• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.1
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• pp.165-174
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• 2010

In this paper, a two-phase structural health monitoring system using acceleration response signatures are presented to firstly alarm the change in structural condition and to secondly detect the changed location for full-scale concrete girder bridges. Firstly, Mihocheon Bridge which is a two-span continuous concrete girder bridge is selected as the target structure. The dynamic response features of Mihocheon Bridge are extracted by forced vibration test using bowling ball. Secondly, the damage alarming occurrence and the damage localization techniques are selected to design two-phase structural health monitoring system for Mihocheon Bridge. As the damage alarming techniques, auto-regressive model using time-domain signatures, correlation coefficient of frequency response function and frequency response ratio assurance criterion are selected. As the damage localization technique, modal strain energy-based damage index method is selected. Finally, the feasibility of two-phase structural health monitoring systems is evaluated from static loading tests using a dump truck.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• 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.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.337-362
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• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

• Steel and Composite Structures
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• v.19 no.2
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• pp.385-403
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• 2015

This paper presents the status improvement of an old damaged deck type rural road steel truss bridge through the modal parametric study after partial retrofitting. The dynamic and static tests on bridge were carried out as in damaged state and after partial retrofitting. The dynamic testing on the steel bridge was carried out using accelerometers under similar environmental conditions with same speed of the moving vehicle. The comparison of the modal parameters i.e., frequency, mode shape mode shape curvature, modal strain energy, along with the deflection parameter are studied with respect to structural analytical model parameters. The status up gradation for the upper and downstream truss obtained was different due to differential level of damage in the bridge. Also after retrofitting the structural elemental behavior obtained was not same as desired. The damage level obtained through static tests carried out using total station indicated further retrofitting requirement.

• Smart Structures and Systems
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• v.21 no.4
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• pp.449-467
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• 2018

In this paper, a theoretical and numerical study on bridge simultaneous damage detection procedure for identifying both the system parameters and input excitation mass, are presented. This method is called 'Adjoint Variable Method' which is an iterative gradient-based model updating method based on the dynamic response sensitivity. The main advantage of proposed method is inclusion of an analytical method to augment the accuracy and speed of the solution. Moving mass is a model which takes into account the inertia effects of the vehicle. This interaction model is a time varying system and proposed method is capable of detecting damage in this variable system. Robustness of proposed method is illustrated by correctly detection of the location and extension of predetermined single, multiple and random damages in all ranges of speed and mass ratio of moving vehicle. A comparison study of common sensitivity and proposed method confirms its efficiency and performance improvement in sensitivity-based damage detection methods. Various sources of errors including the effects of measurement noise and initial assumption error in stability of method are also discussed.

• Earthquakes and Structures
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• v.20 no.2
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• pp.201-213
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• 2021

Contrast to the conventional jointed bridge design, integral abutment bridges (IABs) offer some marked advantages like reduced maintenance and enhanced service life of the structure due to elimination of joints in the deck and monolithic construction practices. However, the force transfer mechanism during seismic and thermal movements is a topic of interest owing to rigid connection between superstructure and substructure (piers and abutments). This study attempts to model an existing IAB by including the abutment backfill interaction and soil-foundation interaction effects using Winkler foundation assumption to determine its seismic response. Keeping in view the significance of abutment behavior in an IAB, the probability of damage to the abutment is evaluated using fragility function. Incremental Dynamic Analysis (IDA) approach is used in this regard, wherein, nonlinear time history analyses are conducted on the numerical model using a selected suite of ground motions with increasing intensities until damage to abutment. It is concluded from the fragility analysis results that for a MCE level earthquake in the location of integral bridge, the probability of complete damage to the abutment is minimal.

• Journal of Korean Society of Steel Construction
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• v.8 no.4 s.29
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• pp.43-50
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• 1996

Damage estimation of bridge structures has recently received considerable attention in the light of maintenance and retrofitting of existing structures under service loads and after natural disasters. A method for the damage assessment of bridge structures using a damage index technique is presented. The damage index is formulated for the changes of modal properties due to the change of the stiffness. In order to verify the method which is presented, numerical analysis is conducted on simple beam models. Each FE model is subjected to different damage scenarios, i.e., locations and degrees of damage. Results of numerical analysis indicate that the proposed method is capable of detecting inflicted damages using the eigenvalue of only first mode.

• Smart Structures and Systems
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• v.5 no.4
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• pp.495-505
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• 2009

Bridges form crucial links in the transportation network especially in high seismic risk regions. This research aims to provide a quantitative methodology for post-earthquake performance evaluation of the bridges. The experimental portion of the research involved shake table tests of a 4-span bridge which was subjected to progressively increasing amplitudes of seismic motions recorded from the Northridge earthquake. As part of this project, a high resolution long gauge fiber optic displacement sensor was developed for post-seismic evaluation of damage in the columns of the bridge. The nonlinear finite element model was developed using Opensees program to simulate the response of the bridge and the abutments to the seismic loads. The model was modified to predict the bent displacements of the bridge commensurate with the measured bent displacements obtained from experimental analysis results. Following seismic events, the tangential stiffness matrix of the whole structure is reduced due to reduction in structural strength. The nonlinear static push over analysis using current damaged stiffness matrix provides the longitudinal and transverse ultimate capacities of the bridge. Capacity loss in the transverse and longitudinal directions following the seismic events was correlated to the maximum displacements of the deck recorded during the events.