摘要
硫酸盐侵蚀是影响混凝土耐久性的重要因素,准确预测其对混凝土抗压强度的劣化影响对结构安全评估至关重要。基于机器学习的方法对硫酸盐侵蚀环境下混凝土抗压强度进行了预测。从现有中外文献中收集了硫酸盐侵蚀下混凝土抗压强度的实验数据157组,以80%和20%的比例划分训练集和测试集,构建了随机森林(random forest,RF)模型和BP神经网络(BP neural network,BPNN)模型对硫酸盐侵蚀后的混凝土抗压强度进行了预测,采用平均绝对误差(mean absolute error,MAE)、均方根误差(root mean squared error,RMSE)和决定系数(R^(2))三个指标对模型性能进行了评价。为了进一步提高预测精度,采用特征重要性、皮尔森相关系数以及两者相结合的方法进行优化。基于最佳的优化方法,对模型进行了进一步的解释。结果表明,两种模型对侵蚀后混凝土抗压强度的预测结果均较为准确,RF模型预测结果优于BPNN模型。优化结果表明,采用特征重要性和皮尔森相关系数相结合的方法优化效果最佳。基于最佳优化方法,进一步就各因素对侵蚀后混凝土抗压强度的影响进行分析,并建立侵蚀后混凝土抗压强度随28 d抗压强度、侵蚀时间和Na_(2)SO_(4)溶液浓度变化的预测模型。研究成果可为采用大数据预测硫酸盐侵蚀环境下混凝土抗压强度提供一种参考。
Sulfate attack is a critical factor affecting concrete durability,and accurate prediction of its detrimental effect on concrete compressive strength is essential for structural safety assessment.A machine learning-based approach was proposed to predict the compressive strength of concrete subjected to sulfate corrosion.A total of 157 experimental datasets on the compressive strength of concrete under sulfate attack were collected from existing domestic and international literature.The data were divided into training and test sets with a ratio of 80%and 20%.RF(random forest)and BPNN(BP neural network)models were developed to predict the compressive strength of concrete after sulfate corrosion.The performance of the models was evaluated using three indexes:MAE(mean absolute error),RMSE(root mean squared error),and the coefficient of determination(R^(2)).To further improve prediction accuracy,feature importance,Pearson correlation coefficient,and a combination of both methods were applied for optimization.Based on the optimal optimization method,the model was further interpreted.The results show that both models provide accurate predictions of compressive strength after sulfate corrosion with the RF model demonstrating superior performance compared to the BPNN model.Optimization analysis reveales that the combination of feature importance and Pearson correlation coefficient yield the best predictive performance.Based on the best optimization method,the influence of different factors on 28 d compressive strength of eroded concrete was analyzed.The prediction models with variation of 28-day compressive strength,erosion time and sodium sulfate concentration were established.This study provides a reference for the application of big data techniques in predicting the compressive strength of concrete in sulfate corrosion environments.
作者
周均昊
杨淑雁
ZHOU Jun-hao;YANG Shu-yan(School of Civil and Hydraulic Engineering,Ningxia University,Yinchuan 750021,China)
出处
《科学技术与工程》
北大核心
2025年第30期13075-13083,共9页
Science Technology and Engineering
基金
宁夏自然科学基金(2023AAC03129)
宁夏留学回国人员创新创业项目(宁人社函[2024]4号)。
关键词
随机森林
BP神经网络
硫酸盐侵蚀
抗压强度
预测模型
RF(random forest)
BPNN(BP neural network)
sulfate attack
compressive strength
prediction model
