• Structural Engineering and Mechanics
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• v.58 no.6
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• pp.1087-1107
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• 2016

A method of damage detection based on the moving harmonic excitation and continuous wavelet transforms is presented. The applied excitation is used as a moving actuator and its frequency and speed parameters can be adjusted for an amplified response. The continuous wavelet transforms, CWT, is used for cracks detection based on the resulting amplified signal. It is demonstrated that this identification procedure is largely better than the classical ones based on eigenfrequencies or on the eigenmodes wavelet transformed. For vibration responses, free and forced vibration analyses of multi-cracked beams are investigated based on both analytical and numerical methodological approaches. Cracks are modeled through rotational springs whose compliances are evaluated using linear elastic fracture mechanics. Based on the obtained forced responses, multi-cracks positions are accurately identified and the CWT identification can be highly improved by adjusting the frequency and the speed excitation parameters.

• Structural Engineering and Mechanics
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• v.51 no.2
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• pp.299-313
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• 2014

In the first part of this paper, the influences of some of crack parameters on natural frequencies of a cracked cantilever Functionally Graded Beam (FGB) are studied. A cantilever beam is modeled using Finite Element Method (FEM) and its natural frequencies are obtained for different conditions of cracks. Then effect of variation of depth and location of cracks on natural frequencies of FGB with single and multiple cracks are investigated. In the second part, two Multi-Layer Feed Forward (MLFF) Artificial Neural Networks (ANNs) are designed for prediction of FGB's Cracks' location and depth. Particle Swarm Optimization (PSO) and Back-Error Propagation (BEP) algorithms are applied for training ANNs. The accuracy of two training methods' results are investigated.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• Smart Structures and Systems
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• v.16 no.1
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• pp.47-65
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• 2015

A novel finite element (FE) model updating method based on multi-resolution analysis (MRA) is proposed. The true stiffness of the FE model is considered as the superposition of two pieces of stiffness information of different resolutions: the pre-defined stiffness information and updating stiffness information. While the resolution of former is solely decided by the meshing density of the FE model, the resolution of latter is decided by the limited information obtained from the experiment. The latter resolution is considerably lower than the former. Second generation wavelet is adopted to describe the updating stiffness information in the framework of MRA. This updating stiffness in MRA is realized at low level of resolution, therefore, needs less number of updating parameters. The efficiency of the optimization process is thus enhanced. The proposed method is suitable for the identification of multiple irregular cracks and performs well in capturing the global features of the structural damage. After the global features are identified, a refinement process proposed in the paper can be carried out to improve the performance of the MRA of the updating information. The effectiveness of the method is verified by numerical simulations of a box girder and the experiment of a three-span continues pre-stressed concrete bridge. It is shown that the proposed method corresponds well to the global features of the structural damage and is stable against the perturbation of modal parameters and small variations of the damage.

• Steel and Composite Structures
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• v.28 no.3
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• pp.279-288
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• 2018

In this paper, a new method has been proposed to detect crack in beam structures under moving mass using regularized extreme learning machine. For this purpose, frequencies of beam under moving mass used as input to train machine. This data is acquired by the analysis of cracked structure applying the finite element method (FEM). Also, a validation study used for verification of the FEM. To evaluate performance of the presented method, a fixed simply supported beam and two span continuous beam are considered containing single or multi cracks. The obtained results indicated that this method can provide a reliable tool to accurately identify cracks in beam structures under moving mass.