Determining and Investigating the Variability of Bridges’ Natural Frequencies with Ground-Based Radar

Michel, Chris; Keller, Sina

doi:10.3390/app12115354

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…Using an automated FDD algorithm, another paper developed an OMA tool for easily integrating default estimates of modal parameters into commercial software packages [80]. FDD was also compared with a ground-based interferometric radar approach for bridge assessment through natural frequency estimation from displacement measurements by Michel et al [81]. Moreover, these papers discussed the implementation of the FDD method [82][83][84][85][86].…”

Section: Frequency Domain Decomposition (Fdd)mentioning

confidence: 99%

Operational Modal Analysis on Bridges: A Comprehensive Review

Hasani,

Freddi

2023

Infrastructures

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Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings.

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Section: Frequency Domain Decomposition (Fdd)mentioning

confidence: 99%

Operational Modal Analysis on Bridges: A Comprehensive Review

Hasani,

Freddi

2023

Infrastructures

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“…For the application of microwave interferometry, good overviews are given by Gentile and Bernardini [ 12 ], Bernardini et al [ 13 ], Rödelsperger et al [ 24 ]. Microwave interferometry has also been applied to various types of structures in recent years: bridges [ 8 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], wind power plants [ 21 ], telecommunication towers [ 32 , 33 ], chimneys [ 24 ] or urban buildings [ 34 , 35 , 36 ]. Publications dealing with the performance evaluation through comparisons with conventional sensors are, for example, [ 8 , 35 , 37 , 38 ].…”

Section: Investigated Sensorsmentioning

confidence: 99%

Contactless Deformation Monitoring of Bridges with Spatio-Temporal Resolution: Profile Scanning and Microwave Interferometry

Schill

Michel

Firus³

2022

Sensors

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Against the background of an aging infrastructure, the condition assessment process of existing bridges is becoming an ever more challenging task for structural engineers. Short-term measurements and structural monitoring are valuable tools that can lead to a more accurate assessment of the remaining service life of structures. In this context, contactless sensors have great potential, as a wide range of applications can already be covered with relatively little effort and without having to interrupt traffic. In particular, profile scanning and microwave interferometry, have become increasingly important in the research field of bridge measurement and monitoring in recent years. In contrast to other contactless displacement sensors, both technologies enable a spatially distributed detection of absolute structural displacements. In addition, their high sampling rate enables the detection of the dynamic structural behaviour. This paper analyses the two sensor types in detail and discusses their advantages and disadvantages for the deformation monitoring of bridges. It focuses on a conceptual comparison between the two technologies and then discusses the main challenges related to their application in real-world structures in operation, highlighting the respective limitations of both sensors. The findings are illustrated with measurement results at a railway bridge in operation.

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“…Ground-based radar (GBR) has frequently been investigated in the context of bridge monitoring in recent years [4][5][6][7][8]. It achieves comparable or even better accuracy than established, conventional strain, or acceleration sensors [7] with the advantage of being remote and non-invasive. Thus, GBRs can be quickly set up for measurements.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Signal Processing for Bridge Traffic Classification with Radar Displacement Time-Series Data

Arnold,

Keller

2024

Infrastructures

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This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. In this study, GBR and an unmanned aerial vehicle (UAV) monitor a two-span bridge in Germany to gather ground-truth data. Based on the UAV data, we determine vehicle type, lane, locus, speed, axle count, and axle spacing for single-presence vehicle crossings. Since displacement is a global response, using peak detection like conventional strain-based BWIMs is challenging. Therefore, we investigate data-driven machine learning approaches to extract the vehicle configurations directly from the displacement data. Despite a small and imbalanced real-world dataset, the proposed approaches classify, e.g., the axle count for trucks with a balanced accuracy of 76.7% satisfyingly. Additionally, we demonstrate that, for the selected bridge, high-frequency vibrations can coincide with axles crossing the junction between the street and the bridge. We evaluate whether filtering approaches via bandpass filtering or wavelet transform can be exploited for axle count and axle spacing identification. Overall, we can show that GBR is a serious contender for BWIM systems.

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Determining and Investigating the Variability of Bridges’ Natural Frequencies with Ground-Based Radar

Cited by 6 publications

References 42 publications

Operational Modal Analysis on Bridges: A Comprehensive Review

Operational Modal Analysis on Bridges: A Comprehensive Review

Contactless Deformation Monitoring of Bridges with Spatio-Temporal Resolution: Profile Scanning and Microwave Interferometry

Machine Learning and Signal Processing for Bridge Traffic Classification with Radar Displacement Time-Series Data

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