350 POI data of super high-rise buildings. In this study, we utilized the ArcGIS kernel density analysis method to analyze the Point of Interest (POI) data for super high-rise buildings in the study area. The search radius for the kernel density analysis was employed to replace the influence range of the super high-rise buildings on the region. The kernel density analysis value indicates the impact of the super high-rise buildings on the region, with a higher value indicating a greater impact. A distance threshold of 500 meters was set, and the pixel size was set to 200. Based on the kernel density analysis value, we classified the influence range of the super high-rise buildings into four grades (Table 3) and obtained the spatial distribution characteristics of the high-rise buildings in the study area (Figure 7). As shown in Figure 7, the super high-rise buildings in the study area are mainly dis- tributed in the intersection of Tianshan District, Shayibake District, Xinshi District, and Shuimogou District, and they are clustered and distributed, with a trend developing to- ward the new urban area, which is consistent with the development of Urumqi city center. Appl. Sci. 2023, 13, 5831 10 of 18 Finally, the clustering distribution of the super high-rise buildings will significantly im- pact the risk of subway construction in this area. Figure 7. Nuclear density analysis of super high-rise buildings in the study area. The white line in Figure 7. Nuclear density analysis of super high-rise buildings in the study area. The white line in the figure represents the boundary of each regional administrative region. the figure represents the boundary of each regional administrative region. As shown in Figure 7, the super high-rise buildings in the study area are mainly distributed in the intersection of Tianshan District, Shayibake District, Xinshi District, and Shuimogou District, and they are clustered and distributed, with a trend developing toward the new urban area, which is consistent with the development of Urumqi city center. Finally, the clustering distribution of the super high-rise buildings will significantly impact the risk of subway construction in this area. 4.3. Road Network Distribution The road network data used in this study were obtained from the “China Natural Disaster Municipal Risk Census Data”. A 500 m 500 m grid was created within the study area using the ArcGis function to create fishing nets, and the grid was used as a statistical unit to calculate the road length in each grid area to obtain the road network density (Equation (2)). Road Network Density = Regional Road Network Length/Grid Area (2) The road network density is used as a parameter of the road network risk distribution, and the risk level of the road network in the study area was divided into four categories (I, II, III, IV) by using the Jenks natural breakpoint method. The risk level distribution map of road network density in the study area was obtained (Figure 8). The road network in the study area is concentrated in the middle, and the high-density area (dark blue area) is mainly located at the intersection of urban interchanges and main roads. These areas are subjected to large pavement dynamic loads for prolonged periods, which increases the risks associated with subway construction. Table 4 shows the parameters used to evaluate the road network risk level and the proportion of different road network risk levels in the study area. Appl. Sci. 2023, 13, x FOR PEER REVIEW 10 of 17 Table 3. Statistical table of risk level and influence range of super high-rise buildings in the study area. The Influence Range of High-Rise Building Risk Level Influence Area (km ) Nuclear Density Level I 0.00–13.43 2779.28 Level II 13.43–26.87 24.92 Level III 26.87–40.31 6.96 Level IV 40.31–53.75 1.84 4.3. Road Network Distribution The road network data used in this study were obtained from the “China Natural Disaster Municipal Risk Census Data.” A 500 m * 500 m grid was created within the study area using the ArcGis function to create fishing nets, and the grid was used as a statistical unit to calculate the road length in each grid area to obtain the road network density (Equation (2)). Road Network Density = Regional Road Network Length/Grid Area (2) The road network density is used as a parameter of the road network risk distribu- tion, and the risk level of the road network in the study area was divided into four cate- gories (I, II, III, IV) by using the Jenks natural breakpoint method. The risk level distribu- tion map of road network density in the study area was obtained (Figure 8). The road network in the study area is concentrated in the middle, and the high-density area (dark blue area) is mainly located at the intersection of urban interchanges and main roads. These areas are subjected to large pavement dynamic loads for prolonged periods, which increases the risks associated with subway construction. Table 4 shows the parameters Appl. Sci. 2023, 13, 5831 11 of 18 used to evaluate the road network risk level and the proportion of different road network risk levels in the study area. Figure 8. Density risk level and distribution for the road network. White grid for the generated Figure 8. Density risk level and distribution for the road network. White grid for the generated fishing net statistics unit. fishing net statistics unit. Table 4. Risk grade and statistical table for the road network in the study area. Road Network Density Risk Level Density Range (%) Proportion (%) Level I 0.00–2.98 3.0 Level II 2.98–10.9 7.9 Level III 10.9–21.2 34.6 Level IV 21.2–31.0 54.5 4.4. Cluster Building Division The building profile data used in this study were obtained from the “China Natural Disaster Housing Risk Census Data.” The formula used for calculating building density (Section 2) was used to establish the fishing net layer and the building contour vector layer (Section 4.3). The building density of each grid was determined by calculating the ratio of the building contour area to the grid area. Based on the building density value of each grid, the risk level was divided into four levels (I, II, III, IV) using the Jenks natural breakpoint method (Table 5). Figure 9 shows the building density distribution map of the study area, where the height of each block represents the building density in that area. Table 5. Risk level and distribution of cluster buildings. Building Density Risk Level Density Range (%) Proportion (%) Level I 68.5–100 2.5 Level II 35.4–68.5 14.8 Level III 12.6–35.4 27.6 Level IV 0.00–12.6 55.1 Appl. Sci. 2023, 13, x FOR PEER REVIEW 11 of 17 Table 4. Risk grade and statistical table for the road network in the study area. Road Network Density Risk Level Density Range (%) Proportion (%) Level I 0.00–2.98 3.0 Level II 2.98–10.9 7.9 Level III 10.9–21.2 34.6 Level IV 21.2–31.0 54.5 4.4. Cluster Building Division The building profile data used in this study were obtained from the “China Natural Disaster Housing Risk Census Data.” The formula used for calculating building density (Section 2) was used to establish the fishing net layer and the building contour vector layer (Section 4.3). The building density of each grid was determined by calculating the ratio of the building contour area to the grid area. Based on the building density value of each grid, the risk level was divided into four levels (I, II, III, IV) using the Jenks natural break- point method (Table 5). Figure 9 shows the building density distribution map of the study area, where the height of each block represents the building density in that area. From the results of the building density analysis shown in Figure 9, we can conclude that the high-density building areas in the study area are primarily located in the north- west of Tianshan District, the northeast of Shayibake District, the central and western part of Toutunhe District, the southeast of Xinshi District, the southeast of Midong District, Appl. Sci. 2023, 13, 5831 12 of 18 and the west of Shuimogou District. The risk level and distribution of building density in each region are shown in Table 5. Figure 9. Building density distribution map of the study area. Figure 9. Building density distribution map of the study area. Table 5. Risk level and distribution of cluster buildings. From the results of the building density analysis shown in Figure 9, we can conclude that the Build high-density ing Densitbuilding y Risk Lear ve eas l in theDens study ity R area an ar ge e primarily (%) locate Propor d in tion the (% northwest ) of Tianshan District, the northeast of Shayibake District, the central and western part of Level I 68.5–100 2.5 Toutunhe District, the southeast of Xinshi District, the southeast of Midong District, and Level II 35.4–68.5 14.8 Appl. Sci. 2023, 13, x FOR PEER REVIEW 12 of 17 the west of Shuimogou District. The risk level and distribution of building density in each Level III 12.6–35.4 27.6 region are shown in Table 5. Level IV 0.00–12.6 55.1 5. Comprehensive Analysis of the Environmental Risk Faced during the Construction 5. Comprehensive Analysis of the Environmental Risk Faced during the Construction of the Subway in the Study Area of the Subway in the Study Area 5.1. Introduction of Geometric Partition Theory 5.1. Introduction of Geometric Partition Theory Multi-layer raster data overlay analysis (pixel statistics) is often used for mathematical Multi-layer raster data overlay analysis (pixel statistics) is often used for mathemati- function calculation and the reclassification of raster. It refers to the reclassification of cal function calculation and the reclassification of raster. It refers to the reclassification of multiple raster data with a unified hierarchy. The available statistical data are mode, maxi- multiple raster data with a unified hierarchy. The available statistical data are mode, max- mum, mean, median, minimum, minority, range, standard deviation, sum, and variability. imum, mean, median, minimum, minority, range, standard deviation, sum, and variabil- The data analysis principle is shown in Figure 10. The partition operation is based on the ity. The data analysis principle is shown in Figure 10. The partition operation is based on classification area of a data set, performs numerical statistical analysis on one or more data the classification area of a data set, performs numerical statistical analysis on one or more sets, and calculates a single output value for each region in the input region data set. The data sets, and calculates a single output value for each region in the input region data set. The output re output results sults can can be be r raster aster data data or or t tables. ables. Figure 10. Figure 10. Gr Grid id data data superposi superposition tion principl principle e diagram. diagram. 5.2. Geometric Partition of the Subway Construction Environmental Risk Sources 5.2. Geometric Partition of the Subway Construction Environmental Risk Sources In the fourth chapter, we used the partitioning principle in Section 5.1 and the method In the fourth chapter, we used the partitioning principle in Section 5.1 and the described in Table 6 to calculate the spatial distribution grid layer for each risk source, method described in Table 6 to calculate the spatial distribution grid layer for each risk a process referred to as geometric partitioning [54]. After partitioning, the study area source, a process referred to as geometric partitioning [54]. After partitioning, the study was divided into 102 zones, and a map of the environmental risk sources for subway area was divided into 102 zones, and a map of the environmental risk sources for subway construction was created (Figure 11). The map reveals that the largest partitioned area in the study area is 1444, indicating that the environmental risks associated with subway construction in this area include soil layer risk, the risk from grade IV super high-rise buildings, the risk from grade IV road networks, and the risk from grade IV building den- sity. Figure 12 shows the areas of statistical significance greater than 2500 km and the percentage of the study area. Table 6. Geometric divisions made on the basis of environmental risk sources associated with the subway construction process in the study area. High-Rise Building Building Density Road Network Density Type Geological Condition Risk Risk Risk Level 1 Soil horizon I I I Level 2 Rock layer II II II Level 3 Earth-rock composite layer III III III Level 4 IV IV IV Partition number Kilobit hundred’s place ten’s place units Appl. Sci. 2023, 13, 5831 13 of 18 construction was created (Figure 11). The map reveals that the largest partitioned area in the study area is 1444, indicating that the environmental risks associated with subway construction in this area include soil layer risk, the risk from grade IV super high-rise buildings, the risk from grade IV road networks, and the risk from grade IV building density. Figure 12 shows the areas of statistical significance greater than 2500 km and the percentage of the study area. Table 6. Geometric divisions made on the basis of environmental risk sources associated with the subway construction process in the study area. Road Network Type Geological Condition High-Rise Building Risk Building Density Risk Density Risk Level 1 Soil horizon I I I Level 2 Rock layer II II II Level 3 Earth-rock composite layer III III III Level 4 IV IV IV Appl. Sci. 2023, 13, x FOR PEER REVIEW 13 of 17 Appl. Sci. 2023, 13, x FOR PEER REVIEW 13 of 17 Partition number Kilobit hundred’s place ten’s place units Figure 11. Geometric zoning map of the environmental risk sources associated with the process of Figure 11. Geometric zoning map of the environmental risk sources associated with the process of Figure 11. Geometric zoning map of the environmental risk sources associated with the process of subway construction in the study area. subway construction in the study area. subway construction in the study area. Figure 12. Risk source partition area and percentage of the partition area. Figure 12. Risk source partition area and percentage of the partition area. Figure 12. Risk source partition area and percentage of the partition area. 5.3. Environmental Risk Source Zoning Evaluation and Control of Subway Construction 5.3. Environmental Risk Source Zoning Evaluation and Control of Subway Construction We redefined the risk level of each partition area to accurately depict the comprehen- We redefined the risk level of each partition area to accurately depict the comprehen- sive influence of each risk source on the environmental risk zoning of subway construc- sive influence of each risk source on the environmental risk zoning of subway construc- tion in Urumqi. We first obtained the risk assessment value by calculating the weighted tion in Urumqi. We first obtained the risk assessment value by calculating the weighted average of the risk scores in each partition area. We then defined areas with risk assess- average of the risk scores in each partition area. We then defined areas with risk assess- ment values in the range of [3.75, 2.5), [2.5, 1.75), and [1.75, 1.25) as low-risk, medium-risk, ment values in the range of [3.75, 2.5), [2.5, 1.75), and [1.75, 1.25) as low-risk, medium-risk, and high-risk areas, respectively. High-risk areas with three or more class I risk sources and high-risk areas, respectively. High-risk areas with three or more class I risk sources were defined as level I high-risk areas, medium-risk areas with two or more level I risk were defined as level I high-risk areas, medium-risk areas with two or more level I risk sources were defined as level I medium-risk areas, and low-risk areas with one or more sources were defined as level I medium-risk areas, and low-risk areas with one or more level I risk sources were defined as level I low-risk areas. Based on their risk assessment level I risk sources were defined as level I low-risk areas. Based on their risk assessment values, the remaining partition areas were defined as II high-risk areas, II medium-risk values, the remaining partition areas were defined as II high-risk areas, II medium-risk areas, and II low-risk areas (Figure 13). areas, and II low-risk areas (Figure 13). Appl. Sci. 2023, 13, 5831 14 of 18 5.3. Environmental Risk Source Zoning Evaluation and Control of Subway Construction We redefined the risk level of each partition area to accurately depict the comprehen- sive influence of each risk source on the environmental risk zoning of subway construction in Urumqi. We first obtained the risk assessment value by calculating the weighted average of the risk scores in each partition area. We then defined areas with risk assessment values in the range of [3.75, 2.5), [2.5, 1.75), and [1.75, 1.25) as low-risk, medium-risk, and high-risk areas, respectively. High-risk areas with three or more class I risk sources were defined as level I high-risk areas, medium-risk areas with two or more level I risk sources were defined as level I medium-risk areas, and low-risk areas with one or more level I risk sources were defined as level I low-risk areas. Based on their risk assessment values, the remaining Appl. Sci. 2023, 13, x FOR PEER REVIEW 14 of 17 partition areas were defined as II high-risk areas, II medium-risk areas, and II low-risk areas (Figure 13). Figure 13. Risk index and classification table for the subway construction risk source zones. The Figure 13. Risk index and classification table for the subway construction risk source zones. The higher the index, the lower the comprehensive risk level of the partition. In other words, the com- higher the index, the lower the comprehensive risk level of the partition. In other words, the prehensive impact of the risk source in the region on subway construction is small. comprehensive impact of the risk source in the region on subway construction is small. We intersected the planned spatial location of Urumqi Metro Line 1–7 with the geo- We intersected the planned spatial location of Urumqi Metro Line 1–7 with the geo- metric partition layer of the subway construction risk source and combined the risk index metric partition layer of the subway construction risk source and combined the risk index table to statistically analyze the situation of railway lines crossing the risk area (Table 7). table to statistically analyze the situation of railway lines crossing the risk area (Table 7). According to Table 8, the comprehensive risk degree of the main crossing risk areas of According to Table 8, the comprehensive risk degree of the main crossing risk areas of Metro Line 1–7 is small. However, all lines cross the high-risk zones, the most significant Metro Line 1–7 is small. However, all lines cross the high-risk zones, the most significant of of which is Metro Line 6. There are six high-risk areas. which is Metro Line 6. There are six high-risk areas. Table 7. Statistical analysis of the Urumqi Metro Line 1–7 across the subway construction risk source Table 7. Statistical analysis of the Urumqi Metro Line 1–7 across the subway construction risk zones. source zones. Metro Main Crossing The Highest Level of Risk Risk Index The Highest Level of Regional Number Risk Index Metro Lines Main Crossing Partition Risk Index Regional Number Risk Index Lines Partition Zoning Risk Zoning Line 1 1432 2.25 1113 1 1.5 Line 1 1432 2.25 1113 1 1.5 Line 2 3433 3.25 1312 2 1.75 Line 2 3433 3.25 1312 2 1.75 LiLine ne 3 3 2433 2433 3 3 1122 1122 2 2 1.5 1.5 Line 4 3424 3.25 1312 1 1.75 Line 4 3424 3.25 1312 1 1.75 Line 5 1444 3.25 1232 1 2 Line 5 1444 3.25 1232 1 2 Line 6 1433 2.75 1212 5 1.5 Line 6 1433 2.75 1212 5 1.5 Line 7 3444 3.75 1222 3 1.75 Line 7 3444 3.75 1222 3 1.75 Table 8. Risk prevention measures taken for each risk zone. Risk Index Zoning Risk Level Explanation Interval Preventive measures must be implemented for various risk sources. The I category high-risk region settlement performance of the area must be strictly monitored during [1.25,1.75) construction. II category high-risk region Preventive measures must be implemented for each level I risk source. I category middle-risk region Preventive measures should be identified and implemented for each [1.75,2.50) II category middle-risk region level I risk source to reduce risk levels The risk is on the edge of tolerance, and it is necessary to pay attention to I category low-risk region [2.50,3.75) the possible impact of each level of risk source II category low-risk region The risk can be tolerated without taking preventive measures Appl. Sci. 2023, 13, 5831 15 of 18 Table 8. Risk prevention measures taken for each risk zone. Zoning Risk Level Risk Index Interval Explanation Preventive measures must be implemented for various risk sources. I category high-risk region The settlement performance of the area must be strictly monitored [1.25, 1.75) during construction. II category high-risk region Preventive measures must be implemented for each level I risk source. I category middle-risk region Preventive measures should be identified and implemented for each [1.75, 2.50) level I risk source to reduce risk levels II category middle-risk region The risk is on the edge of tolerance, and it is necessary to pay attention I category low-risk region to the possible impact of each level of risk source [2.50, 3.75) II category low-risk region The risk can be tolerated without taking preventive measures 6. Conclusions We employed SBAS-InSAR technology to analyze the surface subsidence performance of different areas during the construction of the Urumqi Metro Line 1 and identified the main risk sources of the subway construction environment in Urumqi. We used ArcGIS software to visualize the spatial distribution of each risk source and classify their risk levels. We then carried out a geometric superposition of the spatial distribution layers of each risk source to enable the geometric division of the environmental risk sources of subway construction in the study area. Finally, we redefined the risk index of each partition to draw the following conclusions: (1) Four main types of risk sources affect the process of Urumqi subway construction, and these can be classified into natural conditions and human engineering activities. Each risk source control level can be divided into geological conditions (I–III) based on their spatial distribution and spatial density distribution. Additionally, the distri- bution of super high-rise buildings can be classified into levels I–IV, the road network distribution can be classified into levels I–IV, and the cluster building distribution can be classified into levels I–IV. (2) The environmental risk source area of subway construction in the study area is divided into 103 regions, with 12 areas having an area greater than 250,000 km . The largest partition number is 1444, and the partition number with the greatest risk is 1112. The environmental risk source and risk level of subway construction in this area are soil layer, grade I super high-rise building risk, grade I road network risk, and grade II building density risk. (3) We graded the comprehensive impact of risk sources on subway construction ac- cording to the distribution of risk sources in each sub-region. We also conducted a statistical analysis of Urumqi Metro Line 1–7 crossing the risk source zones and identified the risk control measures needed when the subway line crosses different risk levels. (4) In the future planning and construction of the Urumqi subway line, identifying the environmental risk sources of subway construction will assist the planning depart- ment in understanding the environmental risk situation surrounding the subway planning line. This information will provide a foundation for the rational planning and adjustment of regional subway lines. The subway construction company will be able to determine the environmental risk level of the subway’s construction and take necessary risk control measures in advance based on the intersection of the actual subway line location and the environmental risk source zoning layer. This will help to mitigate the risk of inconsistency between the judgment results of engineering design drawings and engineering practice due to insufficient consideration of risk factors during the planning and design stage. Appl. Sci. 2023, 13, 5831 16 of 18 Author Contributions: Conceptualization, Y.Y. and Y.Q.; methodology, Y.Y. and L.X.; software, Y.Y. and Y.Z.; validation, Y.Y. and X.M.; formal analysis, Y.Y. and Z.G.; investigation, Y.Y. and Y.Z.; resources, Y.Q. and L.X.; data curation, Y.Y. and Y.Z.; writing—original draft preparation, Y.Y.; writing—review and editing, Y.Y. and Y.Z.; visualization, Y.Q. and Y.Z.; supervision, Y.Q.; project administration, Y.Q.; funding acquisition, Y.Q. All authors have read and agreed to the published version of the manuscript. Funding: This study was financially supported by the Natural Science Foundation of Xinjiang Province (Grant No. 2021D01C073). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data generated and analyzed during this study are included in this article. Conflicts of Interest: The authors declare no conflict of interest. References 1. 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Journal
Applied Sciences – Multidisciplinary Digital Publishing Institute
Published: May 9, 2023
Keywords: subway construction; risk source; distribution characteristics; comprehensive impact zoning