• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2011년도 정기 학술발표대회
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• pp.39-39
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• 2011

This study presents an effective vision-based system for cable bridge damage detection. In theory, cable bridges need to be inspected the outer as well as the inner part. Starting from August 2010, a new research project supported by Korea Ministry of Land, Transportation Maritime Affairs(MLTM) was initiated focusing on the damage detection of cable system. In this study, only the surface damage detection algorithm based on a vision-based system will be focused on, an overview of the vision-based cable damage detection is given in Fig. 1. Basically, the algorithm combines the image enhancement technique with principal component analysis(PCA) to detect damage on cable surfaces. In more detail, the input image from a camera is processed with image enhancement technique to improve image quality, and then it is projected into PCA sub-space. Finally, the Mahalanobis square distance is used for pattern recognition. The algorithm was verified through laboratory tests on three types of cable surface. The algorithm gave very good results, and the next step of this study is to implement the algorithm for real cable bridges.

• Smart Structures and Systems
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• 제29권1호
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• pp.167-179
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• 2022

This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.

• 한국구조물진단유지관리공학회 논문집
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• 제27권6호
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• pp.172-181
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• 2023

본 연구에서는 케이블의 손상에 대한 사장교의 실시간 손상평가를 진행하였다. 사장교의 실시간 손상평가를 위한 센서는 가속도 센서를 사용하였으며, KEOT(Kinetic Energy Optimization Techniques)를 이용하여 센서의 위치와 수량에 대한 최적의 조건을 선정했다. KEOT는 구조물이 외력에 의해서 진동할 때, 최대변형에너지의 값을 계측하여 최적 계측 위치와 센서의 수량을 결정한다. 본 연구에서의 손상 조건은 케이블의 파단으로 제한하였으며 사장교를 4개의 구간으로 나누어 구간별 케이블 손상을 주었다. 사장교 케이블의 실시간 손상평가 방법은 FE 구조해석을 통하여 실제 모델과 유사한 가상의 모델을 만들었다. 생성된 가상 모델과 모형 구조물에 랜덤 가진파를 가한 이후 모형 구조물의 케이블 손상을 주었다. 가상 모델에서 출력되는 응답을 무손상 상태의 응답으로 정의하고 실제 모델에서 계측되는 응답을 손상 상태의 데이터로 정의하여 두 데이터를 비교하였다. 무손상 상태의 사장교의 데이터로부터 손상 상태의 사장교의 데이터를 IMD(Improved Mahalanobis Distance) 이론에 적용하여 손상의 정도를 평가하였다. IMD 이론으로 손상을 평가한 결과 구간별 손상을 실시간으로 적절하게 찾아내어 실시간 모니터링에 적용할 수 있는 유용한 손상평가 기술로 확인되었다.

• Smart Structures and Systems
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• 제17권6호
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• pp.859-880
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• 2016

This study aims to establish an effective methodology for the detection of instant damages occurred in cable-stayed bridges with the measurements of cable vibration and structural temperatures. A transfer coefficient for the daily temperature variation and another for the long-term temperature variation are firstly determined to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to decide four upper criteria and four lower criteria for damage detection. With these criteria, the monitoring data for three stay cables of Ai-Lan Bridge are analyzed and compared to verify the proposed damage detection methodology. The simulated results to consider various damage scenarios unambiguously indicate that the damages with cable force changes larger than ${\pm}1%$ can be confidently detected. As for the required time to detect damage, it is found that the cases with ${\pm}2%$ of cable force change can be discovered in no more than 6 hours and those with ${\pm}1.5%$ of cable force change can be identified in at most 9 hours. This methodology is also investigated for more lightly monitored cases where only the air temperature measurement is available. Under such circumstances, the damages with cable force changes larger than ${\pm}1.5%$ can be detected within 12 hours. Even though not exhaustively reflecting the environmental temperature effects on the cable force variation, both the effective temperature and the air temperature can be considered as valid indices to eliminate these effects at high and low monitoring costs.

• Smart Structures and Systems
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• 제29권1호
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• pp.41-51
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• 2022

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

• 한국산학기술학회논문지
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• 제19권1호
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• pp.127-135
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• 2018

최근 국내에서의 잦은 지진 발생으로 지진에 대한 관심이 증가함에 따라 이에 대한 해석법 및 손상도를 평가하는 여러 가지 방법이 제시되고 있다. 그러나 해석 및 손상도를 평가함에 있어 각각의 부재에 대한 변위와 부재력 뿐 아니라 실제 발생하는 지지에 대하여 구조물의 손상고를 정량적으로 평가하기에는 기준 및 방법이 미비한 실정이다. 따라서 본 논문에서는 케이블지지 교량인 사장교를 대상으로 실제지진하중 및 여러 가지 형태의 지진파에 대하여 지진해석을 수행하고 그 결과를 바탕으로 손상도 평가방법을 제시하였다. 손상지수는 사장교의 주탑 기울기를 기반으로 산정하였으며 물리적 내진손상 특성은 각각 A, B, C, D 레벨의 4단계로 제안하였다. 또한 지진해석방법에 따라 내진 손상지수가 항상 크거나 또는 작게 나온다고 단순하게 판단 할 수는 없었으며 본 연구는 사장교를 대상으로 하여 내진 손상지수의 제안 및 손상도 평가를 수행한 것이나 지진하중 하에서 비슷한 최대 변위응답 특성을 보이는 구조물에도 확장하여 적용할 수 있으리라 생각된다.

• Steel and Composite Structures
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• 제44권2호
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Earthquakes and Structures
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• 제8권5호
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• pp.1113-1126
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• 2015

A damage localization algorithm based on Operational Deformed Shapes and known as Interpolation Damage Detection Method, is herein applied to the finite element model of a cable stayed bridge for detecting and localizing damages in the stays and the supporting steel beams under the bridge deck. Frequency Response Functions have been calculated basing on the responses of the bridge model to low intensity seismic excitations and used to recover the Operational Deformed Shapes both in the transversal and in the vertical direction. The analyses have been carried in the undamaged configuration and repeated in several different damaged configurations. Results show that the method is able to detect the damage and its correct location, provided an accurate estimation of the Operational Deformed Shapes is available. Furthermore, the damage detection algorithm results effective also when damages coexist at the same time at several location of the cable-stayed bridge members.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2008년도 추계 학술논문 발표대회
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• pp.235-239
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• 2008

Cable systems are a construction of elements carrying only tension and no compression or bending in membrane structure. Tensile membrane structures are most often used as roofs as they can economically and attractively span large distances. But cable systems have weaknesses to vibration by earthquake, wind and vehicle loads. Damage detection of cable systems by using existing safety diagnosis is difficult to detect the characteristic change of overall structural action. If cable snaps are occurred to cable release and tear in tension structures, these are set up a vibration. So, we used piezo-electric materials, and The principle of operation of a piezoelectric sensor is that a physical dimension, transformed into a force, acts on two opposing faces of the sensing element. In this study, the development on test method of cable system is proposed and tested by tensile strength using piezo-electric materials.