Early Warning of Abnormal Train-Induced Vibrations for a Steel-Truss Arch Railway Bridge: Case Study

Zhao, Hanwei; Deng, Lu; Li, Aiqun; Wang, Manya

doi:10.1061/(asce)be.1943-5592.0001143

Cited by 22 publications

(10 citation statements)

References 20 publications

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“…As vehicle tonnage and truck traffic are increasing, full-load operation of various bridges has become more common [2]. According to Ding et al [3], various natural disasters and man-made influences have also led to many bridges that have not been maintained in time, leading to increased risks. Ensuring the safe operation of bridge structures has become an important issue of concern to the government and the public worldwide.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions

Kaewunruen

Sresakoolchai

et al. 2021

Sustainability

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Over the past centuries, millions of bridge infrastructures have been constructed globally. Many of those bridges are ageing and exhibit significant potential risks. Frequent risk-based inspection and maintenance management of highway bridges is particularly essential for public safety. At present, most bridges rely on manual inspection methods for management. The efficiency is extremely low, causing the risk of bridge deterioration and defects to increase day by day, reducing the load-bearing capacity of bridges, and restricting the normal and safe use of them. At present, the applications of digital twins in the construction industry have gained significant momentum and the industry has gradually entered the information age. In order to obtain and share relevant information, engineers and decision makers have adopted digital twins over the entire life cycle of a project, but their applications are still limited to data sharing and visualization. This study has further demonstrated the unprecedented applications of digital twins to sustainability and vulnerability assessments, which can enable the next generation risk-based inspection and maintenance framework. This study adopts the data obtained from a constructor of Zhongcheng Village Bridge in Zhejiang Province, China as a case study. The applications of digital twins to bridge model establishment, information collection and sharing, data processing, inspection and maintenance planning have been highlighted. Then, the integration of “digital twins (or Building Information Modelling, BIM) + bridge risk inspection model” has been established, which will become a more effective information platform for all stakeholders to mitigate risks and uncertainties of exposure to extreme weather conditions over the entire life cycle.

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Section: Introductionmentioning

confidence: 99%

Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions

Kaewunruen

Sresakoolchai

et al. 2021

Sustainability

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“…The temperature-induced strain is in the low-frequency band and the vehicle-induced strain is in the high-frequency band. 15,16 The decomposition scale of the wavelet transform can be determined by the frequency spectrum of the signal. 17 Assume that the signal frequency of structural temperature data is mainly concentrated at [0, f t ].…”

Section: Problem and Strategy Of The Evaluationfor The Existing Bridgesmentioning

confidence: 99%

“…Seo et al, 10,11 Bao et al, 12 Li et al, 13 and Yu and Cai 14 analyzed the load effects of bridge components using advanced signal and data processing method. Ding et al 15 and Zhao et al 16,17 conducted the real-time early warning of long-span bridges to monitor the safety of vehicle–bridge system. Wang et al, 18 Li and Ou, 19 Ni and Xia, 20 Zhao et al, 21 Zhu et al, 22 and Erazo et al 23 investigated the state evaluation methodology of the existing bridge structure based on the SHM system.…”

Section: Introductionmentioning

confidence: 99%

Live-load strain evaluation of the prestressed concrete box-girder bridge using deep learning and clustering

Zhao

Ding

et al. 2019

Structural Health Monitoring

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The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.

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“…3 Numerous studies have been conducted on the topic of structural condition monitoring for facilities subjected to external loads, including traffic loads and temperature changes. [4][5][6] Specifically, sensor data-driven structural condition monitoring methods have been frequently studied 1,7 as large datasets of structural responses (e.g., deflection, strain, and acceleration) and external loads (e.g., traffic loads or temperature) have become available with the rapid development of sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Lee

Jeong

Sim

et al. 2022

Structural Health Monitoring

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Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

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Early Warning of Abnormal Train-Induced Vibrations for a Steel-Truss Arch Railway Bridge: Case Study

Cited by 22 publications

References 20 publications

Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions

Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions

Live-load strain evaluation of the prestressed concrete box-girder bridge using deep learning and clustering

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

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