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Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

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Frontiers of Structural and Civil Engineering 2023年第9期17卷 1310-1325页

作者： Enming LI Ning ZHANG Bin XI Jian ZHOU Xiaofeng GAO ETSI Minas y Energía Universidad Politécnica de MadridMadrid 28003Spain Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy School of Resources and Safety Engineering Central South UniversityChangsha 410083China Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment(Ministry of Education) College of Environment and EcologyChongqing UniversityChongqing 400045China

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

关键词： sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis

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

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 30-50页

作者： Bin XI Ning ZHANG Enming LI Jiabin LI Jian ZHOU Pablo SEGARRA Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Universidad Politécnica de Madrid-ETSI Minas y Energía Madrid 28003Spain Department of Civil Engineering KU Leuven CampusBrugge 8200Belgium School of Resources and Safety Engineering Central South UniversityChangsha 410083China

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 R^(2) 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.

关键词： recycled aggregate concrete carbonation depth nature-inspired optimization algorithms extreme gradient boosting technique parametric analysis

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