A Genetic Algorithm Optimization Model for Stability of an Inclined Cutoff with Soil-Embedded Depth

Q3 Environmental Science
Rafea Al-Suhili, R. Karim
{"title":"A Genetic Algorithm Optimization Model for Stability of an Inclined Cutoff with Soil-Embedded Depth","authors":"Rafea Al-Suhili, R. Karim","doi":"10.25130/tjes.30.2.4","DOIUrl":null,"url":null,"abstract":"A coupled artificial neural network model with a genetic algorithm optimization model is developed for a practical case of a single cutoff. The proposed cutoff is of a soil-embedded vertical part with an inclined extension. The model successfully found the optimum dimensions of the vertical and inclined parts, the optimum angle of inclination, and the optimum length of protection downstream of the cutoff for a factor of safety of 3 against piping. Two thousand one hundred cases are modeled first using Geo-studio software to find the required length of downstream protection against piping for different lengths of the vertical, inclined lengths of the cutoff, its angle of inclination, soil layer depth, and degree of anisotropy. Then the created data set was used to develop an Artificial Neural Network (ANN) model for finding the length of protection required. The ANN model showed high performance with a determination coefficient of (0.922). The genetic algorithm model needs a minimum number of randomly generated populations of 100000 and three crossover iterations to produce a stable optimum solution. Running the model for different practical cases showed that the optimum angle variation was low and fluctuated around 30o. This low angle variation was due to its lower effect on the downstream soil protection length compared to the other decision variables. At the same time, the other dimensions varied with input variables, such as the depth of the soil layer, the seepage driving head, and the degree of isotropy. For a degree of anisotropy (ratio of vertical to horizontal hydraulic gradient) less than 0.5, the results showed no need for protection against piping; hence it is recommended to use minimum dimensions for such a case. The coupled model can easily obtain the optimum dimensions for any given input. Importance analysis showed that the optimum length of the downstream protection was highly affected by the vertical and inclined length of the cutoff, while it was less affected by the angle of inclination. Correlation analysis supported the importance analysis.\n ","PeriodicalId":30589,"journal":{"name":"Tikrit Journal of Engineering Sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tikrit Journal of Engineering Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25130/tjes.30.2.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 1

Abstract

A coupled artificial neural network model with a genetic algorithm optimization model is developed for a practical case of a single cutoff. The proposed cutoff is of a soil-embedded vertical part with an inclined extension. The model successfully found the optimum dimensions of the vertical and inclined parts, the optimum angle of inclination, and the optimum length of protection downstream of the cutoff for a factor of safety of 3 against piping. Two thousand one hundred cases are modeled first using Geo-studio software to find the required length of downstream protection against piping for different lengths of the vertical, inclined lengths of the cutoff, its angle of inclination, soil layer depth, and degree of anisotropy. Then the created data set was used to develop an Artificial Neural Network (ANN) model for finding the length of protection required. The ANN model showed high performance with a determination coefficient of (0.922). The genetic algorithm model needs a minimum number of randomly generated populations of 100000 and three crossover iterations to produce a stable optimum solution. Running the model for different practical cases showed that the optimum angle variation was low and fluctuated around 30o. This low angle variation was due to its lower effect on the downstream soil protection length compared to the other decision variables. At the same time, the other dimensions varied with input variables, such as the depth of the soil layer, the seepage driving head, and the degree of isotropy. For a degree of anisotropy (ratio of vertical to horizontal hydraulic gradient) less than 0.5, the results showed no need for protection against piping; hence it is recommended to use minimum dimensions for such a case. The coupled model can easily obtain the optimum dimensions for any given input. Importance analysis showed that the optimum length of the downstream protection was highly affected by the vertical and inclined length of the cutoff, while it was less affected by the angle of inclination. Correlation analysis supported the importance analysis.  
含埋深倾斜路堑稳定性的遗传算法优化模型
针对单个截断的实际情况,建立了人工神经网络与遗传算法的耦合优化模型。拟建截水沟为土壤嵌入的垂直部分,具有倾斜延伸部分。该模型成功地找到了垂直和倾斜部分的最佳尺寸、最佳倾斜角度和截流下游的最佳保护长度,针对管道的安全系数为3。首先使用Geo studio软件对2100个案例进行建模,以找到不同垂直长度、倾斜截水长度、倾斜角度、土层深度和各向异性程度的管道下游保护所需长度。然后,使用创建的数据集来开发人工神经网络(ANN)模型,以找到所需的保护长度。神经网络模型具有较高的性能,决定系数为(0.922)。遗传算法模型需要最少100000个随机生成的种群和三次交叉迭代才能产生稳定的最优解。模型在不同的实际情况下运行表明,最佳角度变化较小,在30°左右波动。这种低角度变化是由于与其他决策变量相比,其对下游土壤保护长度的影响较小。同时,其他维度随着输入变量的变化而变化,如土层深度、渗流驱动水头和各向同性程度。对于各向异性程度(垂直与水平水力梯度之比)小于0.5的情况,结果表明不需要对管道进行保护;因此建议在这种情况下使用最小尺寸。耦合模型可以很容易地获得任何给定输入的最佳尺寸。重要性分析表明,下游保护的最佳长度受截水垂直和倾斜长度的影响较大,而受倾斜角度的影响较小。相关性分析支持重要性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.50
自引率
0.00%
发文量
56
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信