硅酸盐通报2025，Vol.44Issue(5)：1656-1665,10.DOI:10.16552/j.cnki.issn1001-1625.2024.1258
基于机器学习的磷石膏轻骨料混凝土配合比设计与力学性能研究
Mix Ratio Design and Mechanical Properties of Phosphogypsum Lightweight Aggregate Concrete Based on Machine Learning
摘要
Abstract
Using phosphogypsum lightweight coarse aggregate instead of natural gravel to prepare lightweight aggregate concrete is an effective technology to realize the comprehensive utilization of phosphogypsum resources.Based on the mix ratio design principle of lightweight aggregate concrete and BP neural network model,a method to predict the mechanical properties of small particle size phosphogypsum lightweight aggregate concrete was proposed.The results show that the compressive strength and splitting tensile strength of concrete decrease with the increase of net water cement ratio,increase with the increase of sand ratio,and decrease slightly with the increase of cement content.The order of significance of the three influencing factors is net water cement ratio,sand ratio and cement content.Appropriately reducing the net water cement ratio,using high sand ratio and low cement content can reduce the generation of pores and initial microcracks in the interfacial transition zone,and improve the overall mechanical strength.The constructed BP neural network model has a high accuracy in predicting the mechanical strength of small particle phosphogypsum lightweight aggregate concrete.The purpose of this study is to provide reference for mix ratio design optimization and mechanical strength prediction of phosphogypsum lightweight aggregate concrete.关键词
磷石膏轻骨料/混凝土/抗压强度/劈裂抗拉强度/BP神经网络/微观分析Key words
phosphogypsum lightweight aggregate/concrete/compressive strength/splitting tensile strength/BP neural network/microanalysis分类
建筑与水利引用本文复制引用
苏瑛,龚伟,刘川北,张俊..基于机器学习的磷石膏轻骨料混凝土配合比设计与力学性能研究[J].硅酸盐通报,2025,44(5):1656-1665,10.基金项目
四川省国际科技合作项目(2021YFH0089) （2021YFH0089）
