{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T21:19:45Z","timestamp":1776287985984,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:00:00Z","timestamp":1640908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51909074"],"award-info":[{"award-number":["51909074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010031","name":"Postdoctoral Research Foundation of China","doi-asserted-by":"publisher","award":["2019M661713"],"award-info":[{"award-number":["2019M661713"]}],"id":[{"id":"10.13039\/501100010031","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the open fund of the State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology","award":["LP2105"],"award-info":[{"award-number":["LP2105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14010166","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuan","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"},{"name":"College of Harbour, Costal and Offshore Engineering, Hohai University, Nanjing 210098, China"},{"name":"State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116081, China"}]},{"given":"Chun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Engineering Geology and Geohazards, Hohai University, Nanjing 210098, China"},{"name":"State Key Laboratory for Geomechanics & Deep Underground Engineering, Beijing 100083, China"}]},{"given":"Manchao","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Engineering Geology and Geohazards, Hohai University, Nanjing 210098, China"},{"name":"State Key Laboratory for Geomechanics & Deep Underground Engineering, Beijing 100083, China"}]},{"given":"Menglong","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Engineering Geology and Geohazards, Hohai University, Nanjing 210098, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0130-5327","authenticated-orcid":false,"given":"Guangcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Faming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Engineering Geology and Geohazards, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.enggeo.2014.08.027","article-title":"Two and three-dimensional numerical analysis of the progressive failure that occurred in an excavation-induced landslide","volume":"183","author":"Troncone","year":"2014","journal-title":"Eng. 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