Open Journal Systems

Bridge monitoring utilizing handheld devices

Ahmed Elhattab 1, Nasim Uddin 2

Article ID: 471
Vol 2, Issue 1, 2018, Article identifier:

VIEWS - 268 (Abstract) 120 (PDF)


Natural Frequencies of structures is an elegant intrinsic property that is essential for many Civil Structural applications, as Structural Health Monitoring and Simulation Modeling. The physically tangible relation between the frequency of the structures and its dynamic characteristics was the impetus for using different time/frequency based methods to quantify this fundamental property. Unfortunately, the disruption effect of noise requires incorporating advanced sensors, that provide signals with a low noise-intensity, to accurately identify the fundamental frequencies of the structure. This article solves this bottleneck via exploiting the Stochastic Resonance (SR) phenomena to extract the fundamental frequencies of a bridge using an acceleration recorded by a conventional portable sensor as the sensor implemented in small portable accelerometer. The portable accelerometer device has an M9 motion coprocessor designed mainly for tracking human activities. Human activities have an exaggerated amplitude when it is compared to the structural responses. Therefore, if an iPhone device is used to record the response of the structure (for example a bridge) the structure response will be swamped by severe surrounding noise because of its small amplitude. Therefore, in this vein, the SR phenomena has been employed to use rather than suppress the noise to magnify the feeble bridge response in the recorded acceleration and hence identify the corresponding frequency. The fidelity of the proposed approach has been verified using the data of a field experiment. The bridge frequencies are identified first using conventional vibration analysis, thereafter, the portable accelerometer has been attached to the bridge rail to record the bridge vibration under the passing traffic. The recorded data has been processed using a new Developed Underdamped Pinning Stochastic Resonance (DUPSR) technique to quantify the bridge frequency.


stochastic resonance; bridge health monitoring; identifying bridge frequency; extraction of feeble bridge response

Full Text:



S.L. Davis, D. Goldberg, K. DeGood, N. Donohue, J. Corless, The fix we’re in for: The state of our nation’s bridges 2013, (2013).

A. Malekjafarian, P.J. McGetrick, E.J. OBrien, A review of indirect bridge monitoring using passing vehicles, Shock and Vibration, (2015).

E.P. Carden, P. Fanning, Vibration based condition monitoring: a review, Structural health monitoring, 3 (2004) 355-377.

A. Rytter, Vibrational based inspection of civil engineering structures, Dept. of Building Technology and Structural Engineering, Aalborg University, 1993.

W. Fan, P. Qiao, Vibration-based damage identification methods: a review and comparative study, Structural Health Monitoring, 10 (2011) 83-111.

R. Adams, P. Cawley, C. Pye, B. Stone, A vibration technique for non-destructively assessing the integrity of structures, Journal of Mechanical Engineering Science, 20 (1978) 93-100.

P. Cawley, R. Adams, The location of defects in structures from measurements of natural frequencies, The Journal of Strain Analysis for Engineering Design, 14 (1979) 49-57.

O. Salawu, Detection of structural damage through changes in frequency: a review, Engineering structures, 19 (1997) 718-723.

W. Heylen, P. Sas, Modal analysis theory and testing, Katholieke Universteit Leuven, Departement Werktuigkunde, 2006.

B.J. Schwarz, M.H. Richardson, Experimental modal analysis, CSI Reliability week, 35 (1999) 1-12.

H. Banks, D. Inman, D. Leo, Y. Wang, An experimentally validated damage detection theory in smart structures, Journal of Sound and Vibration, 191 (1996) 859-880.

H. Chen, C. Spyrakos, G. Venkatesh, Evaluating structural deterioration by dynamic response, Journal of Structural Engineering, 121 (1995) 1197-1204.

G. De Roeck, B. Peeters, J. Maeck, Dynamic monitoring of civil engineering structures, Computational Methods for Shell and Spatial Structures, (2000).

S.S. Kessler, S.M. Spearing, M.J. Atalla, C.E. Cesnik, C. Soutis, Damage detection in composite materials using frequency response methods, Composites Part B: Engineering, 33 (2002) 87-95.

S. Law, H. Ward, G. Shi, R. Chen, P. Waldron, C. Taylor, Dynamic assessment of bridge load-carrying capacities. I, Journal of Structural Engineering, 121 (1995) 478-487.

S. Law, H. Ward, G. Shi, R. Chen, P. Waldron, C. Taylor, Dynamic assessment of bridge load-carrying capacities. II, Journal of Structural Engineering, 121 (1995) 488-495.

J. Keenahan, P. McGetrick, E.J. O'Brien, A. Gonzalez, Using instrumented vehicles to detect damage in bridges, 15th International Conference on Experimental Mechanics, Porto, Portugal, 22-27 July 2012, Paper No. 2934, Faculty of Engineering, University of Porto, 2012.

E.J. O'Brien, J. Keenahan, P. McGetrick, A. González, Using Instrumented Vehicles to Detect Damage in Bridges, BCRI 12-Bridge and Concrete Research in Ireland, Dublin, 6-7 September, 2012, 2012.

J. Keenahan, E.J. OBrien, P.J. McGetrick, A. González, The use of a dynamic truck-trailer drive-by system to monitor bridge damping, Structural Health Monitoring, (2013) 1475921713513974.

E. Winardi, A. Elhattab, N. Uddin, Bridge Curvature for Detecting Bridge Damage Location, 26th ASNT Research Symposium, 2017, pp. 275-283.

C. Tan, A. Elhattab, N. Uddin, Wavelet Based Damage Assessment and Localization for Bridge Structures, 26th ASNT Research Symposium, 2017, pp. 228-240.

C. Tan, A. Elhattab, N. Uddin, “Drive-by’’bridge frequency-based monitoring utilizing wavelet transform, Journal of Civil Structural Health Monitoring, 7 (2017) 615-625.

A.U. Elhattab, Nasim, The Implication of Analysis Module on Vehicle Bridge Interaction Modelling, Civil Engineering Research Journal, 2 (2017) 4.

A. Elhattab, N. Uddin, E. O'Brien, Y. Wang, Field Verification for Drive-by Bridge Monitoring using Non-specialized Inspection Vehicle, 26th ASNT Research Symposium, 2017, pp. 66-74.

A. Elhattab, N. Uddin, E. O'Brien, Drive-by Bridge Damage Detection Using Non-specialized Vehicle, 26th ASNT Research Symposium, 2017, pp. 43-54.

A. Elhattab, N. Uddin, E. O'Brien, Drive-by Bridge Inspection Using Inverse Dynamics Optimization Algorithm, 26th ASNT Research Symposium, 2017, pp. 55-65.

A. Elhattab, N. Uddin, E. OBrien, Identifying Localized Bridge Damage Using Frequency Domain Decomposition, 26th ASNT Research Symposium, 2017, pp. 75-83.

A. ElHattab, N. Uddin, E. OBrien, Drive-by bridge damage detection using non-specialized instrumented vehicle, Bridge Structures, 12 (2017) 73-84.

A. Elhattab, N. Uddin, Drive-by Bridge Damage Monitoring: Concise Review, Civil Engineering Research Journal, 1 (2017) 6.

A. Elhattab, N. Uddin, E. OBrien, Drive-by bridge damage monitoring using Bridge Displacement Profile Difference, Journal of Civil Structural Health Monitoring, 6 (2016) 839-850.

A.A. Elhattab, Drive-by bridge damage inspection, The University of Alabama at Birmingham, 2015.

Y. Mohammed, N. Uddin, Bridge Damage Detection using the Inverse Dynamics Optimization Algorithm, ASNT 26th Research Symposium Proceeding, (2017).

Z. Hou, M. Noori, R.S. Amand, Wavelet-based approach for structural damage detection, Journal of Engineering mechanics, 126 (2000) 677-683.

K. Liew, Q. Wang, Application of wavelet theory for crack identification in structures, Journal of engineering mechanics, 124 (1998) 152-157.

C.-J. Lu, Y.-T. Hsu, Vibration analysis of an inhomogeneous string for damage detection by wavelet transform, International Journal of Mechanical Sciences, 44 (2002) 745-754.

P. McGetrick, C. Kim, A wavelet based drive-by bridge inspection system, Proceedings of the 7th International Conference on Bridge Maintenance Safety and Management (IABMAS’14), 2014.

P.J. McGetrick, C.W. Kim, A parametric study of a drive by bridge inspection system based on the Morlet wavelet, Key Engineering Materials, Trans Tech Publ, 2013, pp. 262-269.

R. Brincker, L. Zhang, P. Andersen, Modal identification from ambient responses using frequency domain decomposition, Proc. of the 18*‘International Modal Analysis Conference (IMAC), San Antonio, Texas, 2000.

G. Lederman, Z. Wang, J. Bielak, H. Noh, J. Garrett, S. Chen, J. Kovacevic, F. Cerda, P. Rizzo, Damage quantification and localization algorithms for indirect SHM of bridges, Proc. Int. Conf. Bridge Maint., Safety Manag., Shanghai, China, 2014.

D. Rezaei, F. Taheri, Experimental validation of a novel structural damage detection method based on empirical mode decomposition, Smart Materials and Structures, 18 (2009) 045004.

A. Sadhu, B. Hazra, A novel damage detection algorithm using time-series analysis-based blind source separation, Shock and Vibration, 20 (2013) 423-438.

J.N. Yang, Y. Lei, S. Lin, N. Huang, Hilbert-Huang based approach for structural damage detection, Journal of engineering mechanics, 130 (2004) 85-95.

R. Benzi, A. Sutera, A. Vulpiani, The mechanism of stochastic resonance, Journal of Physics A: mathematical and general, 14 (1981) L453.

L. Gammaitoni, P. Hänggi, P. Jung, F. Marchesoni, Stochastic resonance, Reviews of modern physics, 70 (1998) 223.

H. Zhang, Q. He, F. Kong, Stochastic resonance in an underdamped system with pinning potential for weak signal detection, Sensors, 15 (2015) 21169-21195.

X. Dong, D. Zhu, Y. Wang, J.P. Lynch, R.A. Swartz, Design and validation of acceleration measurement using the Martlet wireless sensing system, ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, American Society of Mechanical Engineers, 2014, pp. V001T005A006-V001T005A006.

Y. Wang, N. Uddin, L.J. Jacobs, J.-Y. Kim, Field Validation of a Drive-By Bridge Inspection System with Wireless BWIM+ NDE Devices, 2016.

Y. Yang, M. Cheng, K. Chang, Frequency variation in vehicle–bridge interaction systems, International Journal of Structural Stability and Dynamics, 13 (2013) 1350019.

(268 Abstract Views, 120 PDF Downloads)


  • There are currently no refbacks.

Copyright (c) 2018 Wireless Communication Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.