文献类型：期刊文献

英文题名：Prediction of Concrete Cubic Compressive Strength Using ANN Based Size Effect Model

作者：Yang, Q. W.[1];Du, S. G.[1]

机构：[1]Shaoxing Univ, Dept Civil Engn, Shaoxing 312000, Peoples R China

年份：2015

卷号：47

期号：3

起止页码：181

外文期刊名：CMC-COMPUTERS MATERIALS & CONTINUA

收录：SCI-EXPANDED(收录号：WOS:000375367300003)、、EI(收录号：20160801973055)、Scopus(收录号：2-s2.0-84958596299)、WOS

基金：This work is supported by National Natural Science Foundation of China (41427802, 11202138, 41172292) and Zhejiang Province Natural Science Foundation (LZ13D020001).

语种：英文

外文关键词：concrete; size effect; compressive strength; artificial neural network back-propagation

外文摘要：Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper to further improve the forecast accuracy. The results demonstrate that the ANN-based size effect model has a strong potential to predict the cubic compressive strength of concrete.