• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• Journal of Korean Society of Steel Construction
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• v.11 no.1 s.38
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• pp.79-88
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• 1999

Damages of a steel box girder bridge are detected using neural networks. Damage detection using neural networks has increasing momentum in structural engineering. It is a new effort to overcome the limitations of the conventional analytical approaches and applied to the damage detection of a steel box-girder bridge. Data sets for training neural networks are obtained from the acceleration response of the bridge under moving load. Finite element model is first defined and damages of 5, 10, 15 and 20% are assumed in the model. Not only the trained damages but untrained damages are detected in the assessment stage. The untrained damages can be detected with acceptable errors. Because the number of damaged locations are limited to a few parts, more researches are needed to put this technique into practice.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.5
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• pp.271-280
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• 2018

This paper presents a framework for developing aftershock fragility curves for reinforced concrete bridges initially damaged by mainshocks. The presented aftershock fragility is a damage-dependent fragility function, which is conditioned on an initial damage state resulting from mainshocks. The presented framework can capture the cumulative damage of as-built bridges due to mainshock-aftershock sequences as well as the reduced vulnerability of bridges repaired with CFRP pier jackets. To achieve this goal, the numerical model of column jackets is firstly presented and then validated using existing experimental data available in literature. A four-span concrete box-girder bridge is selected as a case study to examine the application of the presented framework. The aftershock fragility curves are derived using response data from back-to-back nonlinear dynamic analyses under mainshock-aftershock sequences. The aftershock fragility curves for as-built bridge columns are firstly compared with different levels of initial damage state, and then the post-repair effect of FRP pier jacket is examined through the comparison of aftershock fragility curves for as-built and repaired piers.

• Structural Engineering and Mechanics
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• v.40 no.5
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• pp.719-743
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• 2011

A hybrid damage monitoring scheme using parallel acceleration-impedance approaches is proposed to detect girder damage and support damage in steel plate-girder bridges which are under ambient train-induced excitations. The hybrid scheme consists of three phases: global and local damage monitoring in parallel manner, damage occurrence alarming and local damage identification, and detailed damage estimation. In the first phase, damage occurrence in a structure is globally monitored by changes in vibration features and, at the same moment, damage occurrence in local critical members is monitored by changes in impedance features. In the second phase, the occurrence of damage is alarmed and the type of damage is locally identified by recognizing patterns of vibration and impedance features. In the final phase, the location and severity of the locally identified damage are estimated by using modal strain energy-based damage index methods. The feasibility of the proposed scheme is evaluated on a steel plate-girder bridge model which was experimentally tested under model train-induced excitations. Acceleration responses and electro-mechanical impedance signatures were measured for several damage scenarios of girder damage and support damage.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.5
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• pp.19-27
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• 2002

Dynamic responses of a multi-span simply supported bridge are investigated to examine the effect of bearing damage under seismic excitations. The damaged bearings are modeled as sliding elements with friction between the superstructure and the top of the pier. Various values of the friction coefficients are examined to figure out the effect of damaged bearings with various levels of peak ground accelerations. It is found that the global seismic behaviors are significantly influenced by the occurrence of bearing damage. It should be noticed that the most possible location of unseating failure of superstructures differs from that in the bridge model without considering the bearing damage. It can be concluded that the bearing damage may play the major role in the unseating failure of a bridge system, so that the damage of bearings should be included to achieve more rational seismic safety evaluation.

• 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.

• Proceedings of the KSR Conference
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• 2003.10b
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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.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.257-289
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• 2015

Shear connectors are generally used to link the slab and girders together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girders, which significantly reduces the load-carrying capacity of the bridge. Because shear connectors are buried inside the structure, routine visual inspection is not able to detect conditions of shear connectors. A few methods have been proposed in the literature to detect the condition of shear connectors based on vibration measurements. This paper proposes a different dynamic condition assessment approach to identify the damage of shear connectors in slab-on-girder bridge structures based on power spectral density transmissibility (PSDT). PSDT formulates the relationship between the auto-spectral densities of two responses in the frequency domain. It can be used to identify shear connector conditions with or without reference data of the undamaged structure (or the baseline). Measured impact force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at sensor locations by experimental modal analysis. PSDT from the slab response to the girder response is derived with the obtained frequency response functions. PSDT vectors in the undamaged and damaged states can be compared to identify the damage of shear connectors. When the baseline is not available, as in most practical cases, PSDT vectors from the measured response at a reference sensor to those of the slab and girder in the damaged state can be used to detect the damage of shear connectors. Numerical and experimental studies on a concrete slab supported by two steel girders are conducted to investigate the accuracy and efficiency of the proposed approach. Identification results demonstrate that damages of shear connectors are identified accurately and efficiently with and without the baseline. The proposed method is also used to evaluate the conditions of shear connectors in a real composite bridge with in-field testing data.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.5 s.45
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• pp.63-74
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• 2005

The purpose of this study is to assess the seismic performance of reinforced concrete bridge columns using inelastic finite element analysis. The accuracy and objectivity of the assessment process may be enhanced by the use of sophisticated nonlinear finite element analysis program. A computer program, named RCAHEST (Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Damage index aims to provide a means of quantifying numerically the damage in reinforced concrete bridge columns sustained under earthquake loading. The proposed numerical method for the seismic performance assessment of reinforced concrete bridge columns is verified by comparison with reliable experimental results.