Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls

Abstract

This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls.

Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges

“…In this research, the peak ground acceleration (PGA) has been used as a measure of earthquake intensity. For future research, using advances in machine learning [31,32,45,[60][61][62][63][64] can enable the extraction of effective earthquake features and the creation of PSDM curves based on these features.…”