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Structural damage identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 447-469页

作者： Yu Zhang Zhihua Xiong Zhuoxi Liang Jiachen She Chicheng Ma College of Water Resources and Architectural Engineering Northwest A&F UniversityYangling712100China

A huge number of old arch bridges located in rural regions are at the peak of *** health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural *** is an urgent need for an economical,fast,and accurate damage identification *** authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was *** comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the *** identification model were found to have a better recognition ***,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the *** PSO optimized *** was capable of the identification of different damage levels of old arch bridges with sensitive damage *** proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.

关键词： Old arch bridge damage identification machine learning random forest particle swarm optimization

Ensemble 1D DenseNet damage identification Method Based on Vibration Acceleration

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结构耐久性与健康监测(英文) 2023年第5期17卷 369-381页

作者： Chun Sha Chaohui Yue Wenchen Wang School of Civil Engineering Chongqing Jiaotong UniversityChongqing400074China School of Architecture and Art Design Henan Polytechnic UniversityJiaozuo454001China Zhong Yun International Engineering Co. Ltd.Zhengzhou450007China

Convolution neural networks in deep learning can solve the problem of damage identification based on vibration *** combining multiple 1D DenseNet submodels,a new ensemble learning method is proposed to improve identification accuracy.1D DenseNet is built using standard 1D CNN and DenseNet basic blocks,and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset *** using submodels for damage identification,the voting method ideas in ensemble learning are used to vote on the results of each submodel,and then vote ***,the cantilever damage problem simulated by ABAQUS is selected as a case study to discuss the excellent performance of the proposed *** results show that the ensemble 1D DenseNet damage identification method outperforms any submodel in terms of ***,the submodel is visualized to demonstrate its operation mode.

关键词： Acceleration damage identification 1D DenseNet cantilever beam ensemble learning

damage identification of A TLP Floating Wind Turbine by Meta-Heuristic Algorithms

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China Ocean Engineering 2015年第6期29卷 891-902页

作者： M.M.Ettefagh Mechanical Engineering Department University of Tabriz

damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring（SHM）. In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP（Tension-Leg Platform） floating wind turbine structure. The Genetic Algorithms（GA）, Artificial Immune System（AIS）, Particle Swarm Optimization（PSO）, and Artificial Bee Colony（ABC） are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine（TLPFWT） is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

关键词： floating wind turbine multi-body dynamics damage identification meta-heuristic algorithms optimization

damage identification in connections of moment frames using time domain responses and an optimization method

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Frontiers of Structural and Civil Engineering 2021年第4期15卷 851-866页

作者： Narges PAHNABI Seyed Mohammad SEYEDPOOR Department of Civil Engineering Shomal UniversityAmol***Iran

damage is defined as changes to the material and/or geometric properties of a structural system,comprising changes to the boundary conditions and system connectivity,adversely affecting the system’s *** the elements of structures,particularly critical components,is vital to evaluate the structural lifespan and *** this study,an optimization-based method for joint damage identification of moment frames using the time-domain responses is *** beam-to-column connection in a metallic moment frame structure is modeled by a zero-length rotational spring at both ends of the beam *** each connection,an end-fixity factor is specified,which changes between 0 and ***,the problem of joint damage identification is converted to a standard optimization *** objective function is defined using the nodal point accelerations extracted from the damaged structure and an analytical model of the structure in which the nodal accelerations are obtained using the Newmark *** optimization problem is solved by an improved differential evolution algorithm(IDEA)for identifying the location and severity of the *** assess the capability of the proposed method,two numerical examples via different damage scenarios are ***,a comparison between the proposed method and the existing damage identification method is *** outcomes reveal the high efficiency of the proposed method for finding the severity and location of joint damage considering noise effects.

关键词： damage identification beam-to-column connection time-domain response optimization

Impact damage identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

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Structural Durability & Health Monitoring 2023年第6期17卷 485-500页

作者： Wei Li Benjian Zou Yuxiang Luo Ning Yang Faye Zhang Mingshun Jiang Lei Jia School of Control Science and Engineering Shandong UniversityJinan250061China Communication Technology Division Shandong Institute of Space Electronic TechnologyYantai264000China

As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the *** this study,a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of ***,together with numerical simulation,the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed,to establish the damage ***,the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine(ELM)model to identify the impact location and damage degree,and the Gray Wolf Optimization(GWO)algorithm is employed to update the weight parameters of the ***,experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm×500 mm×10 mm,the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas,*** experiments with ELM and backpropagation neural networks(BPNN)demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.

关键词： GWO-ELM aluminum alloy stiffened plate damage identification amplitude-frequency characteristic

damage identification of General Overhead Travelling Crane Structure Based on Model Updating by Sensitivity

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第3期34卷 308-317页

作者： Qing Guangwei Yue Lin Guo Qingtao Tao Yanhe Hu Jingbo College of Mechanical and Electronical Engineering Nanjing University of Aeronautics and Astronautics Nanjing Special Equipment Inspection Institute

A model based damage identification was proposed by facilitating parameter sensitivity analysis and applied to a general overhead travelling *** updating reference data,experimental modal frequency was obtained by operational modal analysis(OMA)under ambient *** dimensional damage function was defined to identify the damage by bending *** results showed that the model updating method could locate the damage and quantitatively describe the *** average error of eigenvalues between updated model analysis and the experimental results was less than 4% which proved the accuracy *** comparison of finite element analysis and the test results of the deflection under the capacity load further verified the feasibility of this method.

关键词： model updating structure parameterization damage identification ambient excitation

damage identification under Incomplete Mode Shape Data Using Optimization Technique Based on Generalized Flexibility Matrix

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Journal of Applied Mathematics and Physics 2023年第12期11卷 3887-3901页

作者： Qianhui Gao Zhu Li Yongping Yu Shaopeng Zheng College of Construction Engineering Jilin University Changchun China

A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.

关键词： Generalized Flexibility Matrix damage identification Constrained Nonlinear Least Squares Trust-Region Algorithm Sensitivity Analysis Incomplete Modal Data

PROBABILISTIC damage identification OF STRUCTURES WITH UNCERTAINTY BASED ON DYNAMIC RESPONSES

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Acta Mechanica Solida Sinica 2014年第2期27卷 172-180页

作者： Xiaojun Wang Chen Yang Lei Wang Zhiping Qiu Institute of Solid Mechanics Beihang University QIAN-Xuesen Laboratory of Space Technology China Accademy of Space Technology

The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normally distributed random variables with zero mean value and given covariance. Based on the theoretical model and the measured acceleration responses, the probabilistic structural models in undamaged and damaged states are obtained by two-stage model updating, and then the Probabilities of damage Existence （PDE） of each element are calculated as the damage criterion. The influences of the location of sensors on the damage identification results are also discussed, where one of the optimal sensor placement techniques, the effective independence method, is used to choose the nodes for measurement. The damage identification results by different numbers of measured nodes and different damage criterions are compared in the numerical example.

关键词： damage identification model updating uncertainty probabilistic approach dynamic response

Research on FBG-Based CFRP Structural damage identification Using BP Neural Network

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Photonic Sensors 2018年第2期8卷 168-175页

作者： Xiangyi GENG Shizeng LU Mingshun JIANG Qingmei SUI Shanshan LV Hang XIAO Yuxi JIA Lei JIA School of Control Science and Engineering Shandong University Jinan 250061 China School of Electrical Engineering University of Jinan Jinan 250022 China Key Laboratory for Liquid-Solid Structural Evolution & Processing of Materials (Ministry of Education) Shandong University Jinan 250061 China

A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis （FEA）. At last, the damage identification system is verified on a CFRP plate with 300mm × 300mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

关键词： Carbon fiber reinforced polymer damage identification FBG sensors neural network finite elementanalysis