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Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 9897078, 11 pages
Research Article

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

1Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, K7/25 Quang Trung, Danang, Vietnam
2Faculty of Architecture, Duy Tan University, K7/25 Quang Trung, Danang, Vietnam
3International School, Duy Tan University, 254 Nguyen Van Linh, Danang 550000, Vietnam

Correspondence should be addressed to Nhat-Duc Hoang; nv.ude.utd@cudtahngnaoh

Received 1 December 2016; Revised 17 January 2017; Accepted 15 March 2017; Published 4 April 2017

Academic Editor: Lukasz Sadowski

Copyright © 2017 Nhat-Duc Hoang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.