离岸混凝土氯离子扩散系数的人工神经网络模型  

Prediction of chloride diffusion coefficients of offshore concrete structures using artificial neural networks

作　　者：卢显扬 杨健[1] 刘清风[1] 胡宇[1] 

机构地区：[1]上海交通大学船舶海洋与建筑工程学院,上海200240

出　　处：《河北工程大学学报（自然科学版）》2016年第1期5-10,共6页Journal of Hebei University of Engineering:Natural Science Edition

基　　金：国家自然科学基金资助项目(51378303;51508324);上海市"浦江人才计划"(13PJ1405200;15PJ1403800);教育部博士点新教师基金资助项目(20130073120074);上海市教委创新重点项目(14ZZ027)

摘　　要：为研究人工神经网络在离岸混凝土氯离子渗透中的应用,从已有文献中选用653组数据,建立网络结构为13-27-1的模型进行训练、预测。研究结果表明:人工神经网络能有效预测离岸混凝土中的氯离子扩散系数;水灰比,水泥、减水剂、外加剂(粉煤灰、矿渣、硅灰)、骨料的含量以及混凝土的抗压强度、养护机制、试验方法、暴露时间和暴露环境均会对氯离子扩散系数产生影响。An increasing attention has been directed to applying the artificial neural network（ ANN）method in civil engineering. This paper examines the possibility of artificial neural network（ ANN） to predict the chloride diffusion coefficient of concrete. A total 653 available sets of data from 13 literatures was used for establishing the network model. The developed ANN model used as many as 13 input variables,including water / cement ratio; the dosage of cement,superplasticizer,fly ash,granulated blast furnace,silica fume,aggregate; compressive strength; curing mechanism; testing method;testing time and environment to achieve one output parameter,referred to as chloride diffusion coefficient. The research results show that ANN is feasible in predicting the chloride diffusion coefficient in offshore concrete structures and the selected input variables are all correlated parameters.

关 键 词：离岸混凝土 氯离子 扩散系数 人工神经网络 

分 类 号：TU528[建筑科学—建筑技术科学]