Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams

Abstract

Shear strength (SS) is an essential component in the design of reinforced concrete structural elements, particularly in severe settings where reinforcements can occur and cause a loss in SS. The purpose of this work is to develop a framework for predicting the SS and optimal design of corroded reinforced concrete (CRCo) beams by using a machine learning (ML) approach. The model employed was Gradient Boosting (CGB), optimized using three metaheuristic algorithms. By optimizing the CGB model with the Hunger Games Search (HGS) algorithm, the study achieved the best ML model for predicting the SS of CRCo beams, with an R² value of 0.996. The proposed model outperformed five other empirical SS models for CRCo beams, and sensitivity, partial dependence analyses were also conducted to explore the impact of various variables on SS. Finally, this work provided guidance on selecting appropriate beam sizes and corrosion rates for optimal CRCo beams design solutions.