同济大学学报（自然科学版）2024，Vol.52Issue(7)：1018-1023,6.DOI:10.11908/j.issn.0253-374x.23419
基于机器学习的超高性能混凝土成本优化
Mix Proportion Optimization of Ultra-High Performance Concrete Based on Machine Learning
摘要
Abstract
In recent years,ultra-high performance concrete(UHPC)has become one of the hot research directions due to its excellent mechanical properties and durability,but its high cost has always limited its application in engineering.In order to reduce the cost of UHPC,this paper proposes a method based on machine learning to optimize the mix proportion of UHPC.In order to achieve this goal,the prediction model of a 28-day compressive strength and expansion of UHPC was first established by using artificial neural network(ANN),which was taken as the constraint condition,taking into account the constraints of UHPC component content,component proportion and absolute volume,The cost of UHPC was reduced by using genetic algorithm(GA).The research results show that the error between the prediction results of ANN model and the experimental results is within 10%,which has good prediction accuracy.The cost of UHPC optimized by GA is reduced to $838.8,which is lower than the cost of $1000 mentioned in the literature.关键词
超高性能混凝土(UHPC)/机器学习/人工神经网络(ANN)/遗传算法/成本Key words
ultra-high performance concrete(UHPC)/machine learning/artificial neural network(ANN)/genetic algorithm/cost分类
建筑与水利引用本文复制引用
周帅,贾跃,李凯,李紫剑,巫晓雪,彭海游,张成明,韩凯航,王冲..基于机器学习的超高性能混凝土成本优化[J].同济大学学报（自然科学版）,2024,52(7):1018-1023,6.基金项目
国家自然科学基金(52002040),重庆市地质灾害防治中心(KJ2021050),宁夏回族自治区重点研发计划项目(2023BDE02004) （52002040）
