Structural Health Monitor of the US Grant Bridge (Data Patterns)

There are several algorithms for measurement-data interpretation and structural evaluation that have been proposed and implemented by various researchers over the past years. Although there is no current reliable standardized method that can be applied to bridges for detection of abnormal behaviors, consideration of structural health monitoring (SHM) at the design stage seems to be a necessity for achieving an efficient and reliable monitoring of structures in-service. In this paper, result of the long term SHM of the US Grant Bridge that was initiated at the design stages of the bridge is presented. First, the components of the monitoring system are overviewed. Second, long term data patterns of the collected strain data are combined with temperature data to document the “normal” or expected behavior of the structure. Finally, the result of the calibrated data model and its performance during a major maintenance event is detailed.

[1] M. Gul and F. Necati Catbas, “Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications,” Mech. Syst. Signal Process., vol. 23, no. 7, pp. 2192–2204, Oct. 2009. [2] S. N. Pakzad and R. Yao, “Autoregressive statistical pattern recognition algorithms for damage detection in civil structures,” Mechanical Systems and Signal Processing, vol. 31. pp. 355–368, 2012. 11-06-10 19-09-10 28-12-10 07-04-11 16-07-11 24-10-11 01-02-12 -4 -2 0 2 4 6 8 Date Strain No dimention (Balanced based on Standard deviation ~9 μ E Simulated data vs Actual Measurement using one year measurement DN12644SBO Measurement Data ARIMAX Model Result Seasonal Mean Seasonal Mean ± Seasonal σ 275 [3] N. M. Okasha, D. M. Frangopol, and A. D. Orcesi, “Automated finite element updating using strain data for the lifetime reliability assessment of bridges,” Reliab. Eng. Syst. Saf., vol. 99, pp. 139–150, Mar. 2012. [4] D. Posenato, F. Lanata, D. Inaudi, and I. F. C. Smith, “Model-free data interpretation for continuous monitoring of complex structures,” Adv. Eng. Informatics, vol. 22, no. 1, pp. 135–144, 2008. [5] R. Yao and S. N. Pakzad, “Data-Driven Methods for Threshold Determination in Time-Series Based Damage Detection,” Struct. Congr. 2011, pp. 77–88, Apr. 2011. [6] S. Vanlanduit, E. Parloo, B. Cauberghe, P. Guillaume, and P. Verboven, “A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties,” J. Sound Vib., vol. 284, no. 3, pp. 1033–1050, 2005. [7] I. Laory, N. B. Hadj Ali, T. N. Trinh, and I. F. C. Smith, “Measurement System Configuration for Damage Identification of Continuously Monitored Structures,” Journal of Bridge Engineering, vol. 17. pp. 857–866, 2012. [8] F. Cavadas, I. F. C. Smith, and J. Figueiras, “Damage detection using data-driven methods applied to moving-load responses,” Mech. Syst. Signal Process., vol. 39, pp. 409–425, 2013. [9] M. Gul and F. N. Catbas, “Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering,” J. Sound Vib., vol. 330, no. 6, pp. 1196–1210, Mar. 2011. [10] K. K. Nair, A. S. Kiremidjian, and K. H. Law, “Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure,” Journal of Sound and Vibration, vol. 291. pp. 349–368, 2006. [11] I. Trendafilova and E. Manoach, “Vibration-based damage detection in plates by using time series analysis,” Mech. Syst. Signal Process., vol. 22, pp. 1092–1106, 2008. [12] S. D. Fassois and J. S. Sakellariou, “Time-series methods for fault detection and identification in vibrating structures.,” Philos. Trans. A. Math. Phys. Eng. Sci., vol. 365, pp. 411–448, 2007. [13] R. RUOTOLO and C. SURACE, “USING SVD TO DETECT DAMAGE IN STRUCTURES WITH DIFFERENT OPERATIONAL CONDITIONS,” J. Sound Vib., vol. 226, no. 3, pp. 425–439, 1999. [14] E. J. Cross, K. Y. Koo, J. M. W. Brownjohn, and K. Worden, “Long-term monitoring and data analysis of the Tamar Bridge,” Mech. Syst. Signal Process., vol. 35, pp. 16–34, 2013. [15] C.J. Mahan, “U. S. Grant Bridge.” . [16] E. Andrew and B. Daniel, “U S Grant Bridge Portsmouth - Fullerton,” 2009. [17] A. Helmicki and V. Hunt, “Instrumentation of the US Grant Bridge for Monitoring of Fabrication , Erection , In-Service Behavior , and to Support Management , Maintenance , and Inspection,” no. 14805, 2013. [18] M. Norouzi, J. Kumpf, V. Hunt, and A. Helmicki, “An Integrated Monitor and Warning System for the Jeremiah Morrow Bridge,” in NDE-NDT for Highways and Bridges: Structural Materials Technology (SMT), 2012. [19] M. Norouzi, V. Hunt, and A. Helmicki, “Abnormal behavior detection in the Jeremiah Morrow Bridge based on the long term measurement data patterns,” in SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2013, pp. 869536–869536–13. [20] H. M. Jaenisch, J. C. Pooley, J. W. Handley, and S. R. Murray, “Data modeling for fault detection,” in MFPT Meeting, 2003. [21] A. Pankratz, Forecasting with univariate Box-Jenkins models: Concepts and cases. 2009. [22] G. Box, G. Jenkins, and G. Reinsel, Time series analysis: forecasting and control. 2013. [23] P. Brockwell and R. Davis, Time series: theory and methods. 2009. [24] C. N. Stefanakos and G. A. Athanassoulis, “A unified methodology for the analysis, completion and simulation of nonstationary time series with missing values, with application to wave data,” Appl. Ocean Res., vol. 23, no. 4, pp. 207–220, Aug. 2001. [25] F. Lanata, “Damage Detection Algorithms For Continous Static Monitoring of Structures,” University of Genoa, 2005. [26] G. A. Athanassoulis and C. N. Stefanakos, “A nonstationary stochastic model for long-term time series of significant wave height,” J. Geophys. Res., vol. 100, no. C8, p. 16149, 1995.
Usage Shares
Total Views
18 Page Views
Total Shares
0 Tweets
0 PDF Downloads
0 Facebook Shares
Total Usage