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材料导报  2019, Vol. 33 Issue (Z2): 317-320    
  无机非金属及其复合材料 |
基于BP神经网络的混凝土综合性能预测
李地红, 高群, 夏娴, 张景卫, 于海洋, 王艳君, 代函函, 许国栋
北京建筑大学土木与交通工程学院,北京 100044
Prediction of Comprehensive Performance of Concrete Based on BP Neural Network
LI Dihong, GAO Qun, XIA Xian, ZHANG Jingwei, YU Haiyang, WANG Yanjun, DAI Hanhan, XU Guodong
College of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044,China
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摘要 本工作采用BP神经网络系统进行混凝土综合性能预测。由于影响混凝土材料性能的因素众多,且实验周期长,实验量大,因此能在少量实验前提下得到精确的实验结果,对于混凝土方面的研究工作显得十分重要。利用BP神经网络系统进行该实验设计,可以对混凝土性能做出预判,对实施实验及实验结果有较强的导向作用。本研究以影响混凝土性能的诸多因素作为输入向量,通过预测系统运行,得到较为精确的结果,与实验值相比,误差主要分布在15%以内,极个别出现24%的预测误差。以此为依据,进行混凝土材料实验工作,使得实验工作有明显的趋向,快速得到满意的实验结果。
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李地红
高群
夏娴
张景卫
于海洋
王艳君
代函函
许国栋
关键词:  BP神经网络  混凝土实验  综合性能  预测    
Abstract: In this paper, the BP neural network system is used to predict the comprehensive performance of concrete. As is well known, there are many factors affecting the performance of concrete materials, the experimental period is long, the amount of experiment is large, and accurate expe-rimental results can be obtained under a small number of experimental conditions, which is very important for the research work on concrete. The BP neural network system is used for the experimental design, which can predict the performance of concrete and has a strong guiding effect on the implementation experiment and experimental results. In this paper, many factors affecting the performance of concrete are used as input vectors. By predicting the system operation, more accurate results are obtained. Compared with the experimental values, the error is mainly distributed within 15%, and the individual has 24% prediction error. Based on this, the experimental work of concrete materials is carried out, which makes the experimental work have a clear trend and quickly obtain satisfactory experimental results.
Key words:  BP neural network    concrete experiment    comprehensive performance    prediction
               出版日期:  2019-11-25      发布日期:  2019-11-25
ZTFLH:  TU528  
  TP312  
通讯作者:  lidihong@bucea.edu.cn   
作者简介:  李地红,1998于哈尔滨建筑大学获得博士学位。2000—2001年作为访问学者在日本东京大学从事舣装材料结构研究与船舶材料评价。李地红教授多年来一直从事聚合物基复合材料的教学、科研工作,主要研究聚合物基复合材料结构分析、结构设计、结构与工艺一体化设计。近5年承担11项科研项目,总经费384万元,3项任项目负责人,经费209万元,国家重大专项专题两项,经费187万元。任职以来,发表学术论文43篇,获国家发明专利9项。近5年发表学术论文21篇,其中SCI收录4篇,EI收录9篇。核心以上期刊7篇,获国家发明专利4项。
引用本文:    
李地红, 高群, 夏娴, 张景卫, 于海洋, 王艳君, 代函函, 许国栋. 基于BP神经网络的混凝土综合性能预测[J]. 材料导报, 2019, 33(Z2): 317-320.
LI Dihong, GAO Qun, XIA Xian, ZHANG Jingwei, YU Haiyang, WANG Yanjun, DAI Hanhan, XU Guodong. Prediction of Comprehensive Performance of Concrete Based on BP Neural Network. Materials Reports, 2019, 33(Z2): 317-320.
链接本文:  
http://www.mater-rep.com/CN/  或          http://www.mater-rep.com/CN/Y2019/V33/IZ2/317
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