Integrating analytical and machine learning approaches to simulate and predict dam foundation stress and river valley contraction in a large-scale reservoir
{"title":"Integrating analytical and machine learning approaches to simulate and predict dam foundation stress and river valley contraction in a large-scale reservoir","authors":"Ziwen Zhou, Zhifang Zhou, Sai K. Vanapalli","doi":"10.1007/s10064-024-03941-1","DOIUrl":null,"url":null,"abstract":"<div><p>The safety of several large-scale reservoirs all over the world has been of concern due to dam foundation stress (DFS) that gradually changes following impoundment inducing the river valley contraction (RVC). Presently, there are limited approaches for the prediction of DFS and RVC based on complex hydro-geomechanics principles. However, these approaches require extensive information that is cumbersome and time-consuming to gather and hence expensive. In this paper, five machine learning models (MLMs) for DFS and RVC prediction were established by merging innovative analytical, BP neural networks and optimized algorithm approaches. Three key influencing factors; namely: seepage, temperature, and creep are used as input information in these models. The developed MLMs were validated using well-documented case study results over nine years for Xiluodu reservoir in China. The trend-fitting effect and statistical indicators of the proposed MLMs demonstrated strong predictive ability (R<sup>2</sup> > 0.9). Among the MLMs, Generic algorithm-BP and Sparrow search algorithm-BP methods were found to be comprehensive. The predicted RVC and DFS using MLMs are consistent with the coupled multi-field analytical method from the literature and provide reliable predictions using limited information. This study serves as a valuable reference for predicting DFS and RVC of large reservoirs for ensuring long-term safety.\n</p></div>","PeriodicalId":500,"journal":{"name":"Bulletin of Engineering Geology and the Environment","volume":"83 11","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Engineering Geology and the Environment","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10064-024-03941-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
The safety of several large-scale reservoirs all over the world has been of concern due to dam foundation stress (DFS) that gradually changes following impoundment inducing the river valley contraction (RVC). Presently, there are limited approaches for the prediction of DFS and RVC based on complex hydro-geomechanics principles. However, these approaches require extensive information that is cumbersome and time-consuming to gather and hence expensive. In this paper, five machine learning models (MLMs) for DFS and RVC prediction were established by merging innovative analytical, BP neural networks and optimized algorithm approaches. Three key influencing factors; namely: seepage, temperature, and creep are used as input information in these models. The developed MLMs were validated using well-documented case study results over nine years for Xiluodu reservoir in China. The trend-fitting effect and statistical indicators of the proposed MLMs demonstrated strong predictive ability (R2 > 0.9). Among the MLMs, Generic algorithm-BP and Sparrow search algorithm-BP methods were found to be comprehensive. The predicted RVC and DFS using MLMs are consistent with the coupled multi-field analytical method from the literature and provide reliable predictions using limited information. This study serves as a valuable reference for predicting DFS and RVC of large reservoirs for ensuring long-term safety.
期刊介绍:
Engineering geology is defined in the statutes of the IAEG as the science devoted to the investigation, study and solution of engineering and environmental problems which may arise as the result of the interaction between geology and the works or activities of man, as well as of the prediction of and development of measures for the prevention or remediation of geological hazards. Engineering geology embraces:
• the applications/implications of the geomorphology, structural geology, and hydrogeological conditions of geological formations;
• the characterisation of the mineralogical, physico-geomechanical, chemical and hydraulic properties of all earth materials involved in construction, resource recovery and environmental change;
• the assessment of the mechanical and hydrological behaviour of soil and rock masses;
• the prediction of changes to the above properties with time;
• the determination of the parameters to be considered in the stability analysis of engineering works and earth masses.