Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process.

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Publication Details

Journal
Infrastructures
Published
2024-12-03
DOI
https://doi.org/10.3390/infrastructures9120218
Citations
4
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

Abdollah Malekjafarian, Ekin Özer, Ramin Ghiasi, Niall McSweeney
4 citations
Infrastructures
Structural Health Monitoring Techniques
1.04
article

Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements

Abdollah Malekjafarian, Ekin Özer, Ramin Ghiasi, Niall McSweeney
article en
4 citations

Abstract

In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process.

InfrastructuresVol. 9(12)
University College Dublin (IE)
Science Foundation Ireland
Openalex Percentile: Top 25%
Structural Health Monitoring Techniques
1.04
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