首页|A modified back analysis method for deep excavation with multi-objective optimization procedure
A modified back analysis method for deep excavation with multi-objective optimization procedure
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization(MOPSO)algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter opti-mization and real-time prediction of system behavior,the back propagation neural network(BPNN)is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90％.Moreover,different optimization al-gorithms,including non-dominated sorting genetic algorithm(NSGA-Ⅱ),Pareto Envelope-based Selec-tion Algorithm Ⅱ(PESA-Ⅱ)and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.
Chenyang Zhao、Le Chen、Pengpeng Ni、Wenjun Xia、Bin Wang
展开 >
State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,430071,China
Guangdong Research Center for Underground Space Exploitation Technology,School of Civil Engineering,Sun Yat-sen University,Guangzhou,510275,China
Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai,519000,China
Jiangsu Provincial Transportation Engineering Construction Bureau,Nanjing,210004,China
展开 >
National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaOpen Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics
NETL
NSTL
万方数据
