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Smart city article details
| Title | A Crowdsensing-Based Platform For Transportation Infrastructure Monitoring And Management In Smart Cities |
|---|---|
| ID_Doc | 1190 |
| Authors | Shirzad-Ghaleroudkhani N.; Mei Q.; Gül M. |
| Year | 2022 |
| Published | The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems |
| DOI | http://dx.doi.org/10.1016/B978-0-12-817784-6.00005-9 |
| Abstract | Crowdsensing-based infrastructure monitoring is an up-coming framework and has the potential to become a high-level monitoring tool and help manage populations of infrastructure systems with reduced cost and increased efficiency in future smart cities. Using this framework, the existing infrastructure system can be prescreened on a large scale to determine the key infrastructure elements that exhibit abnormal behavior before implementing more detailed on-site monitoring systems or conducting detailed site inspections on individual infrastructure elements. In this framework, a large amount of crowdsensing data, i.e., “big data,” is collected from commercial-grade sensors in mobile personal devices, e.g., smartphones, smart vehicles, and cameras. In order for such crowdsensing-based frameworks to succeed, the crowdsensing data must be efficiently and effectively managed and analyzed to support decision-making. In this context, this chapter presents a software platform to analyze and manage the crowdsensing data collected from moving vehicles. The platform includes a database to store the crowdsensing data, algorithms that are integrated for data analysis, and a web-based interactive system for visualization. Three algorithms developed by our group that can process vibration and image data collected from moving vehicles for bridge and road condition assessment are presented in this chapter, which are Mel-frequency cepstral analysis-based methods for bridge damage detection, inverse filter-based method bridge frequency identification, and deep learning-based method for road crack detection. In the long term, we envision that this platform will be open-sourced, and more algorithms that can process other crowdsensing data can be integrated in this platform in the future. © 2022 Elsevier Inc. All rights reserved. |
| Author Keywords | Bridge monitoring; Crack detection; Crowdsensing-based platform; Smart cities; Transportation infrastructure |
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