Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods.This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method(FEM)and Artificial Neural Network(ANN)combined with Butterfly Optimization Algorithm(BOA).ANN is quite successful in such identification issues,but it has some limitations,such as reduction of error after system training is complete,which means the output does not provide optimal results.This paper improves ANN training after introducing BOA as a hybrid model(BOA-ANN).Natural frequencies are used as input parameters and crack depth as output.The data are collected from improved FEM using simulation tools(ABAQUS)based on different crack depths and locations as the first stage.Next,data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique.The proposed approach,compared to other methods,can predict crack depth with improved accuracy.

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 nonconservation 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.