{"title":"土坝边坡稳定性评价人工神经网络模型的理论分析与发展","authors":"R. Hussain, Asmaa Al-samarrae","doi":"10.25130/tjes.29.4.1","DOIUrl":null,"url":null,"abstract":"In the design of earth dams, it must be considered that the water leakage through the earth dam generates upward and pore pressure, in addition to leakage forces that cause internal erosion, which has a direct influence on the structural stability of this system. Also, the rising and dropping in the water level has a direct effect on the stability of the dam's face slope. One way to solve these issues is the installation of a core or a horizontal water drainage system. The present study relied on the GEO-Studio computer tool to evaluate cross-sectional models of earthen dams by determining the safety factor under different situations represented by a change in filter type, and the flow state as a result of raising and lowering the water level at the dam reservoir and the full fill condition of the dam reservoir. The research found that the existence of a core substantially contributed to improving the safety coefficient for the case of rising the water level (2m) and rapidly rising by assigning it the greatest safety coefficient values. The absence of a filter had an opposite influence on the safety coefficient by decreasing it. Also, the factor of safety for the downstream slope was affected by less than 5% for different flow conditions, compared with the higher effect generated by the upstream slope. Furthermore, an artificial neural network model with an accuracy ratio of more than 97% was developed for the predicted safety factor.","PeriodicalId":30589,"journal":{"name":"Tikrit Journal of Engineering Sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Theoretical Analysis and Development of an Artificial Neural Network Model to Evaluate Earthen Dam Slope Stability\",\"authors\":\"R. Hussain, Asmaa Al-samarrae\",\"doi\":\"10.25130/tjes.29.4.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the design of earth dams, it must be considered that the water leakage through the earth dam generates upward and pore pressure, in addition to leakage forces that cause internal erosion, which has a direct influence on the structural stability of this system. Also, the rising and dropping in the water level has a direct effect on the stability of the dam's face slope. One way to solve these issues is the installation of a core or a horizontal water drainage system. The present study relied on the GEO-Studio computer tool to evaluate cross-sectional models of earthen dams by determining the safety factor under different situations represented by a change in filter type, and the flow state as a result of raising and lowering the water level at the dam reservoir and the full fill condition of the dam reservoir. The research found that the existence of a core substantially contributed to improving the safety coefficient for the case of rising the water level (2m) and rapidly rising by assigning it the greatest safety coefficient values. The absence of a filter had an opposite influence on the safety coefficient by decreasing it. Also, the factor of safety for the downstream slope was affected by less than 5% for different flow conditions, compared with the higher effect generated by the upstream slope. Furthermore, an artificial neural network model with an accuracy ratio of more than 97% was developed for the predicted safety factor.\",\"PeriodicalId\":30589,\"journal\":{\"name\":\"Tikrit Journal of Engineering Sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tikrit Journal of Engineering Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25130/tjes.29.4.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tikrit Journal of Engineering Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25130/tjes.29.4.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
Theoretical Analysis and Development of an Artificial Neural Network Model to Evaluate Earthen Dam Slope Stability
In the design of earth dams, it must be considered that the water leakage through the earth dam generates upward and pore pressure, in addition to leakage forces that cause internal erosion, which has a direct influence on the structural stability of this system. Also, the rising and dropping in the water level has a direct effect on the stability of the dam's face slope. One way to solve these issues is the installation of a core or a horizontal water drainage system. The present study relied on the GEO-Studio computer tool to evaluate cross-sectional models of earthen dams by determining the safety factor under different situations represented by a change in filter type, and the flow state as a result of raising and lowering the water level at the dam reservoir and the full fill condition of the dam reservoir. The research found that the existence of a core substantially contributed to improving the safety coefficient for the case of rising the water level (2m) and rapidly rising by assigning it the greatest safety coefficient values. The absence of a filter had an opposite influence on the safety coefficient by decreasing it. Also, the factor of safety for the downstream slope was affected by less than 5% for different flow conditions, compared with the higher effect generated by the upstream slope. Furthermore, an artificial neural network model with an accuracy ratio of more than 97% was developed for the predicted safety factor.