Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique
作者机构：ETSI Minas y EnergíaUniversidad Politécnica de MadridMadrid 28003Spain Leibniz Institute of Ecological Urban and Regional Development(IOER)Dresden 01217Germany Department of Civil and Environmental EngineeringPolitecnico Di MilanoMilano 20133Italy School of Resources and Safety EngineeringCentral South UniversityChangsha 410083China Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment(Ministry of Education)College of Environment and EcologyChongqing UniversityChongqing 400045China
出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)
年 卷 期：2023年第17卷第9期
页 面：1310-1325页
核心收录：
学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0714[理学-统计学（可授理学、经济学学位）] 0814[工学-土木工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：funding provided by the China Scholarship Council (Nos.202008440524 and 202006370006) supported by the Distinguished Youth Science Foundation of Hunan Province of China (No.2022JJ10073) Innovation Driven Project of Central South University (No.2020CX040) Shenzhen Sciencee and Technology Plan (No.JCYJ20190808123013260).
主 题：sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis
摘 要：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.
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