Use of Machine Learning to Predict California Bearing Ratio of Soils

Author:

Kassa Semachew Molla12ORCID,Wubineh Betelhem Zewdu34ORCID

Affiliation:

1. Faculty of Civil and Water Resource Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia

2. Department of Civil Engineering, College of Engineering and Technology, Wachemo University, Hosaena, Ethiopia

3. Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland

4. Department of Information Technology, College of Engineering and Technology, Wachemo University, Hosaena, Ethiopia

Abstract

CBR is a crucial metric used to assess the durability of base course materials and subgrade soils in various types of pavements. In this research, the machine learning (ML) approach has been implemented using random forest (RF), decision tree (DT), linear regression (LR), and artificial neural network (ANN) models to estimate CBR (California bearing ratio) values of the soil based on seven predictors such as maximum dry density, soil classification, optimum moisture content, liquid limit, plastic limit, plastic index, and swell, which can be easily determined from the laboratory. AASHTO M 145 was used to categorize 252 soil samples that formed the basis of an experimental data set. In this model study, the data were split into 20% test data and 80% training data. Standard statistical measures including coefficient of determination, correlations, and errors were used to assess the effectiveness of the models such as MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean square error). From these evaluation metrics, the random forest algorithm gets a smaller error and larger relative error (R2) value to compare with other algorithms. Therefore, it can be concluded that a random forest algorithm based on the analysis findings can accurately forecast the soil’s CBR.

Publisher

Hindawi Limited

Subject

Civil and Structural Engineering

Reference23 articles.

1. Evaluation of soaked and unsoaked CBR values of soil based on the compaction characteristics;S. M. Lakshmi;Malaysian Journal of Civil Engineering,2016

2. Estimation of California bearing ratio by using soft computing systems

3. Modeling of California bearing ratio using basic engineering properties;S. Taha

4. Prediction of California bearing ratio of a stabilized expansive soil using artificial neural network and support vector machine;A. K. Sabat;Electronic Journal of Geotechnical Engineering,2015

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3