用机器学习方法预测使用可持续天然 FRP 复合材料加固的混凝土强度

IF 5.3 Q2 MATERIALS SCIENCE, COMPOSITES Composites Part C Open Access Pub Date : 2024-05-03 DOI:10.1016/j.jcomc.2024.100466
Shabbir Ali Talpur , Phromphat Thansirichaisree , Nakhorn Poovarodom , Hisham Mohamad , Mingliang Zhou , Ali Ejaz , Qudeer Hussain , Panumas Saingam
{"title":"用机器学习方法预测使用可持续天然 FRP 复合材料加固的混凝土强度","authors":"Shabbir Ali Talpur ,&nbsp;Phromphat Thansirichaisree ,&nbsp;Nakhorn Poovarodom ,&nbsp;Hisham Mohamad ,&nbsp;Mingliang Zhou ,&nbsp;Ali Ejaz ,&nbsp;Qudeer Hussain ,&nbsp;Panumas Saingam","doi":"10.1016/j.jcomc.2024.100466","DOIUrl":null,"url":null,"abstract":"<div><p>Recent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This study investigates the use of machine learning algorithms for predicting the compressive strength concrete specimens confined with various NFRPs. Four algorithms were employed: decision tree, random forest, neural network, and gradient boosting regressor. A diverse dataset encompassing various geometries, material properties, and confinement configurations was used to train and evaluate the models. Gradient boosting regressor (GBR) achieved the highest performance, with an average R-squared value of 0.94 and low mean absolute error (MAE) and root mean squared error (RMSE) during training and k-fold cross-validation. Neural network and random forest also demonstrated satisfactory performance, with average R-squared values of 0.88 and 0.86, respectively, during cross-validation. These results suggest that machine learning holds promise for predicting the compressive strength of concrete confined with NFRPs. GBR offers the most accurate predictions, making it a valuable tool for engineers seeking to optimize the design and performance of strengthened structures using sustainable materials.</p></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666682024000379/pdfft?md5=08c6bce51afb8626a4ef3bdbd9b2895c&pid=1-s2.0-S2666682024000379-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Machine learning approach to predict the strength of concrete confined with sustainable natural FRP composites\",\"authors\":\"Shabbir Ali Talpur ,&nbsp;Phromphat Thansirichaisree ,&nbsp;Nakhorn Poovarodom ,&nbsp;Hisham Mohamad ,&nbsp;Mingliang Zhou ,&nbsp;Ali Ejaz ,&nbsp;Qudeer Hussain ,&nbsp;Panumas Saingam\",\"doi\":\"10.1016/j.jcomc.2024.100466\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This study investigates the use of machine learning algorithms for predicting the compressive strength concrete specimens confined with various NFRPs. Four algorithms were employed: decision tree, random forest, neural network, and gradient boosting regressor. A diverse dataset encompassing various geometries, material properties, and confinement configurations was used to train and evaluate the models. Gradient boosting regressor (GBR) achieved the highest performance, with an average R-squared value of 0.94 and low mean absolute error (MAE) and root mean squared error (RMSE) during training and k-fold cross-validation. Neural network and random forest also demonstrated satisfactory performance, with average R-squared values of 0.88 and 0.86, respectively, during cross-validation. These results suggest that machine learning holds promise for predicting the compressive strength of concrete confined with NFRPs. GBR offers the most accurate predictions, making it a valuable tool for engineers seeking to optimize the design and performance of strengthened structures using sustainable materials.</p></div>\",\"PeriodicalId\":34525,\"journal\":{\"name\":\"Composites Part C Open Access\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666682024000379/pdfft?md5=08c6bce51afb8626a4ef3bdbd9b2895c&pid=1-s2.0-S2666682024000379-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Composites Part C Open Access\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666682024000379\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, COMPOSITES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composites Part C Open Access","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666682024000379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
引用次数: 0

摘要

最近发生的地震凸显了加固设计不合标准的现有结构的必要性。NFRP 为加固提供了一种可持续的、具有成本效益的替代方案,但准确预测其性能仍是一项挑战。本研究调查了机器学习算法在预测使用各种 NFRP 加固的混凝土试件抗压强度方面的应用。研究采用了四种算法:决策树、随机森林、神经网络和梯度提升回归器。模型的训练和评估使用了一个包含各种几何形状、材料特性和约束配置的多样化数据集。梯度提升回归器(GBR)的性能最高,平均 R 平方值为 0.94,在训练和 k 倍交叉验证期间的平均绝对误差(MAE)和均方根误差(RMSE)都很低。神经网络和随机森林的表现也令人满意,在交叉验证期间的平均 R 平方值分别为 0.88 和 0.86。这些结果表明,机器学习有望预测使用非弹性体加固混凝土的抗压强度。GBR 提供了最准确的预测,使其成为工程师优化使用可持续材料的加固结构设计和性能的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Machine learning approach to predict the strength of concrete confined with sustainable natural FRP composites

Recent earthquakes have highlighted the need to strengthen existing structures with substandard designs. NFRPs provide a sustainable, cost-effective alternative for strengthening, but accurately predicting their performance remains a challenge. This study investigates the use of machine learning algorithms for predicting the compressive strength concrete specimens confined with various NFRPs. Four algorithms were employed: decision tree, random forest, neural network, and gradient boosting regressor. A diverse dataset encompassing various geometries, material properties, and confinement configurations was used to train and evaluate the models. Gradient boosting regressor (GBR) achieved the highest performance, with an average R-squared value of 0.94 and low mean absolute error (MAE) and root mean squared error (RMSE) during training and k-fold cross-validation. Neural network and random forest also demonstrated satisfactory performance, with average R-squared values of 0.88 and 0.86, respectively, during cross-validation. These results suggest that machine learning holds promise for predicting the compressive strength of concrete confined with NFRPs. GBR offers the most accurate predictions, making it a valuable tool for engineers seeking to optimize the design and performance of strengthened structures using sustainable materials.

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
Composites Part C Open Access
Composites Part C Open Access Engineering-Mechanical Engineering
CiteScore
8.60
自引率
2.40%
发文量
96
审稿时长
55 days
期刊最新文献
Hybrid lattice structure with micro graphite filler manufactured via additive manufacturing and growth foam polyurethane Cure-induced residual stresses and viscoelastic effects in repaired wind turbine blades: Analytical-numerical investigation Bioinspired surface modification of mussel shells and their application as a biogenic filler in polypropylene composites A review of repairing heat-damaged RC beams using externally bonded- and near-surface mounted-CFRP composites Comparative analysis of delamination resistance in CFRP laminates interleaved by thermoplastic nanoparticle: Evaluating toughening mechanisms in modes I and II
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1