Machine learning techniques versus classical statistics in strength predictions of eco-friendly masonry units

A. A. Mahamat, Moussa Mahamat Boukar
{"title":"Machine learning techniques versus classical statistics in strength predictions of eco-friendly masonry units","authors":"A. A. Mahamat, Moussa Mahamat Boukar","doi":"10.1109/icecco53203.2021.9663760","DOIUrl":null,"url":null,"abstract":"Earth-based materials demonstrated promising characteristics in the development of eco-friendly, low cost and sustainable construction materials. However, their unconventional utilization in construction makes the assessment of their properties very difficult and inaccurate because they are assessed based on conventional materials procedures. Hence, the properties of earth-based materials are not well understood. The assessment of earth-based materials properties for sustainable construction is time-consuming, expensive, and inaccurate. To obtain more accurate properties, an artificial neural network and statistical linear regression analysis were used to predict the compressive strength of alkali-activated soil. Statistical linear regression analysis was carried out to compare the efficiency of the machine learning technique with the classical statistics model. Parameters such as Si/Al, activator level, curing temperature, water absorption, and weight were used as input parameters to predict the target variable. The coefficient of determination was used to examine the performance of the models. The results depict that artificial neural network outperformed statistical linear regression analysis with R2 =0.74, RMSE=0.119 and R2 =0.48, RMSE=0.466 respectively. This indicates that statistical linear regression analysis is inefficient for prediction of the strength in alkali activated soils.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icecco53203.2021.9663760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Earth-based materials demonstrated promising characteristics in the development of eco-friendly, low cost and sustainable construction materials. However, their unconventional utilization in construction makes the assessment of their properties very difficult and inaccurate because they are assessed based on conventional materials procedures. Hence, the properties of earth-based materials are not well understood. The assessment of earth-based materials properties for sustainable construction is time-consuming, expensive, and inaccurate. To obtain more accurate properties, an artificial neural network and statistical linear regression analysis were used to predict the compressive strength of alkali-activated soil. Statistical linear regression analysis was carried out to compare the efficiency of the machine learning technique with the classical statistics model. Parameters such as Si/Al, activator level, curing temperature, water absorption, and weight were used as input parameters to predict the target variable. The coefficient of determination was used to examine the performance of the models. The results depict that artificial neural network outperformed statistical linear regression analysis with R2 =0.74, RMSE=0.119 and R2 =0.48, RMSE=0.466 respectively. This indicates that statistical linear regression analysis is inefficient for prediction of the strength in alkali activated soils.
机器学习技术与经典统计在环保砌体单元强度预测中的对比
土基材料在生态友好、低成本、可持续发展的建筑材料方面显示出良好的发展前景。然而,它们在建筑中的非常规应用使得对其性能的评估非常困难和不准确,因为它们是基于传统的材料程序进行评估的。因此,人们对土基材料的性质还不太了解。评估可持续建筑的土质材料性能耗时、昂贵且不准确。为了获得更准确的特性,采用人工神经网络和统计线性回归分析对碱活化土的抗压强度进行了预测。进行统计线性回归分析,比较机器学习技术与经典统计模型的效率。以Si/Al、活化剂用量、固化温度、吸水率、重量等参数作为输入参数来预测目标变量。用决定系数来检验模型的性能。结果表明,人工神经网络优于统计线性回归分析,R2 =0.74, RMSE=0.119, R2 =0.48, RMSE=0.466。这表明统计线性回归分析对于碱活化土的强度预测是低效的。
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
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:604180095
Book学术官方微信