An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam

The Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool.