• 한국철도학회:학술대회논문집
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• 한국철도학회 2003년도 추계학술대회 논문집(II)
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• pp.489-494
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• 2003

A damage identification technique using static displacements was investigated to assess the structural integrity of bridge structures. For this, the optimization technique was utilized. In this study, structural damage was represented by the reduction in the stiffness of an element. Next, a health index of the element was introduced to estimate the stiffness reduction of the bridge under consideration. Comparisons with numerical and experimental tests were performed to investigate the applicability of the proposed method in the practical field. Various damage scenarios were considered by varying damage-width as well as damage-degree. The influence of noise on the damage identification scheme was also investigated numerically. Finally, the applicability and the limitation of the proposed method' were discussed.

• Smart Structures and Systems
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• 제23권2호
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• pp.195-205
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• 2019

An accurate non-model-based method for delamination identification of laminated composite plates is proposed in this work. A weighted mode shape damage index is formulated using squared weighted difference between a measured mode shape of a composite plate with delamination and one from a polynomial that fits the measured mode shape of the composite plate with a proper order. Weighted mode shape damage indices associated with at least two measured mode shapes of the same mode are synthesized to formulate a synthetic mode shape damage index to exclude some false positive identification results due to measurement noise and error. An auxiliary mode shape damage index is proposed to further assist delamination identification, by which some false negative identification results can be excluded and edges of a delamination area can be accurately and completely identified. Both numerical and experimental examples are presented to investigate effectiveness of the proposed method, and it is shown that edges of a delamination area in composite plates can be accurately and completely identified when measured mode shapes are contaminated by measurement noise and error. In the experimental example, identification results of a composite plate with delamination from the proposed method are validated by its C-scan image.

• Computational Structural Engineering : An International Journal
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• 제3권1호
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• pp.1-7
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• 2003

For system identification of large structures, it is not practical to identify the entire structure due to the prohibitive computational time and difficulty in numerical convergence. This paper explores the possibility of performing system identification at substructure level, taking advantage of reduction in both the number of unknowns and the number of degrees of freedom involved. Another advantage is that different portions (substructures) of a structural system can be identified independently and even concurrently with parallel computing. Two substructural identification methods are formulated on the basis whether substructural approach is used to obtain first-order or second-order model. For substructural first-order model, identification at the substructure level will be performed by means of the Observer/Kalman filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) whereas identification at the global level will be performed to obtain second-order model in order to evaluate the system's stiffness and mass parameters. In the case of substructural second-order model, identification will be performed at the substructure level throughout the identification process. The efficiency of the proposed technique is shown by numerical examples for multi-storey shear buildings subjected to random forces, taking into consideration the effects of noisy measurement data. The results indicate that both the proposed methods are effective and efficient for damage identification of large structures.

• Smart Structures and Systems
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• 제31권3호
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• pp.229-245
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• 2023

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

• Smart Structures and Systems
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• 제5권4호
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• pp.457-468
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• 2009

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

• Smart Structures and Systems
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• 제15권1호
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Smart Structures and Systems
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• 제23권5호
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.120-123
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• 2005

A dynamic system identification technique based on the topology optimization method is developed. The specific problem in consideration is the damage location identification of a plate structure using the Frequency Response Function (FRF) of a damaged structure. In this work, the identification problem is formulated as a topology optimization problem. The importance of using anti-resonance information in addition to using resonance information is addressed. Though a simple problem was considered here, the possibility of using the topology optimization for damage identification is investigated lot the first time.

• Structural Engineering and Mechanics
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• 제54권4호
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• pp.711-723
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• 2015

This study investigates the identification of added mass and its location in the glass fiber reinforced polymer (GFRP) beam structures. The main emphasis of this paper is to ascertain the importance of inclusion of rotational degrees of freedom (dofs) in the introduction of added mass or damage identification. Two identification indices that include the rotational dofs have been introduced in this paper: the modal force index (MFI) and the modal rotational curvature index (MRCI). The MFI amplifies damage signature using undamaged numerical stiffness matrix which is related to changes in the altered mode shapes from the original mode shapes. The MRCI is obtained by using a higher derivative of rotational mode shapes. Experimental and numerical results are compared with the existing methods leading to a conclusion that the contributions of the rotational modes play a key role in the identification of added mass. The authors believe that the similar results are likely in the case of damage identification also.

• Smart Structures and Systems
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• 제11권1호
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• pp.69-86
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• 2013

An efficient methodology using static test data and changes in natural frequencies is proposed to identify the damages in structural systems. The methodology consists of two main stages. In the first stage, the Damage Signal Match (DSM) technique is employed to quickly identify the most potentially damaged elements so as to reduce the number of the solution space (solution parameters). In the second stage, a particle swarm optimization (PSO) approach is presented to accurately determine the actual damage extents using the first stage results. One numerical case study by using a planar truss and one experimental case study by using a full-scale steel truss structure are used to verify the proposed hybrid method. The identification results show that the proposed methodology can identify the location and severity of damage with a reasonable level of accuracy, even when practical considerations limit the number of measurements to only a few for a complex structure.