Neural prediction of concrete compressive strength

被引：0

作者：

Dauji S. ^{[1
]}

机构：

[1] Nuclear Recycle Board, Bhabha Atomic Research Centre, Anushaktinagar, Mumbai

来源：

International Journal of Materials and Structural Integrity | 2018年 / 12卷 / 1-3期

关键词：

Artificial neural networks; Compressive strength; Concrete; Modular ANN; Prediction;

D O I：

10.1504/IJMSI.2018.093884

中图分类号：

学科分类号：

摘要：

In the past, a few researchers predicted the compressive strength of concrete from its ingredients by employing artificial neural network (ANN). Evaluation of the models with different performance metrics, such as correlation coefficient and errors of estimation, indicated that there was scope for improvement in the prediction performance of ANN. In this paper development of prediction models has been carried out for superior performance with two concepts: single ANN and modular ANN. Experimental data from literature have been utilised for the study. Better overall performance of the developed ANN models than reported in the literature was ascertained by higher correlation, less root mean square error and mean absolute error. The performance of the modular ANN concept was superior to that of single ANN concept for the present application. © 2018 Inderscience Enterprises Ltd.

引用

页码：17 / 35

页数：18

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