Prediction of Concrete Strength Using Neural-Expert System
Publication: Journal of Materials in Civil Engineering
Volume 18, Issue 3
Abstract
Over the years, many methods have been developed to predict the concrete strength. In recent years, artificial neural networks (ANNs) have been applied to many civil engineering problems with some degree of success. In the present paper, ANN is used as an attempt to obtain more accurate concrete strength prediction based on parameters like concrete mix design, size and shape of specimen, curing technique and period, environmental conditions, etc. A total of 864 concrete specimens were cast for compressive strength measurement and verification through the ANN model. The back propagation-learning algorithm is employed to train the network for extracting knowledge from training examples. The predicted strengths found by employing ANN are compared with the actual values. The results indicate that ANN is a useful technique for predicting the concrete strength. Further, an effort to build an expert system for the problem is described in this paper. To overcome the bottleneck of intricate knowledge acquisition, an expert system is used as a mechanism to transfer engineering experience into usable knowledge through rule-based knowledge representation techniques.
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Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 18 • Issue 3 • June 2006
Pages: 462 - 466
Copyright
© 2006 ASCE.
History
Received: Mar 1, 2005
Accepted: Jun 27, 2005
Published online: Jun 1, 2006
Published in print: Jun 2006
Notes
Note. Associate Editor: Zhishen Wu
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