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

Abstract EN

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.