{"title":"Predicting the unconfined compressive strength of stabilized soil using random forest coupled with meta-heuristic algorithms","authors":"Yan Li","doi":"10.1007/s12652-024-04857-0","DOIUrl":null,"url":null,"abstract":"<p>Unconfined Compressive Strength (UCS) is a crucial mechanical parameter of rocks, which is pivotal in developing accurate geomechanical models. Traditionally, UCS estimation involves expensive and time-consuming methods, such as lab testing of retrieved core samples or well-log data analysis. This research presents a novel approach for real-time estimation of UCS, crucial in various geomechanical applications. It employs Random Forest (RF) prediction models enhanced by Runge Kutta Optimization (RKO) and Beluga Whale Optimization (BWO) algorithms for improved accuracy and efficiency. Validation using UCS samples from diverse soil types yields three distinct models: RFRK (RF + RKO), RFBW (RF + BWO), and an individual RF model, each contributing valuable insights. The RFBW model particularly stands out with high R<sup>2</sup> values (0.994) and a favorable RMSE (73.93), indicating superior predictive and generalization capabilities. This method represents a significant advancement in UCS prediction, offering efficiency and time-saving benefits across geomechanical fields.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04857-0","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Unconfined Compressive Strength (UCS) is a crucial mechanical parameter of rocks, which is pivotal in developing accurate geomechanical models. Traditionally, UCS estimation involves expensive and time-consuming methods, such as lab testing of retrieved core samples or well-log data analysis. This research presents a novel approach for real-time estimation of UCS, crucial in various geomechanical applications. It employs Random Forest (RF) prediction models enhanced by Runge Kutta Optimization (RKO) and Beluga Whale Optimization (BWO) algorithms for improved accuracy and efficiency. Validation using UCS samples from diverse soil types yields three distinct models: RFRK (RF + RKO), RFBW (RF + BWO), and an individual RF model, each contributing valuable insights. The RFBW model particularly stands out with high R2 values (0.994) and a favorable RMSE (73.93), indicating superior predictive and generalization capabilities. This method represents a significant advancement in UCS prediction, offering efficiency and time-saving benefits across geomechanical fields.
期刊介绍:
The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to):
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Context awareness, social sensing and inference
Multi modal interaction design
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Intelligent and self-organizing transportation networks & services
Healthcare Systems
Virtual Humans & Virtual Worlds
Wearables sensors and actuators