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Settlement Prediction for a Metro Tunnel Adjacent to a Deep Large-Scale Foundation Pit
(1 Zhejiang Design Institute of Water Conservancy and Hydroelectric Power, Hangzhou 310002; 2 Zhejiang Electric Power Design Institute, Hangzhou 310012;3 School of Earth Sciences and Engineering, HoHai University, Nanjing 210098)
Abstract Due to the complex factors of and strict requirements for deformation control, it is difficult to accurately predict the settlement of a metro tunnel adjacent to a deep large-scale foundation pit. In this paper, the Support Vector Machine (SVM) theory is introduced for its excellent prediction capabilities regarding small sample, complex, and nonlinear data, and Ant Colony Optimization (ACO) was adopted to obtain an optimized SVM prediction model by selecting the most appropriate parameter combination. Based on the settlement prediction of a tunnel on Nanjing Metro Line 1, this model meets the requirements of settlement prediction for metro tunnels adjacent to deep large-scale foundation pits because of its sound prediction accuracy and accurate reflection of deformation trends.
Abstract:
Due to the complex factors of and strict requirements for deformation control, it is difficult to accurately predict the settlement of a metro tunnel adjacent to a deep large-scale foundation pit. In this paper, the Support Vector Machine (SVM) theory is introduced for its excellent prediction capabilities regarding small sample, complex, and nonlinear data, and Ant Colony Optimization (ACO) was adopted to obtain an optimized SVM prediction model by selecting the most appropriate parameter combination. Based on the settlement prediction of a tunnel on Nanjing Metro Line 1, this model meets the requirements of settlement prediction for metro tunnels adjacent to deep large-scale foundation pits because of its sound prediction accuracy and accurate reflection of deformation trends.
CHEN Xi-Feng-1,
LIU Ling-2,
HUANG Teng-3
.Settlement Prediction for a Metro Tunnel Adjacent to a Deep Large-Scale Foundation Pit[J] MODERN TUNNELLING TECHNOLOGY, 2014,V51(6): 94-100