S.J.S. Hakim

A Hybrid Procedure for Structural Damage Identification in Beam-Like Structures Using Wavelet Analysis

There are significant changes in the dynamic responses of damaged structures in which the damage depth becomes influential. This fact enables the recognition of damages in structures from their dynamic response data. This paper aims to identify damage locations as well as quantification of damage extent utilizing a vibration-based method. In this work, a signal-based damage identification procedure using the information entropy of wavelet packet energy is employed to evaluate beam-like structures. Damage indices called singular wavelet packet entropy and relative wavelet packet entropy are proposed to identify the location and to quantify the structural damage. The method involves two steps: firstly, the singular value of wavelet packet entropy, which reveals the abnormality distribution of the energy in signal time-frequency domain and secondly, relative wavelet packet entropy that is an acceptable damage feature to examine damage location and quantitative evaluation of the extent of damages through the vibration signals. The effects of wavelet type and decomposition level on the detection of damage location are examined. Numerical and experimental results from four test beams are used to verify the viability of this method. Results show that the damage indices method has great potential in identification of location and depth of cut in a steel beam. The procedure can be used for health monitoring of other complex structures.

Damage Detection Based on Wavelet Packet Transform and Information Entropy

In this study, a new approach for damage detection in beam-like structures is presented. Damage feature such as relative wavelet packet entropy (RWPE) based on the decomposed component is used to identify the damage location and evaluate the damage severity. RWPE describes present information of relative wavelet energies correlated with different frequency ranges. It is should be noted that the acceleration measured in the all three directions should be used in computation of RWPE. Numerical and experimental results are used to verify the practicality of this method. Results show that the damage index technique has great potential in detection of location and depth of cut in a steel beam. The procedure can be applied for health monitoring of other complex structures.

Application of artificial neural network on vibration test data for damage identification in bridge girder

Structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus, it is important to monitor structures for the occurrence, location and extent of damage. Artificial neural networks (ANNs) as a numerical technique have been applied increasingly for damage identification with varied success. ANNs are inspired by human biological neurons and have been used to model some specific problems in many areas of engineering and science to achieve reasonable results. ANNs have the ability to learn from examples and then adapt to changing situations when sufficient input-output data are available. This paper presents the application of ANNs for detection of damage in a steel girder bridge using natural frequencies as dynamic parameters. Dynamic parameters are easy to implement for damage assessment and can be directly linked to the topology of structure. In this study, the required data for the ANNs in the form of natural frequencies will be obtained from experimental modal analysis. This paper also highlights the concept of ANNs followed by the detail presentation of the experimental modal analysis for natural frequencies extraction. ©2011 Academic Journals.

Damage Detection Optimization Using Wavelet Multiresolution Analysis and Genetic Algorithm

In this study, an optimized damage detection using genetic algorithm (GA) in beam-like structures without using baseline data. For this purpose, a vibration-based damage detection algorithm using a damage indicator called ‘Relative Wavelet Packet Entropy’ (RWPE) was applied to determine the location and severity of damage. To improve the algorithm, GA was utilized to optimize the algorithm so as to identify the best choice of wavelet parameters. To examine the robustness and accuracy of the proposed method, numerical examples and experimental cases with different damage depths were considered and conducted.