# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 3 Hazard Assessment Results for the Study Area
### Construction of Hazard Assessment Index System for Ground Subsidence
#### 3.1.2 Engineering Factors  
The engineering factors predominantly influencing the ground collapse hazard in the study area include the road load classification, road network density, collapse zones, and goat areas. (1) Road Load Classification  
The road vehicle loads were qualitatively analyzed based on road classification, with higher road classifications indicating greater vehicle loads. Utilizing ArcGIS, the main roads, secondary roads, and other supporting roads in the study area were extracted  
Figure 14: Distance from study area to water. (**a**) Distribution. (**b**) Classification. Figure 13: Distance from study area to fault zone. (**a**) Distribution. (**b**) Classification. \begin{table}
\begin{tabular}{c c} \hline \hline
**Distance to Water** & **Value** \\ \hline \(>\)300 m & 1 \\
200–300 m & 2 \\
100–200 m & 3 \\
0–100 m & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Assigned values of distance to water. and assigned load values. Main roads were assigned a value of 5, secondary roads were assigned a value of 3, and other supporting roads were assigned a value of 1. The road load classification was subsequently merged, and a kernel density analysis was employed to categorize the road load levels, as depicted in Figure 15.  
(2) Road Network Density in the Study Area  
The road network within the study area was extracted by intersecting the previously downloaded Tangshan City road network with the administrative boundaries of the study area using geoprocessing tools. A kernel density analysis was performed on the road network, as illustrated in Figure 16.  
This analysis calculated the total length of the polylines within each 30 m \(\times\) 30 m raster cell. The road network density was determined via the ratio of the total length of roads within the raster cells to the cell area. The road network density was classified into five categories using natural breaks, as detailed in Table 10.  
Figure 16: Road network density of study area. (**a**) Road network density distribution. (**b**) Road network density classification. Figure 15: Road load of study area. (**a**) Road load distribution. (**b**) Road load classification. (3) Collapse Zones in the Main Urban Area  
The distribution of karst collapse zones is directly related to ground collapse. Based on the distribution of multiple collapse points in the urban area, it is evident that most collapse points are clustered along fault zones. Karst development leads to the formation of underground karst cavities, which can easily result in collapse. The karst collapse zones in the study area are predominantly located in the central and northern regions. Karst collapses are closely related to groundwater levels and are mostly situated within the subsidence funnels caused by the groundwater lowering. According to the \"Tangshan City Comprehensive Disaster Prevention and Reduction Detailed Plan (2016-2020)\", the distribution of collapses in the study area was obtained, as shown in the diagram. The collapses were classified into four severity levels and assigned corresponding values, as illustrated in Figure 17.  
(4) Goaf Areas in the Study Area  
Two significant goat collapse areas are located in the northern part of the study area, within the jurisdiction of Kailuan District. Another larger goat area is situated in Tangshan Nanhu Park. The mining-induced collapses in these areas result in uneven ground surfaces and large underground goal cavities. If the overlying soil layer becomes unstable, then this can easily trigger ground collapse. Therefore, goat areas were selected as an indicator for assessing the ground collapse hazard in the urban area. The study area was divided into goat areas and non-goaf areas, which were assigned values of 2 and 1, respectively, as shown in Figure 18. \begin{table}
\begin{tabular}{c c} \hline \hline
**Road Network Density (m\({}^{-1}\))** & **Value** \\ \hline
0–10.86 & 1 \\
10.86–21.39 & 2 \\
21.39–34.56 & 3 \\
34.56–49.38 & 4 \\ \textgreater{}49.38 & 5 \\ \hline \hline \end{tabular}
\end{table}