Predictive Modeling of Surface Subsidence Considering Different Environmental Risk Zones - Yunsong Li; Yongjun Qin; Liangfu Xie; Yangchun Yuan; Jie Ran (Advances in Civil Engineering)
Insufficient knowledge and control of the surrounding environmental risks in the process of subway tunnel construction will lead to different degrees of surface settlement during construction, and it is of practical significance to grasp the impact of environmental risks on surface settlement and make predictions on this basis. There is a close connection between the surrounding environment of subway tunnel construction and the settlement monitoring data, and the high precision prediction of surface settlement through the surrounding environment before subway tunnel construction can guarantee the safety of subway tunnel construction. Therefore, this paper proposes a surface settlement prediction model based on environmental risk zoning. Establish the integrated impact zoning of underground mining environmental risk through spatial superposition and risk quantification, and divide the construction environment into high-risk, middle-risk, and low-risk region. Adopt four different noise reduction algorithms for data noise reduction on the raw data of the monitoring points at the intervals of different risk zones, and combine the time series prediction as well as the deep learning prediction method to get the prediction model for environmental risk zoning based on the environmental risk zoning. The monitoring data of Urumqi Metro Line 1 is analyzed as an example, and the suitable combination of prediction models for each region under the environmental risk zoning of the subway tunnel construction is obtained: high-risk region (singular value decomposition (SVD) + long and short-term memory (LSTM) neural network); middle-risk region (wavelet transform/Kalman filter + back propagation neural network (BPNN)); and low-risk region (mean filtering + autoregressive integrated moving average model (ARIMA)).