Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning. (4th April 2017)

• Record Type: Journal Article
• Authors: Hoang, Nhat-Duc; Vu, Duy-Thang; Tran, Xuan-Linh; Tran, Van-Duc
• Other Names: Sadowski Lukasz Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2017(2017)
• Publication Date: 2017-04-04
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2017/9897078
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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.