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 vi...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.展开更多
Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(A...Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been applied.Thus,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PSO),and genetic algorithms(GAs)is proposed to solve this problem.The DNN can effectively simulate any sophisticated equation,and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model.In the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent loading.The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.展开更多
文摘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.
文摘Currently,the vertical drain consolidation problem is solved by numerous analytical solutions,such as time-dependent solutions and linear or parabolic radial drainage in the smear zone,and no artificial intelligence(AI)approach has been applied.Thus,in this study,a new hybrid model based on deep neural networks(DNNs),particle swarm optimization(PSO),and genetic algorithms(GAs)is proposed to solve this problem.The DNN can effectively simulate any sophisticated equation,and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model.In the present study,analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under timedependent loading.The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.
