Development and Validation of a Data-Based SHM Method for Railway Bridges

Neves, Ana Cláudia; González, Ignacio; Karoumi, Raid

doi:10.1007/978-3-030-81716-9_5

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…These recent monitoring techniques and related data interpretations have been explored with application to both highway and railway bridges [24]. Artificial neural networks have been proposed for classifying bridge health and damage states using deck acceleration and bridge weighin-motion data [25], [26]. Long-short-term-memory neural networks and other deep neural network have been explored focusing on reducing the number of false alarms due to sensor failures [27].…”

Section: A Related Workmentioning

confidence: 99%

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Cheema,

Sarwar,

Gogineni

et al. 2024

IEEE Sensors J.

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Structural health monitoring (SHM) of bridges is crucial for ensuring safety and long-term durability, however, standard damage-detection algorithms are computationally intensive. This paper proposes a computationally-efficient algorithm based on graph signal processing (GSP) to leverage the underlying network structure in the data. Under the assumption that damages impact both spatial and temporal structures of the sensors data, the algorithm combines spatial and temporal information from accelerometers by computing the smoothness of graph signals expanded along time. The Kullback-Leibler (KL) divergence is used as dissimilarity metric to distinguish between healthy condition and presence of a damage, while Tukey's method for outliers removal and sequential detection via exponential Weighted moving average (EWMA) are then employed for performance improvement. The proposed GSP-based SHM system is appealing in terms of simplicity and low-complexity, and is also suitable for realtime monitoring. The effectiveness in terms of detection performance is validated both on synthetically-generated data and real-world measurements.

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Section: A Related Workmentioning

confidence: 99%

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Cheema,

Sarwar,

Gogineni

et al. 2024

IEEE Sensors J.

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“…There is no need to develop a model of the system behavior, and the simplicity of implementation makes data-based SHM suitable for various structures. Data-based methodologies are entirely data-driven and do not provide any information regarding the physical processes underlying the data evolution [ 345 ]. Further, they operate inadequately when attempting to determine the character of a change, such as damage progression [ 346 ].…”

Section: Shm Systemsmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

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This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

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“…SHM of bridges has been the interest of many researchers, thus, state-of-the-art research is available on this topic [11], [12]. The proposal of the SHM system considers FEA results and outcomes of load tests because experimental studies in the field and analytical studies using the FEA framework provide the decision-making for a proper SHM system for the bridge [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study

Fawad,

Salamak,

Hanif

et al. 2024

IEEE Access

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In recent times, digital transformations and Industry 4.0 have revolutionized real-time bridge monitoring and its inspection. The use of smart Structural Health Monitoring (SHM) techniques is becoming powerful with the competencies of Building Information Modeling (BIM) tools, Artificial Intelligence (AI), Internet of Things (IoT), and Virtual/Augmented (VR/AR) technologies. However, the lack of interconnectivity between these tools limits their functionality. This research has addressed this problem by developing an integrated framework to assess serviceability and implement a smart SHM for a newly constructed extradosed bridge. Using Finite Element Analysis (FEA), the study proposes an integrated SHM system that utilizes various IoT sensors, including Wired Strain Gauges (WSG), Liquid Levelling Sensors (LLS), MEMS accelerometers, and a Weather Monitoring Station (WMS) to monitor concrete deformations, vertical displacements, structural vibrations, and weather conditions. BIM tool is used to develop the virtual replica of the proposed SHM system which is then used in the 3D Game Engine (GE) to develop an AR application. This application is then successfully deployed and tested in the AR headset (HoloLens) where its capabilities for onsite bridge health monitoring are discovered. This approach overcomes the limitations of HoloLens devices by providing real-time access to SHM data through a web platform, enabling on-site or remote AR-based bridge health monitoring. Conclusively, this paper emphasizes the numerical modeling of bridges for the design of a health monitoring system, that highlights the importance of robust SHM techniques in assessing bridge conditions. Moreover, it introduces a novel approach for smart bridge inspection and onsite visualization of structural defects in an AR environment.

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Development and Validation of a Data-Based SHM Method for Railway Bridges

Cited by 8 publications

References 14 publications

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

Computationally Efficient Structural Health Monitoring Using Graph Signal Processing

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study

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