A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete
作者机构：Department of Civil and Environmental EngineeringPolitecnico Di MilanoMilano 20133Italy Leibniz Institute of Ecological Urban and Regional Development(IOER)Dresden 01217Germany Universidad Politécnica de Madrid-ETSI Minas y EnergíaMadrid 28003Spain Department of Civil EngineeringKU Leuven CampusBrugge 8200Belgium School of Resources and Safety EngineeringCentral South UniversityChangsha 410083China
出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)
年 卷 期：2024年第18卷第1期
页 面：30-50页
核心收录：
学科分类：08[工学] 081304[工学-建筑技术科学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0813[工学-建筑学] 0814[工学-土木工程]
基 金：the funding supported by China Scholarship Council(Nos.202008440524 and 202006370006) partially supported by the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073) the Innovation Driven Project of Central South University(No.2020CX040) Shenzhen Science and Technology Plan(No.JCYJ20190808123013260)
主 题：recycled aggregate concrete carbonation depth nature-inspired optimization algorithms extreme gradient boosting technique parametric analysis
摘 要：The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic ***,RA concrete is plagued with considerable durability concerns,particularly *** advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is *** the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to *** the other hand,carbonation is influenced by many factors and is hard to *** this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA *** types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive *** was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,***-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 *** also showed better generalization capabilities when compared with different models in the ***,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.
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