{"title":"基于人工神经网络和差分演化的边坡稳定性评价","authors":"V. T. Vu","doi":"10.2478/cee-2023-0026","DOIUrl":null,"url":null,"abstract":"Abstract This study aims for two purposes: firstly, using the Differential Evolution method combined with limit equilibrium methods to find the factor of safety of a variety of different configurations of slopes and soil parameters. Two patterns of the embankments are assessed, a one-layer soil pattern with 540 cases and a two-layer soil pattern with 24300 cases. Secondly, using these data to train and test an artificial neural network for predicting the factor of safety of slopes. The experimental data and values predicted by the artificial neural network correlate well with a linear coefficient of correlation of around 0.99. Given large enough training data, the proposed approach shows its reliability in quick evaluation of the slope stability without a long process of searching for a critical slip surface.","PeriodicalId":42034,"journal":{"name":"Civil and Environmental Engineering","volume":"19 1","pages":"288 - 300"},"PeriodicalIF":1.1000,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of Slope Stability with the Assistance of Artificial Neural Network and Differential Evolution\",\"authors\":\"V. T. Vu\",\"doi\":\"10.2478/cee-2023-0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aims for two purposes: firstly, using the Differential Evolution method combined with limit equilibrium methods to find the factor of safety of a variety of different configurations of slopes and soil parameters. Two patterns of the embankments are assessed, a one-layer soil pattern with 540 cases and a two-layer soil pattern with 24300 cases. Secondly, using these data to train and test an artificial neural network for predicting the factor of safety of slopes. The experimental data and values predicted by the artificial neural network correlate well with a linear coefficient of correlation of around 0.99. Given large enough training data, the proposed approach shows its reliability in quick evaluation of the slope stability without a long process of searching for a critical slip surface.\",\"PeriodicalId\":42034,\"journal\":{\"name\":\"Civil and Environmental Engineering\",\"volume\":\"19 1\",\"pages\":\"288 - 300\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Civil and Environmental Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/cee-2023-0026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Civil and Environmental Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cee-2023-0026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Assessment of Slope Stability with the Assistance of Artificial Neural Network and Differential Evolution
Abstract This study aims for two purposes: firstly, using the Differential Evolution method combined with limit equilibrium methods to find the factor of safety of a variety of different configurations of slopes and soil parameters. Two patterns of the embankments are assessed, a one-layer soil pattern with 540 cases and a two-layer soil pattern with 24300 cases. Secondly, using these data to train and test an artificial neural network for predicting the factor of safety of slopes. The experimental data and values predicted by the artificial neural network correlate well with a linear coefficient of correlation of around 0.99. Given large enough training data, the proposed approach shows its reliability in quick evaluation of the slope stability without a long process of searching for a critical slip surface.