摘要
为研究深度学习方法在氯离子浓度预测中的应用,通过自然潮差环境下粉煤灰混凝土的长期暴露试验获取3150组自由氯离子浓度数据,建立不同激活函数、不同隐藏层层数的多层感知器(multi-layer perceptron,MLP)模型,开展考虑水灰比、暴露时间、粉煤灰掺量、渗透深度4个输入参数影响的粉煤灰混凝土中的自由氯离子浓度预测研究.结果表明,采用ReLu函数及4层隐藏层构建MLP模型时,自由氯离子浓度的预测结果最优.同时,将构建的最优MLP模型开展基于未测参数的自由氯离子浓度预测,比基于菲克第二定律的预测结果更准确.因此,MLP模型具有精度高和适用范围广泛的特点,可作为氯盐环境下混凝土中自由氯离子浓度预测的新方法.
To explore the application of deep learning theory on predicting chloride concentration,3150 groups of free chloride concentration data were obtained by a long-term exposure test of fly ash concrete under natural tidal environment.A multi-layer perceptron(MLP)model with different activation functions and hidden layers,including four input parameters,namely water-cement ratio,exposure time,fly ash content and penetration depth,was established to predict the free chloride concentration in fly ash concrete.Results show that the prediction result is the best when the MLP model is constructed by using ReLu function with four hidden layers.Meanwhile,the selected optimal MLP model has more accurate precision when predicting the free chloride concentration based on the not measured parameters,compared with the method based on Fick’s second law.Therefore,the MLP model has the advantages of high precision and wide application scope,which can be used as a new method for predicting free chloride concentration of concrete under chloride environment.
作者
章玉容
余威龙
麻雪晴
骆天意
王京京
ZHANG Yurong;YU Weilong;MA Xueqing;LUO Tianyi;WANG Jingjing(College of Civil Engineering,Zhejiang University of Technology,Hangzhou 310014,China;Key Laboratory of Civil Engineering Structure&Disaster Prevention and Mitigation Technology of Zhejiang Province,Hangzhou 310014,China;Faculty of Architecture,Civil and Transportation Engineering,Beijing University of Technology,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2023年第2期205-212,共8页
Journal of Beijing University of Technology
基金
北京市自然科学基金资助项目(9204021)。
关键词
自由氯离子浓度预测
深度学习
多层感知器
激活函数
隐藏层层数
粉煤灰混凝土
free chloride concentration prediction
deep learning
multilayer perceptron
activation function
hidden layer
fly ash concrete
