Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods

作     者：Min WANG Mingfeng DU Xiaoying ZHUANG Hui LV Chong WANG Shuai ZHOU 

作者机构：China Merchants Chongqing Communications Technology Research and Design Institute Co.Ltd.Chongqing 400067China College of Materials Science and EngineeringChongqing UniversityChongqing 400045China Department of Mathematics and PhysicsLeibniz Universität HannoverHannover 30167Germany Department of Geotechnical EngineeringCollege of Civil EngineeringTongji UniversityShanghai 200092China 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿(英文版))

年 卷 期：2025年第19卷第1期

页      面：143-161页

核心收录：

学科分类：08[工学] 0815[工学-水利工程] 081503[工学-水工结构工程] 

基　　金：supported by the National Key R&D Program of China(No.2022YFB2602600) the National Natural Science Foundation of China(Grant No.52478235) the National Key R&D Program of China(No.2023YFB3711400) the Key Research and Development Program of Ningxia Hui Autonomous Region(No.2023BDE02004) 

主　　题：ultra-high performance concrete machine learning multi-objective optimization life-cycle assessment 

摘      要：Ultra-high performance concrete(UHPC)has gained a lot of attention lately because of its remarkable properties,even if its high cost and high carbon emissions run counter to the current development *** lower the cost and carbon emissions of UHPC,this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this *** key features of UHPC are filtered using the recursive feature elimination approach,and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction *** optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-Ⅲ and multiobjective evolutionary algorithm based on adaptive geometric *** results are evaluated by technique for order preference by similarity to ideal solution and validated by *** outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning *** multiobjective optimization produces Pareto fronts,which illustrate the trade-off between the mix’s compressive strength,slump flow,cost,and environmental sustainability as well as the wide variety of possible *** research contributes to the development of cost-effective and environmentally sustainable UHPC,and aids in robust,intelligent,and sustainable building practices.