• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.102-110
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• 2023

PSC-I girder bridges constitute the largest proportion among highway bridges in Korea. According to the precision safety diagnosis data for the past 10 years, approximately 41.3% of the PSC-I bridges have been graded as C. Furthermore, with the increase in the aging of bridges, preemptive management is becoming more important. Damage and deterioration to the deck and girder with a long replacement cylce can have considerable impacts on the service and deterioration of a bridge. In addition, the high rate of device damages, including expansion joints and bearings, necessitates an investigation into the influence of the device damage in the structural members of the bridge. Therefore, this study defined representative PSC-I girder bridges with single and multiple spans to evaluate heterogeneous damages that incorporate the damage of the bridge member and device with the deterioration of the deck. The heterogeneous damages increased a crack area ratio compared to the individual single damage. For the single-span bridge, the occurrence of bearing damage leads to the spread of crack distribution in the girder, and in the case of multi-span bridges, expansion joint damage leads to the spread of crack distribution in the deck. The research underscores that bridge devices, when damaged, can cause subsequent secondary damage due to improper repair and replacement, which emphasizes the need for continuous observation and responsive action to the damages of the main devices.

• 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.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• Magazine of the Korea Concrete Institute
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• v.6 no.2
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• pp.148-158
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• 1994

The goal of this study is to develop an equivalent fatigue load model for prestressed concrete composite girder bridges in Korea. To meet this goal, the probabilistic characteristics of traffics which cause fatigue damage in bridges are properly modeled. An equivalent fatigue load inodel for prestressed concrete composite girder bridges with constant. amplitude and frequency is established. The model proposed in this paper is very simple to use and gives fairly good results.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Journal of Korean Society of Steel Construction
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• v.10 no.2 s.35
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• pp.279-290
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• 1998

An existing Damage Index Method is verified to demonstrate its feasibility for detecting structural damage in truss bridges (1) for which modal parameters are available for a few modes of vibration and (2) for which baseline modal information is not available from its as-built state. The theory of approach to detect locations of damage and to identify baseline modal model is summarized on the basis of system identification theory and modal sensitivity theory. The feasibility of the Damage Index Method is demonstrated using a numerical example of a truss bridge with 11 subsystems of 211 members and for which only two modes of vibration were recorded for post-damaged state.

• 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.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.77.1-77.1
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• 2010

본 연구에서는 가속도기반 무선센서노드와 MEMS 가속도계 센서를 이용하여 철도교량의 구조건전성모니터링 기법들의 성능을 검토하였다. 이를 위해 다음과 같은 세 단계의 연구가 수행되었다. 첫째, 저가형 가속도기반 무선센서노드를 설계하였다. 둘째, 철도교량의 구조건전성모니터링을 위한 진동특성 및 건전성평가기법을 선정하였다. 마지막으로, 모형 강판형교에 대한 진동실험을 통해 개발된 가속도기반 무선센서노드의 성능을 평가하고 일련의 손상 시나리오를 대상으로 건전성평가 기법의 성능을 검토하였다. 시간영역 기법인 AR 모델과 주파수영역 기법인 파워스펙트럼 상관계수가 강판형교 구조손상 모니터링에 높은 정확도를 보였다.