Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

被引：227

作者：

Prasad, B. K. Raghu ^{[1
]}

Eskandari, Hamid ^{[1
]}

Reddy, B. V. Venkatarama ^{[1
]}

机构：

[1] Indian Inst Sci, Dept Civil Engn, Bangalore 560012, Karnataka, India

来源：

CONSTRUCTION AND BUILDING MATERIALS | 2009年 / 23卷 / 01期

关键词：

Self compacting concrete; Artificial neural network; High performance concrete; High volume fly ash; Slump flow; HIGH-PERFORMANCE CONCRETE; SELF-COMPACTING CONCRETE; NEURAL-NETWORKS; DESIGN; SLUMP; MODEL;

D O I：

10.1016/j.conbuildmat.2008.01.014

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：117 / 128

页数：12

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