A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification
Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2528 |
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| author | Liam Cronin Debarshi Sen Giulia Marasco Iman Dabbaghchian Lorenzo Benedetti Thomas Matarazzo Shamim Pakzad |
| author_facet | Liam Cronin Debarshi Sen Giulia Marasco Iman Dabbaghchian Lorenzo Benedetti Thomas Matarazzo Shamim Pakzad |
| author_sort | Liam Cronin |
| collection | DOAJ |
| description | Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience. |
| format | Article |
| id | doaj-art-d7c0d2c85fab4e95bcdd284ccf132119 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d7c0d2c85fab4e95bcdd284ccf1321192025-08-20T02:18:10ZengMDPI AGSensors1424-82202025-04-01258252810.3390/s25082528A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal IdentificationLiam Cronin0Debarshi Sen1Giulia Marasco2Iman Dabbaghchian3Lorenzo Benedetti4Thomas Matarazzo5Shamim Pakzad6Department of Civil and Environmental Engineering, Lehigh University, 117 ATLSS Dr., Bethlehem, PA 18015, USADepartment of Civil Engineering, Southern Illinois University Carbondale, 1263 Lincoln Dr., Carbondale, IL 62901, USADepartment of Civil and Environmental Engineering, Lehigh University, 117 ATLSS Dr., Bethlehem, PA 18015, USADepartment of Civil and Environmental Engineering, Lehigh University, 117 ATLSS Dr., Bethlehem, PA 18015, USADisplaid, Via Carlo Freguglia, 2, 20122 Milano, ItalyDepartment of Mechanical Engineering, United States Military Academy, 606 Thayer Rd., West Point, NY 10996, USADepartment of Civil and Environmental Engineering, Lehigh University, 117 ATLSS Dr., Bethlehem, PA 18015, USAVibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience.https://www.mdpi.com/1424-8220/25/8/2528mobile sensingcrowdsourcingsystem identificationbridge monitoringride-sharesmartphone |
| spellingShingle | Liam Cronin Debarshi Sen Giulia Marasco Iman Dabbaghchian Lorenzo Benedetti Thomas Matarazzo Shamim Pakzad A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification Sensors mobile sensing crowdsourcing system identification bridge monitoring ride-share smartphone |
| title | A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification |
| title_full | A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification |
| title_fullStr | A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification |
| title_full_unstemmed | A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification |
| title_short | A Roadmap for Ubiquitous Crowdsourced Mobile Sensing-Based Bridge Modal Identification |
| title_sort | roadmap for ubiquitous crowdsourced mobile sensing based bridge modal identification |
| topic | mobile sensing crowdsourcing system identification bridge monitoring ride-share smartphone |
| url | https://www.mdpi.com/1424-8220/25/8/2528 |
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