Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

被引:41
|
作者
Zhang, Xuan [1 ,2 ,3 ]
Zhu, Chun [4 ,5 ]
He, Manchao [4 ,5 ]
Dong, Menglong [4 ]
Zhang, Guangcheng [6 ]
Zhang, Faming [4 ]
机构
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China
[2] Hohai Univ, Coll Harbour Costal & Offshore Engn, Nanjing 210098, Peoples R China
[3] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian 116081, Peoples R China
[4] Hohai Univ, Inst Engn Geol & Geohazards, Nanjing 210098, Peoples R China
[5] State Key Lab Geomech & Deep Underground Engn, Beijing 100083, Peoples R China
[6] China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
step moving rockslide; long short-term memory neural network; joint persistence ratio; deformation forecasting; hydrodynamic action; DISPLACEMENT PREDICTION; STABILITY ANALYSIS; SLOPE; AREA; EXCAVATION; ROCKSLIDES; MACHINE; LATTICE; COUNTY; CHINA;
D O I
10.3390/rs14010166
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.
引用
收藏
页数:27
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