Damage detection of steel girder railway bridges utilizing operational vibration response

Azim, Riasat; Gül, Mustafa

doi:10.1002/stc.2447

Cited by 34 publications

(27 citation statements)

References 31 publications

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“…Azim & Gül [22] presented a sensor-clustering-based time series analysis method for continuous global monitoring of girder-type railway bridges using operational data. The method consisted of applying an autoregressive moving average model with exogenous inputs to analyse the free vibration response of the bridge in order to extract damage features.…”

Section: Introductionmentioning

confidence: 99%

Damage detection in railway bridges using traffic-induced dynamic responses

Meixedo

Santos

Ribeiro

et al. 2021

Engineering Structures

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This paper aims at detecting damage in railway bridges based on traffic-induced dynamic responses. To achieve this goal, an unsupervised automatic data-driven methodology is proposed, consisting of a combination of time series analysis methods and multivariate statistical techniques. Damage-sensitive features of train-induced responses are extracted and allow taking advantage, not only of the repeatability of the loading, but also, and more importantly, of its great magnitude, thus enhancing the sensitivity to small-magnitude structural changes.The efficiency of the proposed methodology is validated in a long-span steel-concrete composite bowstring-arch railway bridge with a permanent structural monitoring system installed. An experimentally validated finite element model was used, along with experimental values of temperature, noise, and train loadings and speeds, to realistically simulate baseline and damage scenarios.The proposed methodology proved to be highly sensitive in detecting early damage, even when it consists of small stiffness reductions that do not impair the safety or use of the structure, and highly robust to false detections. The analysis and validation allowed concluding that the ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by variations in train speed or temperature, was achieved by carefully defining the modelling and fusion sequence of the information. A single-value damage indicator, proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition, proved capable of characterizing multi-sensor data while being sensitive to identify local changes.

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Section: Introductionmentioning

confidence: 99%

Damage detection in railway bridges using traffic-induced dynamic responses

Meixedo

Santos

Ribeiro

et al. 2021

Engineering Structures

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“…[13][14][15] First step of bridge condition assessment is data collection. Most conventional methods rely on fixed sensors to collect data from bridges (see, e.g., literature [16][17][18][19][20][21][22] ). The efficiency and performance of these methods have been examined and demonstrated in those studies.…”

Section: Introductionmentioning

confidence: 99%

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

Mei

Shirzad-Ghaleroudkhani

Gül

et al. 2021

Struct Control Health Monit

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Summary Advances in smart infrastructure produces a natural demand of system identification techniques for structural health and performance monitoring that can be scaled to regions and large asset inventories. Conventional approaches require sensors to be installed, often in long‐term deployments, on the monitored infrastructure systems, which is a costly undertaking when thousands of systems (e.g., bridges) need to be monitored. This paper presents a novel mode shape identification method for bridges that uses data collected from moving vehicles as input—a paradigm that can overcome limitations associated with conventional approaches. The method consists of two steps. First, the data collected from moving measurement points are mapped to virtual fixed points to generate a sparse matrix. Then, a “soft‐imputing” technique is employed to fill the sparse matrix. Finally, a singular value decomposition is applied to extract the mode shapes of the bridge. Experiments with synthetic, yet realistic, data are conducted to verify the method. The sensitivity of the proposed approach to different factors, including the number of vehicles, car speed, road roughness, and measurement errors, are also investigated. The results show that the proposed method is capable of identifying the mode shapes of the bridge accurately.

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“…In relation to passengers' satisfactory experience in the train, it is also essential to offer a very high degree of comfortability, avoiding train wobbles or sudden movements. Many sensors and electronics are being used to supervise railway dynamics and infrastructures [4], focusing on safety assurance and contributing to the condition-based maintenance topic. Generally, the three main reasons that cause a feeling of comfort loss in passengers are: punctual track irregularities that generate acceleration peaks [5], a non-optimized guidance of the train that can cause a constant loss of comfort over time [6], and the malfunctioning of any of the components implicated in the dynamic behaviour of the train (damping and suspension) that can also cause an extended loss of comfort.…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

et al. 2021

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Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s2 is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s2). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.

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Damage detection of steel girder railway bridges utilizing operational vibration response

Cited by 34 publications

References 31 publications

Damage detection in railway bridges using traffic-induced dynamic responses

Damage detection in railway bridges using traffic-induced dynamic responses

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

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