首页|A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

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The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R2 value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

recycled aggregate concretecarbonation depthnature-inspired optimization algorithmsextreme gradient boosting techniqueparametric analysis

Bin XI、Ning ZHANG、Enming LI、Jiabin LI、Jian ZHOU、Pablo SEGARRA

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Department of Civil and Environmental Engineering,Politecnico Di Milano,Milano 20133,Italy

Leibniz Institute of Ecological Urban and Regional Development(IOER),Dresden 01217,Germany

Universidad Politécnica de Madrid-ETSI Minas y Energía,Madrid 28003,Spain

Department of Civil Engineering,KU Leuven Campus,Brugge 8200,Belgium

School of Resources and Safety Engineering,Central South University Changsha 410083,China

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国家留学基金委项目国家留学基金委项目Distinguished Youth Science Foundation of Hunan Province of ChinaInnovation Driven Project of Central South UniversityShenzhen Science and Technology Plan

2020084405242020063700062022JJ100732020CX040JCYJ20190808123013260

2024

10.1007/s11709-024-1041-y

结构与土木工程前沿
高等教育出版社

结构与土木工程前沿

CSTPCDEI
影响因子：0.082
ISSN：2095-2430
年,卷(期)：2024.18(1)
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