基于神经网络与回归分析的多孔混凝土性能预测  

Performance prediction of porous concrete based on neural network and regression analysis

作　　者：曲广雷 闫宗伟 郑木莲[1] 刘红 袁月明 QU Guang-lei;YAN Zong-wei;ZHENG Mu-lian;LIU Hong;YUAN Yue-ming(Key Laboratory for Special Area Highway Engineering of Ministry of Education,Chang'an University,Xi'an 710064,China;Shandong High Speed Linteng Highway Co.,Ltd.,Linyi 273400,China)

机构地区：[1]长安大学特殊地区公路工程教育部重点实验室,西安710064 [2]山东高速临滕公路有限公司,山东临沂273400

出　　处：《吉林大学学报(工学版)》2025年第1期269-282,共14页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(52078051);山东省交通科技项目(HS2022B073)。

摘　　要：为实现对多孔混凝土28 d抗压强度和透水系数两项关键性能指标的预测,通过相关性分析确定水胶比、胶凝材料用量、骨胶比和实测孔隙率为模型输入参数,然后基于构建的数据集,采用神经网络和回归分析分别建立了4种预测模型。结果表明:两种统计回归模型的预测误差较大,对多变量的响应缺乏敏感性,其拟合优度R^(2)均在0.681以下;两种神经网络模型更适合解决复杂、多变量的性能预测问题,其中经遗传算法优化的神经网络抗压强度预测模型的拟合优度R^(2)达到0.9以上,体现了该预测模型的精准性和稳定性。研究成果可为多孔混凝土的配合比优化设计与性能调控提供参考和指导。In order to predict the 28d compressive strength and permeability coefficient of porous concrete,the water-cement ratio,cementitious material dosage,bone-cement ratio and measured porosity were determined as model input parameters through correlation analysis.Furthermore,the prediction models for the two indicators are established based on the constructed dataset using artificial neural networks and regression analysis,respectively.The results showed that the statistical regression models had significant prediction errors,lacked sensitivity to multivariate responses,and their goodness-of-fit R^(2) were all below0.681.The artificial neural network model is more suitable for solving complex and multivariate performance prediction problems.The neural network compressive strength prediction model optimized by the genetic algorithm has a goodness-of-fit R^(2) of more than 0.9,which reflects the accuracy and stability of the prediction model.The research results could provide reference and guidance for the optimal design of the mix ratio and performance regulation of porous concrete.

关 键 词：道路与铁道工程 多孔混凝土 抗压强度 透水系数 神经网络 回归分析 

分 类 号：U414[交通运输工程—道路与铁道工程]