Research of Neural Network Based on Improved PSO Algorithm for Carbonation Depth Prediction of Concrete

作     者：DAI W SHUI Z H 

作者机构：School of Materials Science and EngineeringWuhan University of TechnologyWuhan 430070China School of Economics and ManagementHuangshi Institution of TechnologyHuangshi 435003China 

出 版 物：《武汉理工大学学报》 (Journal of Wuhan University of Technology)

年 卷 期：2010年第32卷第17期

页      面：170-175页

学科分类：08[工学] 081304[工学-建筑技术科学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0813[工学-建筑学] 

主　　题：PSO BP neural network concrete carbonation depth prediction 

摘      要：Firstly,neural network based on improved particle swarm optimization (PSO)algorithm is introduced in this paper. Based on the data collected from projects in typical areas,the concrete carbonation depth is assessed with consideration of various factors such as unit cement consumption (C),unit water consumption (W),binder material content (B),water binder ratio (W/B ),concrete strength (MPa),rapid carbonization days (D),fly ash consumption of unit volume concrete(FA),fly ash percentage of total cementitious materials (FA%),expansion agent consumption of unit volume concrete(EA),expansion agent percentage of total cementitious materials (FA%).Gaining the data from project-experiment,a model is presented to calculate and forecast carbonation depth using neural network based on improved PSO algorithm. The calculation results indicate that this algorithm accord with the prediction carbonation depth of concrete accuracy requirements and has a better convergence and generalization,worth being popularized.