Mobile news

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data


  • American Society of Civil Engineers. Failure to Act: Closing the Infrastructure Investment Gap for America’s Economic Future (American Society of Civil Engineers, 2016).

  • Koks, E. E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat. Commun. 10, 1–11 (2019).

    Article 

    Google Scholar
     

  • Van Aalst, M. K. The impacts of climate change on the risk of natural disasters. Disasters 30, 5–18 (2006).

    Article 

    Google Scholar
     

  • Mitchell, J. F., Lowe, J., Wood, R. A. & Vellinga, M. Extreme events due to human-induced climate change. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 364, 2117–2133 (2006).

    Article 

    Google Scholar
     

  • Banholzer, S., Kossin, J. & Donner, S. in Reducing Disaster: Early Warning Systems for Climate Change, 21–49 (Springer, 2014).

  • Cook, W., Barr, P. J. & Halling, M. W. Bridge failure rate. J. Perform. Constr. Facil. 29, 04014080 (2015).

    Article 

    Google Scholar
     

  • Van Leeuwen, Z. & Lamb, R. Flood and Scour Related Failure Incidents at Railway Assets Between 1846 and 2013 (Railway Safety & Standards Board, 2014).

  • Garg, R. K., Chandra, S. & Kumar, A. Analysis of bridge failures in India from 1977 to 2017. Struct. Infrastruct. Eng. 18, 295–312 (2022).

    Article 

    Google Scholar
     

  • Jeong, Y., Kim, W., Lee, I. & Lee, J. Bridge inspection practices and bridge management programs in China, Japan, Korea, and US. J. Struct. Integr. Maint. 3, 126–135 (2018).


    Google Scholar
     

  • Lynch, J. P. An overview of wireless structural health monitoring for civil structures. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 365, 345–372 (2007).

    Article 

    Google Scholar
     

  • Farrar, C. R. & Worden, K. Structural Health Monitoring: A Machine Learning Perspective (John Wiley & Sons, 2012).

  • Sanayei, M., Khaloo, A., Gul, M. & Catbas, F. N. Automated finite element model updating of a scale bridge model using measured static and modal test data. Eng. Struct. 102, 66–79 (2015).

    Article 

    Google Scholar
     

  • Khaloo, A., Lattanzi, D., Cunningham, K., Dell’Andrea, R. & Riley, M. Unmanned aerial vehicle inspection of the placer river trail bridge through image-based 3d modelling. Struct. Infrastruct. Eng. 14, 124–136 (2018).

    Article 

    Google Scholar
     

  • Momtaz Dargahi, M., Khaloo, A. & Lattanzi, D. Color-space analytics for damage detection in 3d point clouds. Struct. Infrastruct. Eng. 18, 775–788 (2022).

    Article 

    Google Scholar
     

  • Lin, C. & Yang, Y. Use of a passing vehicle to scan the fundamental bridge frequencies: an experimental verification. Eng. Struct. 27, 1865–1878 (2005).

    Article 

    Google Scholar
     

  • Feng, M., Fukuda, Y., Mizuta, M. & Ozer, E. Citizen sensors for SHM: use of accelerometer data from smartphones. Sensors 15, 2980–2998 (2015).

    Article 

    Google Scholar
     

  • Yang, Y.-B., Lin, C. & Yau, J. Extracting bridge frequencies from the dynamic response of a passing vehicle. J. Sound Vib. 272, 471–493 (2004).

    Article 

    Google Scholar
     

  • Yang, Y. & Chang, K. Extracting the bridge frequencies indirectly from a passing vehicle: parametric study. Eng. Struct. 31, 2448–2459 (2009).

    Article 

    Google Scholar
     

  • Siringoringo, D. M. & Fujino, Y. Estimating bridge fundamental frequency from vibration response of instrumented passing vehicle: analytical and experimental study. Adv. Struct. Eng. 15, 417–433 (2012).

    Article 

    Google Scholar
     

  • Zhang, Y., Wang, L. & Xiang, Z. Damage detection by mode shape squares extracted from a passing vehicle. J. Sound Vib. 331, 291–307 (2012).

    Article 

    Google Scholar
     

  • Yang, Y.-B., Yang, J. P., Zhang, B. & Wu, Y. Vehicle Scanning Method for Bridges (Wiley Online Library, 2020).

  • Sitton, J. D., Rajan, D. & Story, B. A. Bridge frequency estimation strategies using smartphones. J. Civ. Struct. Health Monit. 10, 513–526 (2020).

    Article 

    Google Scholar
     

  • Sitton, J. D., Zeinali, Y., Rajan, D. & Story, B. A. Frequency estimation on two-span continuous bridges using dynamic responses of passing vehicles. J. Eng. Mech. 146, 04019115 (2020).

    Article 

    Google Scholar
     

  • Marulanda, J., Caicedo, J. M. & Thomson, P. Modal identification using mobile sensors under ambient excitation. J. Comput. Civ. Eng. 31, 04016051 (2016).

    Article 

    Google Scholar
     

  • Matarazzo, T. J. & Pakzad, S. N. Structural identification for mobile sensing with missing observations. J. Eng. Mech. 142, 04016021 (2016).

    Article 

    Google Scholar
     

  • Matarazzo, T. J. & Pakzad, S. N. Truncated physical model for dynamic sensor networks with applications in high-resolution mobile sensing and bigdata. J. Eng. Mech. 142, 04016019 (2016).

    Article 

    Google Scholar
     

  • Matarazzo, T. J. & Pakzad, S. N. Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput.-Aided Civ. Infrastruct. Eng. 33, 4–20 (2018).

    Article 

    Google Scholar
     

  • Malekjafarian, A. & OBrien, E. J. Identification of bridge mode shapes using short time frequency domain decomposition of the responses measured in a passing vehicle. Eng. Struct. 81, 386–397 (2014).

    Article 

    Google Scholar
     

  • Ozer, E., Feng, M. Q. & Feng, D. Citizen sensors for SHM: towards a crowdsourcing platform. Sensors 15, 14591–14614 (2015).

    Article 

    Google Scholar
     

  • Ozer, E., Purasinghe, R. & Feng, M. Q. Multi-output modal identification of landmark suspension bridges with distributed smartphone data: Golden gate bridge. Struct. Control Health Monit. 27, e2576 (2020).

    Article 

    Google Scholar
     

  • Figueiredo, E., Moldovan, I., Alves, P., Rebelo, H. & Souza, L. Smartphone application for structural health monitoring of bridges. Sensors 22, 8483 (2022).

    Article 

    Google Scholar
     

  • McGetrick, P., Hester, D. & Taylor, S. Implementation of a drive-by monitoring system for transport infrastructure utilising smartphone technology and GNSS. J. Civ. Struct. Health Monit. 7, 175–189 (2017).

    Article 

    Google Scholar
     

  • Matarazzo, T. J. et al. Crowdsensing framework for monitoring bridge vibrations using moving smartphones. Proc. IEEE 106, 577–593 (2018).

    Article 

    Google Scholar
     

  • Eshkevari, S. S., Cronin, L., Matarazzo, T. J. & Pakzad, S. N. Bridge modal property identification based on asynchronous mobile sensing data. Struct. Health Monit. 22, 2022–2037 (2023).

    Article 

    Google Scholar
     

  • Quqa, S., Giordano, P. F. & Limongelli, M. P. Shared micromobility-driven modal identification of urban bridges. Autom. Constr. 134, 104048 (2022).

    Article 

    Google Scholar
     

  • Matarazzo, T. J. et al. Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips. Commun. Eng. 1, 29 (2022).

    Article 

    Google Scholar
     

  • OKeeffe, K. P., Anjomshoaa, A., Strogatz, S. H., Santi, P. & Ratti, C. Quantifying the sensing power of vehicle fleets. Proc. Natl Acad. Sci. USA 116, 12752–12757 (2019).

    Article 

    Google Scholar
     

  • Askegaard, V. & Mossing, P. Long Term Observation of Rc-bridge Using Changes in Natural Frequency. Nordic Concrete Research. Publication No 7 (Nordic Concrete Federation, 1988).

  • Peeters, B. & De Roeck, G. One-year monitoring of the Z24-bridge: environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30, 149–171 (2001).

    Article 

    Google Scholar
     

  • Peeters, B., Maeck, J. & De Roeck, G. Vibration-based damage detection in civil engineering: excitation sources and temperature effects. Smart Mater. Struct. 10, 518 (2001).

    Article 

    Google Scholar
     

  • Liang, Y., Li, D., Song, G. & Feng, Q. Frequency co-integration-based damage detection for bridges under the influence of environmental temperature variation. Measurement 125, 163–175 (2018).

    Article 

    Google Scholar
     

  • Ralbovsky, M., Deix, S. & Flesch, R. Frequency changes in frequency-based damage identification. Struct. Infrastruct. Eng. 6, 611–619 (2010).

    Article 

    Google Scholar
     

  • Kim, J.-T., Park, J.-H. & Lee, B.-J. Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions. Eng. Struct. 29, 1354–1365 (2007).

    Article 

    Google Scholar
     

  • Jin, C., Jang, S., Sun, X., Li, J. & Christenson, R. Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network. J. Civ. Struct. Health Monit. 6, 545–560 (2016).

    Article 

    Google Scholar
     

  • Fan, W. & Qiao, P. Vibration-based damage identification methods: a review and comparative study. Struct. Health Monit. 10, 83–111 (2011).

    Article 

    Google Scholar
     

  • Farrar, C. & James Iii, G. System identification from ambient vibration measurements on a bridge. J. Sound Vib. 205, 1–18 (1997).

    Article 

    Google Scholar
     

  • Shi, Z., Law, S. & Zhang, L. Damage localization by directly using incomplete mode shapes. J. Eng. Mech. 126, 656–660 (2000).

    Article 

    Google Scholar
     

  • Lee, J. J., Lee, J. W., Yi, J. H., Yun, C. B. & Jung, H. Y. Neural networks-based damage detection for bridges considering errors in baseline finite element models. J. Sound Vib. 280, 555–578 (2005).

    Article 

    Google Scholar
     

  • Hu, C. & Afzal, M. T. A statistical algorithm for comparing mode shapes of vibration testing before and after damage in timbers. J. Wood Sci. 52, 348–352 (2006).

    Article 

    Google Scholar
     

  • OBrien, E. J. & Malekjafarian, A. A mode shape-based damage detection approach using laser measurement from a vehicle crossing a simply supported bridge. Struct. Control Health Monit. 23, 1273–1286 (2016).

    Article 

    Google Scholar
     

  • Liew, K. M. & Wang, Q. Application of wavelet theory for crack identification in structures. J. Eng. Mech. 124, 152–157 (1998).

    Article 

    Google Scholar
     

  • Hong, J.-C., Kim, Y., Lee, H. & Lee, Y. Damage detection using the Lipschitz exponent estimated by the wavelet transform: applications to vibration modes of a beam. Int. J. Solids Struct. 39, 1803–1816 (2002).

    Article 

    Google Scholar
     

  • Douka, E., Loutridis, S. & Trochidis, A. Crack identification in plates using wavelet analysis. J. Sound Vib. 270, 279–295 (2004).

    Article 

    Google Scholar
     

  • Chang, C.-C. & Chen, L.-W. Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach. Mech. Syst. Signal Process. 19, 139–155 (2005).

    Article 

    Google Scholar
     

  • Poudel, U. P., Fu, G. & Ye, J. Wavelet transformation of mode shape difference function for structural damage location identification. Earthq. Eng. Struct. Dyn. 36, 1089–1107 (2007).

    Article 

    Google Scholar
     

  • Tan, C., Elhattab, A. & Uddin, N. Wavelet based damage assessment and localization for bridge structures. In 26th ASNT Research Symposium, 228–240 (2017).

  • Pandey, A., Biswas, M. & Samman, M. Damage detection from changes in curvature mode shapes. J. Sound Vib. 145, 321–332 (1991).

    Article 

    Google Scholar
     

  • Wahab, M. A. & De Roeck, G. Damage detection in bridges using modal curvatures: application to a real damage scenario. J. Sound Vib. 226, 217–235 (1999).

    Article 

    Google Scholar
     

  • Kim, B. H., Park, T. & Voyiadjis, G. Z. Damage estimation on beam-like structures using the multi-resolution analysis. Int. J. Solids Struct. 43, 4238–4257 (2006).

    Article 

    Google Scholar
     

  • Feng, D. & Feng, M. Q. Output-only damage detection using vehicle-induced displacement response and mode shape curvature index. Struct. Control Health Monit. 23, 1088–1107 (2016).

    Article 

    Google Scholar
     

  • Shokrani, Y., Dertimanis, V. K., Chatzi, E. N. & N. Savoia, M. On the use of mode shape curvatures for damage localization under varying environmental conditions. Struct. Control Health Monit. 25, e2132 (2018).

    Article 

    Google Scholar
     

  • Allemang, R. J. A correlation coefficient for modal vector analysis. In Proc. 1st Int. Modal Analysis Conference, 110–116 (1982).

  • Pakzad, S. N. & Fenves, G. L. Statistical analysis of vibration modes of a suspension bridge using spatially dense wireless sensor network. J. Struct. Eng. 135, 863–872 (2009).

    Article 

    Google Scholar
     

  • Abdel-Ghaffar, A. M. & Scanlan, R. H. Ambient vibration studies of Golden Gate Bridge: I. Suspended structure. J. Eng. Mech. 111, 463–482 (1985).

    Article 

    Google Scholar
     

  • Çelebi, M. Golden Gate Bridge response: a study with low-amplitude data from three earthquakes. Earthq. Spectra 28, 487–510 (2012).

    Article 

    Google Scholar
     

  • Malekjafarian, A. & OBrien, E. J. On the use of a passing vehicle for the estimation of bridge mode shapes. J. Sound Vib. 397, 77–91 (2017).

    Article 

    Google Scholar
     

  • Riasat Azim, M. & Gül, M. Damage detection of steel-truss railway bridges using operational vibration data. J. Struct. Eng. 146, 04020008 (2020).

    Article 

    Google Scholar
     

  • Gao, Y. & Spencer, B. Damage localization under ambient vibration using changes in flexibility. Earthq. Eng. Eng. Vib. 1, 136–144 (2002).

    Article 

    Google Scholar
     

  • Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598 (2012).

    Article 
    MathSciNet 

    Google Scholar
     

  • Chen, W.-F. & Duan, L. Handbook of International Bridge Engineering (CRC Press, 2013).

  • Eshkevari, S. S., Matarazzo, T. J. & Pakzad, S. N. Bridge modal identification using acceleration measurements within moving vehicles. Mech. Syst. Signal Process. 141, 106733 (2020).

    Article 

    Google Scholar
     

  • Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).

    Article 
    MathSciNet 

    Google Scholar
     

  • Marx, V. The big challenges of big data. Nature 498, 255–260 (2013).

    Article 

    Google Scholar
     

  • He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).

  • Brunton, S. L., Proctor, J. L. & Kutz, J. N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113, 3932–3937 (2016).

    Article 
    MathSciNet 

    Google Scholar
     

  • Spencer Jr, B. F., Hoskere, V. & Narazaki, Y. Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering 5, 199–222 (2019).

    Article 

    Google Scholar
     

  • Khaloo, A., Lattanzi, D., Jachimowicz, A. & Devaney, C. Utilizing UAV and 3D computer vision for visual inspection of a large gravity dam. Front. Built Environ. 4, 31 (2018).

    Article 

    Google Scholar
     

  • Rafiei, M. H. & Adeli, H. A novel unsupervised deep learning model for global and local health condition assessment of structures. Eng. Struct. 156, 598–607 (2018).

    Article 

    Google Scholar
     

  • Smith, I. F. Studies of sensor data interpretation for asset management of the built environment. Front. Built Environ. 2, 8 (2016).

    Article 

    Google Scholar
     

  • Malekjafarian, A., Golpayegani, F., Moloney, C. & Clarke, S. A machine learning approach to bridge-damage detection using responses measured on a passing vehicle. Sensors 19, 4035 (2019).

    Article 

    Google Scholar
     

  • Liu, J. et al. Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3007–3011 (IEEE, 2020).

  • Feng, B. T., Ogren, A. C., Daraio, C. & Bouman, K. L. Visual vibration tomography: Estimating interior material properties from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16231–16240 (2022).

  • Guo, K. & Buehler, M. J. A semi-supervised approach to architected materials design using graph neural networks. Extrem. Mech. Lett. 41, 101029 (2020).

    Article 

    Google Scholar
     

  • Nadkarni, N., Daraio, C. & Kochmann, D. M. Dynamics of periodic mechanical structures containing bistable elastic elements: from elastic to solitary wave propagation. Phys. Rev. E 90, 023204 (2014).

    Article 

    Google Scholar
     

  • Chang, K.-C. & Kim, C.-W. Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge. Eng. Struct. 122, 156–173 (2016).

    Article 

    Google Scholar
     

  • Sadeghi Eshkevari, S., Matarazzo, T. J. & Pakzad, S. N. Simplified vehicle–bridge interaction for medium to long-span bridges subject to random traffic load. J. Civ. Struct. Health Monit. 10, 693–707 (2020).

    Article 

    Google Scholar
     

  • Cronin, L. et al. Commodifying Infrastructure Spatial Dynamics with Crowdsourced Smartphone Data (2024).

  • Dickey, D. A. & Fuller, W. A. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431 (1979).

    MathSciNet 

    Google Scholar
     



  • READ SOURCE

    This website uses cookies. By continuing to use this site, you accept our use of cookies.