• Smart Structures and Systems
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• v.14 no.2
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• pp.105-128
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• 2014

The stochastic damage locating vector (SDLV) method has been studied extensively in recent years because of its potential to determine the location of damage in structures without the need for measuring the input excitation. The SDLV method has been shown to be a particularly useful tool for damage localization in steel truss bridges through numerical simulation and experimental validation. However, several issues still need clarification. For example, two methods have been suggested for determining the observation matrix C identified for the structural system; yet little guidance has been provided regarding the conditions under which the respective formulations should be used. Additionally, the specific layout of the sensors to achieve effective performance with the SDLV method and the associated relationship to the specific type of truss structure have yet to be explored. Moreover, how the location of truss members influences the damage localization results should be studied. In this paper, these three issues are first investigated through numerical simulation and subsequently the main results are validated experimentally. The results of this paper provide guidance on the effective use of the SDLV method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Smart Structures and Systems
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• v.12 no.6
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• pp.679-694
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• 2013

In this paper the application of the Interpolation Damage Detection Method to the numerical model of a suspension bridge instrumented with a network of Micro-Electro-Mechanical System sensors is presented. The method, which, in its present formulation, belongs to Level II damage identification method, can identify the presence and the location of damage from responses recorded on the structure before and after a seismic damaging event. The application of the method does not require knowledge of the modal properties of the structure nor a numerical model of it. Emphasis is placed herein on the influence of recorded signals noise on the reliability of the results given by the Interpolation Damage Detection Method. The response of a suspension bridge to seismic excitation is computed from a numerical model and artificially corrupted with random noise characteristic of two families of Micro-Electro-Mechanical System accelerometers. The reliability of the results is checked for different damage scenarios.

• Computers and Concrete
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• v.4 no.5
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• pp.377-402
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• 2007

The paper presents quasi-static plane strain FE-simulations of strain localization in reinforced concrete beams without stirrups. The material was modeled with two different isotropic continuum crack models: an elasto-plastic and a damage one. In case of elasto-plasticity, linear Drucker-Prager criterion with a non-associated flow rule was defined in the compressive regime and a Rankine criterion with an associated flow rule was adopted in the tensile regime. In the case of a damage model, the degradation of the material due to micro-cracking was described with a single scalar damage parameter. To ensure the mesh-independence and to capture size effects, both criteria were enhanced in a softening regime by nonlocal terms. Thus, a characteristic length of micro-structure was included. The effect of a characteristic length, reinforcement ratio, bond-slip stiffness, fracture energy and beam size on strain localization was investigated. The numerical results with reinforced concrete beams were quantitatively compared with corresponding laboratory tests by Walraven (1978).

• Smart Structures and Systems
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• v.30 no.4
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• pp.397-410
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• 2022

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.938-942
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• 2007

The proposed algorithm tries to localize damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides an effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• Smart Structures and Systems
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• v.3 no.1
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• pp.23-49
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• 2007

Wavelet transforms are the emerging signal-processing tools for damage identification and time-frequency localization. A small perturbation in a static or dynamic displacement profile could be captured using multi-resolution technique of wavelet analysis. The paper presents the wavelet analysis of damaged linear structural elements using DB4 or BIOR6.8 family of wavelets. Starting with a localized reduction of EI at the mid-span of a simply supported beam, damage modeling is done for a typical steel and reinforced concrete beam element. Rotation and curvature mode shapes are found to be the improved indicators of damage and when these are coupled with wavelet analysis, a clear picture of damage singularity emerges. In the steel beam, the damage is modeled as a rotational spring and for an RC section, moment curvature relationship is used to compute the effective EI. Wavelet analysis is performed for these damage models for displacement, rotation and curvature mode shapes as well as static deformation profiles. It is shown that all the damage indicators like displacement, slope and curvature are magnified under higher modes. A localization scheme with arbitrary location of curvature nodes within a pseudo span is developed for steady state dynamic loads, such that curvature response and damages are maximized and the scheme is numerically tested and proved.

• Smart Structures and Systems
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• v.5 no.3
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• pp.291-316
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• 2009

Damage detection using structural classical deflection flexibility has received considerable attention due to the unique features of the flexibility in the last two decades. However, for relatively complex structures, most methods based on classical deflection flexibility fail to locate damage sites to the exact members. In this study, for structures whose members are dominated by axial forces, such as truss structures, a more feasible flexibility for damage detection is proposed, which is called the Axial Strain (AS) flexibility. It is synthesized from measured modal frequencies and axial strain mode shapes which are expressed in terms of translational mode shapes. A damage indicator based on AS flexibility is proposed. In addition, how to integrate the AS flexibility into the Damage Location Vector (DLV) approach (Bernal and Gunes 2004) to improve its performance of damage localization is presented. The methods based on AS flexbility localize multiple damages to the exact members and they are suitable for the cases where the baseline data of the intact structure is not available. The proposed methods are demonstrated by numerical simulations of a 14-bay planar truss and a five-story steel frame and experiments on a five-story steel frame.

• Smart Structures and Systems
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• v.31 no.6
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• pp.561-571
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• 2023

Ensuring the safety of a structure necessitates that repairs are carried out based on accurate inspections and records of damage information. Traditional methods of recording damage rely on individual paper-based documents, making it challenging for inspectors to accurately record damage locations and track chronological changes. Recent research has suggested the adoption of building information modeling (BIM) to record detailed damage information; however, localizing damages on a BIM model can be time-consuming. To overcome this limitation, this study proposes a method to automatically localize damages on a BIM model in real-time, utilizing consecutive images and measurements from an inertial measurement unit in close proximity to damages. The proposed method employs a visual-inertial odometry algorithm to estimate the camera pose, detect damages, and compute the damage location in the coordinate of a prebuilt BIM model. The feasibility and effectiveness of the proposed method were validated through an experiment conducted on a campus building. Results revealed that the proposed method successfully localized damages on the BIM model in real-time, with a root mean square error of 6.6 cm.