采用稳健的数据驱动模型来评估安装灌浆柱对环境造成的损害

M. Hameed, Faidhalrahman Khaleel, Deiaaldeen Khaleel
{"title":"采用稳健的数据驱动模型来评估安装灌浆柱对环境造成的损害","authors":"M. Hameed, Faidhalrahman Khaleel, Deiaaldeen Khaleel","doi":"10.1109/IEEECONF53624.2021.9668027","DOIUrl":null,"url":null,"abstract":"The jet grouting process involves injecting large quantities of highly pressurized fluids into the soil, which may result in a substantial ground displacement and adverse effects on the environment around the excavation. Consequently, the ground displacement must be estimated accurately in the design phase. In this study, two machine learning models namely, extreme learning machine (ELM) and modified K-nearest neighbor (KNN) are used to estimate the ground displacements. The comparison results show that the ELM is superior to the KNN model in terms of estimation accuracy (coefficient of determination is 0.940). Moreover, the ELM model shows an enhancement by 11.43% higher accuracy in terms of reducing the mean absolute error compared to the KNN model. Overall, the results indicate that ELM has the ability to accurately assess the harmful damages caused by installing grouted columns.","PeriodicalId":389608,"journal":{"name":"2021 Third International Sustainability and Resilience Conference: Climate Change","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Employing a robust data-driven model to assess the environmental damages caused by installing grouted columns\",\"authors\":\"M. Hameed, Faidhalrahman Khaleel, Deiaaldeen Khaleel\",\"doi\":\"10.1109/IEEECONF53624.2021.9668027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The jet grouting process involves injecting large quantities of highly pressurized fluids into the soil, which may result in a substantial ground displacement and adverse effects on the environment around the excavation. Consequently, the ground displacement must be estimated accurately in the design phase. In this study, two machine learning models namely, extreme learning machine (ELM) and modified K-nearest neighbor (KNN) are used to estimate the ground displacements. The comparison results show that the ELM is superior to the KNN model in terms of estimation accuracy (coefficient of determination is 0.940). Moreover, the ELM model shows an enhancement by 11.43% higher accuracy in terms of reducing the mean absolute error compared to the KNN model. Overall, the results indicate that ELM has the ability to accurately assess the harmful damages caused by installing grouted columns.\",\"PeriodicalId\":389608,\"journal\":{\"name\":\"2021 Third International Sustainability and Resilience Conference: Climate Change\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Third International Sustainability and Resilience Conference: Climate Change\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEECONF53624.2021.9668027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Third International Sustainability and Resilience Conference: Climate Change","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEECONF53624.2021.9668027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

喷射注浆是向土体中注入大量高压流体的过程,可能会导致土体发生较大位移,对基坑周围环境造成不利影响。因此,在设计阶段必须准确地估计地面位移。本研究采用极限学习机(ELM)和改进k近邻(KNN)两种机器学习模型来估计地面位移。比较结果表明,ELM的估计精度优于KNN模型(决定系数为0.940)。此外,与KNN模型相比,ELM模型在降低平均绝对误差方面的精度提高了11.43%。综上所述,ELM能够准确地评估注浆柱的有害损害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing a robust data-driven model to assess the environmental damages caused by installing grouted columns
The jet grouting process involves injecting large quantities of highly pressurized fluids into the soil, which may result in a substantial ground displacement and adverse effects on the environment around the excavation. Consequently, the ground displacement must be estimated accurately in the design phase. In this study, two machine learning models namely, extreme learning machine (ELM) and modified K-nearest neighbor (KNN) are used to estimate the ground displacements. The comparison results show that the ELM is superior to the KNN model in terms of estimation accuracy (coefficient of determination is 0.940). Moreover, the ELM model shows an enhancement by 11.43% higher accuracy in terms of reducing the mean absolute error compared to the KNN model. Overall, the results indicate that ELM has the ability to accurately assess the harmful damages caused by installing grouted columns.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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