Structural Damage Detection Based On Model Updating And Intelligent Optimization Strategy

Structural damage detection(SDD)is a key step in implementing structural health monitoring(SHM).As for this study,it aims at improving objective functions of model updating-based methods,sparse representation of structural damage and improving performance of the ant lion optimizer(ALO)algorithm.To systematically study these methodologies,this paper combines the numerical simulations and experimental verifications.The main contents can be summarized as follows:(1)A detailed literature review on SDD techniques is summarized,in which the SDD methodologies have been classified into two major categories based on their range of application,i.e.,local detection methods and vibration information-based global detection methods,respectively.Local detection methods mainly contain visual inspection methods and instrumented detection methods.While the global detection methods include dynamic fingerprint,signal feature and model updating based methods,respectively.Lastly,pros and cons of these methods are further discussed.(2)A novel SDD method is proposed based on the objective function weight strategy and the trace sparse regularization through combining the ALO algorithm.In order to improve the accuracy of SDD,quantitative functions established by modal parameters are weighted in the objective functions based on damage sensitivities of different modal parameters,such as frequencies and mode shapes.To effectively extract the sparse representation information of damage and improve the anti-noise robustness of the SDD method,the trace sparse regularization is introduced into the objective function according to the sparsity of structural damage in physical space.Compared with the traditional objective functions,the proposed objective function can obviously improve the accuracy and anti-noise robustness of damage identification,and can attain more steadier SDD results.Numerical simulations and experimental verifications both show that the proposed method can identify structural damages effectively and accurately.(3)A novel SDD method is proposed based on the multi-objective ant lion optimizer(MOALO)algorithm through combining the objective function weight strategy and the trace sparse regularization.The MOALO algorithm is introduced into the SDD field to solve the multi-objective optimization problem on SDD.Compared with the ALO algorithm,the MOALO algorithm has better optimization performance and it can further improve the accuracy of SDD.The numerical simulation results on a two-storey frame structure is employed to illustrate the outperformance of the proposed SDD method,so as to effectively and accurately implement SDD.(4)A novel SDD method is proposed based on the hybrid ALO and improved Nelder-Mead(ALO-INM)algorithm through combining the objective function weight strategy and the trace sparse regularization.To tackle insufficient global convergence performance of traditional swarm intelligence algorithms in solving SDD problem,a hybrid ALO-INM algorithm is proposed.Three classical benchmark functions are adopted to assess that the hybrid ALO-INM algorithm is better than the ALO algorithm in both global convergence performance and stability.Furthermore,the results of numerical examples and experimental verifications both show that the proposed method can further improve the identification accuracy and the robustness to noises,so as to achieve more accurate and stable SDD.(5)A two-stage SDD method is proposed based on modal strain energy(MSE)through combining the ALO-INM algorithm and the objective function weight strategy.Aiming at locating unknown locations of damages in the engineering application,the modal strain energy change ratio(MSECR)of structural elements before and after being damaged is used to locate potential damages of the structure,and then theseelements are taken as the optimization parameters of the hybrid ALO-INM algorithm.The results of numerical simulations show that the proposed method can effectively reduce the computational cost of SDD and further improve the accuracy of SDD.