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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.