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. 2015 Jun 19;15(6):14591-614.
doi: 10.3390/s150614591.

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

Ekin Ozer  1 , Maria Q Feng  2 , Dongming Feng  3
Affiliations

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

Ekin Ozer et al. Sensors (Basel). .

Abstract

This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Previously, the authors have investigated the reliability of smartphone accelerometers for vibration-based SHM. This paper takes a step further to integrate mobile sensing and web-based computing for a prospective crowdsourcing-based SHM platform. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. A web-based platform was developed to collect and process the data automatically and store the processed data, such as modal properties of the structure, for long-term SHM purposes. Finally, the integrated mobile and web-based platforms were tested to collect the low-amplitude ambient vibration data of a bridge structure. Possible sources of uncertainties related to citizens were investigated, including the phone location, coupling conditions, and sampling duration. The field test results showed that the vibration data acquired by smartphones operated by citizens without expertise are useful for identifying structural modal properties with high accuracy. This platform can be further developed into an automated, smart, sustainable, cost-free system for long-term monitoring of structural integrity of spatially distributed urban infrastructure. Citizen Sensors for SHM will be a novel participatory sensing platform in the way that it offers hybrid solutions to transitional crowdsourcing parameters.

Keywords: Citizen Sensors; ambient vibration; crowdsourcing; mobile networks; modal identification; smartphone sensors; structural health monitoring.

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Figures

Figure 1
Figure 1
Integration scheme of system platforms.
Figure 2
Figure 2
User login, recording, and submission screenshots, respectively.
Figure 3
Figure 3
Digital signal processing operations applied on the server-side.
Figure 4
Figure 4
Screenshot from the web interface showing the SHM results page.
Figure 5
Figure 5
Inner and outer views, dimensions, and sensor layout of the pedestrian link bridge.
Figure 6
Figure 6
1st, 2nd, and 3rd modal frequencies (8.46, 18.95, 29.67 Hz) and mode shapes from FDD by reference accelerometers.
Figure 7
Figure 7
Acceleration time histories and Fourier spectra samples from Test 3 and Test 6.
Figure 8
Figure 8
Fourier spectra from the average of 40 samples for Test 1–6.
Figure 9
Figure 9
Identified frequencies obtained from different samples.
Figure 10
Figure 10
Arias intensities obtained from different samples.
Figure 11
Figure 11
Modal identification results from Test 1–6 and crowdsourcing.
Figure 12
Figure 12
Acceleration time histories and Fourier spectra samples from pedestrian-induced vibrations.
See this image and copyright information in PMC

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