Abstract
This paper presents an application of a novel data collection method: mobile sensing. Mobile sensor networks can provide extensive information similar to dense fixed sensor networks while conserving the ease of smaller networks. However, mobile sensing data is expected to have missing observations in time and space, leaving data matrices incompatible with common identification techniques. STRIDE is an algorithm implemented for modal identification using this class of sensor data, which includes missing observations. Although mobile sensing devices are not widely available and large-scale mobile sensors networks have yet to be implemented, pseudo mobile sensing data is extracted from a dense sensor network using a simulated mobile sensor network. In this paper, ambient vibrations of Golden Gate Bridge are considered and pseudo (simulated) mobile sensing data are populated from a subset that shares the paths of simulated mobile sensors. The paper provides promising results to encourage the implementation of large-scale mobile sensor networks in future SHM endeavors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pakzad SN, Fenves GL, Kim S, Culler DE (2008) Design and implementation of scalable wireless sensor network for structural monitoring. J Infrastruct Syst 14:89–101
Pakzad SN, Fenves GL (2009) Statistical analysis of vibration modes of a suspension bridge using spatially dense wireless sensor network. J Struct Eng 135(7):863–872
Guo HY, Zhang L, Zhang LL, Zhou JX (2004) Optimal placement of sensors for structural health monitoring using improved genetic algorithms. Smart Mater Struct 13:528–534. doi:10.1088/0964-1726/13/3/011
Saitta S, Kripakaran P, Raphael B, Smith IF (2008) Improving system identification using clustering. J Comput Civ Eng 22:292–302. doi:10.1061/(ASCE)0887-3801(2008)22:5(292)
Matarazzo TJ, Pakzad SN (2013) Mobile sensors in bridge health monitoring. In: Proceedings of the ninth international workshop on structural health monitoring (IWSHM), Stanford, p 8
Bagajewicz M, Sa M (2000) Cost-optimal design of reliable sensor networks. J Comput Chem Eng 23:1757–1762
Zhu D, Yi X, Wang Y et al (2010) A mobile sensing system for structural health monitoring: design and validation. Smart Mater Struct 19:055011. doi:10.1088/0964-1726/19/5/055011
Sibley GT, Rahimi MH, Sukhatme GS (2002) Robomote: a tiny mobile robot platform for large-scale ad-hoc sensor networks. In: Proceedings of the 2002 IEEE international conference on robotics and automation (Cat No. 02CH37292), vol 2, pp 1143–1148. doi:10.1109/ROBOT.2002.1014697
Dantu K, Rahimi M, Shah H, Babel S, Dhariwal A, Sukhatme GS (2005) Robomote: enabling mobility in sensor networks. In: Proceedings of the 4th international symposium on information processing in sensor networks, IEEE Press, April 2005, p 55
Lin CW, Yang YB (2005) Use of a passing vehicle to scan the fundamental bridge frequencies: an experimental verification. Eng Struct 27:1865–1878. doi:10.1016/j.engstruct.2005.06.016
Cerda F, Garrett J, Bielak J et al (2012) Indirect structural health monitoring in bridges: scale experiments. In: Proceedings of the seventh international conference on bridge maintenance, safety and management, Lago Di Como, pp 346–353
Unnikrishnan J, Vetterli M (2012) Sampling and reconstructing spatial fields using mobile sensors. In: 2012 IEEE international conference on acoustics, speech and signal processing, IEEE, pp 3789–3792
Singhvi V, Krause A, Guestrin C, Garrett Jr, JH, Matthews HS (2005) Intelligent light control using sensor networks. In: Proceedings of the 3rd international conference on embedded networked sensor systems, November 2005, ACM, pp 218–229
Cai Y, Chen SS, Rote DM, Coffey HT (1994) Vehicle/guideway interaction for high speed vehicles on a flexible guideway. J Sound Vib 175(5):625–646
Deng L, Cai CS (2009) Identification of parameters of vehicles moving on bridges. Eng Struct 31:2474–2485. doi:10.1016/j.engstruct.2009.06.005
Cantieni R (1992) Dynamic behavior of highway bridges, 240 pp
Rubin DB, Dempster AP, Laird NM (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39:1–38
Shumway RH, Stoffer DS (1982) An approach to time series smoothing and forecasting using the EM algorithm. J Time Ser Anal 3:253–264
Digalakis V, Rohlicek JR, Ostendorf M (1993) ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition. IEEE Trans Speech Audio Process 1:431–442. doi:10.1109/89.242489
Dorvash S, Pakzad SN (2012) Stochastic iterative modal identification algorithm and application in wireless sensor networks. Struct Contr Health Monit. doi:10.1002/stc
Jones RH (1980) Maximum likelihood fitting of time ARMA models to time series with missing observations. Technometrics 22:389–395
Shumway RH, Stoffer DS (2010) Time series analysis and its applications: with R examples. Springer
Matarazzo TJ, Pakzad SN (2013) Structural modal identification using data sets with missing observations. In: Lynch JP, Yun C-B, Wang K-W (eds) Proceedings of the SPIE sensors smart structures technologies, San Diego, p 14
Harvey AC, Pierse RG (1984) Estimating missing observations in economic time series. J Am Stat Assoc 79:125–131
Chang M, Pakzad SN (2013) Observer Kalman filter Identification for output-only systems using interactive structural modal identification toolsuite (SMIT). J Bridge Eng 130703221853009. doi:10.1061/(ASCE)BE.1943-5592.0000530
Law SS, Zhu XQ (2005) Bridge dynamic responses due to road surface roughness and braking of vehicle. J Sound Vib 282:805–830. doi:10.1016/j.jsv.2004.03.032
González A, Covián E, Madera J (2008) Determination of bridge natural frequencies using a moving vehicle instrumented with accelerometers and a geographical positioning system. In: Proceedings of the ninth international conference on computer structures technology, p 16
Acknowledgment
This research was partially supported by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Matarazzo, T.J., Pakzad, S.N. (2014). Modal Identification of Golden Gate Bridge Using Pseudo Mobile Sensing Data with STRIDE. In: Catbas, F. (eds) Dynamics of Civil Structures, Volume 4. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-04546-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-04546-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04545-0
Online ISBN: 978-3-319-04546-7
eBook Packages: EngineeringEngineering (R0)