Design and characterization of a piezoelectric sensor for monitoring scour hole evolution

Azhari, Faezeh; Tom, Caroline; Benassini, Joseph L.; Loh, Kenneth J.; Bombardelli, Fabián A.

doi:10.1117/12.2044391

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…Another study focused on monitoring analyzed the difference between right and left accelerations of the train (i.e., roll component) which occurred in the presence of deformation, to prove the imbalance in the bridge behavior [41]. Mode Shape Ratio None [43] Vibration energy harvesting device Yes [44] Hilbert Huang Transform Yes [45] Fiber Optic Sensors Yes [46] Eigen frequency None [47] Frequency Domain Decomposition None [17] [ 23] Decentralized modal analysis Yes [48] Frequency analysis of piezoelectric rod sensors [49] Yes [50] [51]…”

Section: Conventional Monitoring-based and Machine Learning-based Met...mentioning

confidence: 99%

“…All the studies listed in Table 1 have numerical models except for [49]. There were a lot of studies focused on using accelerations of the bridge to identify scour.…”

Section: Conventional Monitoring-based and Machine Learning-based Met...mentioning

confidence: 99%

“…The research listed in Table 2 mainly focused on the monitoring device they used. [49], [50], [48], [44], and [45] aimed to measure scour depth with the sensors they used. [52] monitored sediment deposition processes.…”

Section: Direct Monitoring-based Studiesmentioning

confidence: 99%

“…[23] set fitting curves to the mode shapes and used root mean square (RMS) differences between healthy and damaged mode shapes to indicate scour. [48], [49], and [50] detected scour through changes in the fundamental frequencies of the polymer rods. [52] used the increasing permittivity of the soil to identify scour; as scour developed, the sensors' permittivity suddenly increased.…”

Section: Direct Monitoring-based Studiesmentioning

confidence: 99%

See 3 more Smart Citations

Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

et al. 2023

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Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.

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Section: Conventional Monitoring-based and Machine Learning-based Met...mentioning

confidence: 99%

“…All the studies listed in Table 1 have numerical models except for [49]. There were a lot of studies focused on using accelerations of the bridge to identify scour.…”

Section: Conventional Monitoring-based and Machine Learning-based Met...mentioning

confidence: 99%

Section: Direct Monitoring-based Studiesmentioning

confidence: 99%

Section: Direct Monitoring-based Studiesmentioning

confidence: 99%

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Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

et al. 2023

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“…However, there is no way to approve their operations. 5,6 Another type of sensor is a magnetic sliding collar sensor, which takes advantage of a manual or automated gravity-based physical probe resting on the streambed and moves downward as scour develops. A sufficiently large gravity rod should be selected to avoid its penetration into the streambed in static state.…”

Section: Introductionmentioning

confidence: 99%

Bridge pier scour level quantification based on output-only Kalman filtering

Ghorbani

Svecova

Thomson

et al. 2021

Structural Health Monitoring

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Soil scour near a bridge pier foundation is one of the leading causes of bridge failures. Traditional vibration-based scour monitoring methods are nearly incapable of quantifying scour levels using a single acceleration response without knowledge of excitation information. In this paper, a new output-only scour level prediction method is introduced via the integration of an unscented Kalman filter (UKF), random decrement (RD), and newly derived continuous Euler–Bernoulli beam addressing river water, traffic loads, and the linear and nonlinear behavior of sediments around the pier as external effects. We conducted extensive simulation studies and applied the method to an existing medium-span bridge with a steel girder and concrete deck in service in the province of Manitoba, Canada. These studies show that our proposed method can accurately estimate scour levels using only one accelerometer, which was validated with an independent bathymetric survey of the soil level at the pier foundation. Furthermore, three different linear and nonlinear soil profiles representing the soil behavior around the pier were also investigated as case studies in the scour level estimation process. The results confirm that a cubic function exhibits the best performance in quantifying the scour level around bridge piers.

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