Technical Papers
Sep 16, 2013

Methodology for Designing Diagnostic Data Streams for Use in a Structural Impairment Detection System

Publication: Journal of Bridge Engineering
Volume 19, Issue 4

Abstract

This paper outlines a general methodology used to design digital data streams of electronic sensors attached to critical components of a structure to be processed by a structural impairment detection system (SIDS). The methodology begins by evaluating a specific structure, establishing a baseline behavior profile, and identifying specific structural impairments that are likely to occur. Finite-element modeling of a specific test bed is presented as a tool with which to design and create diagnostic data streams. Establishing a list probable impairments is critical to designing diagnostic data streams with which to implement a SIDS capable of indicating and, more importantly, identifying digital data signatures that may indicate specific structural impairments. Once a set of impairment scenarios is defined, finite-element models representing those impairments are created and several locations sensitive to modeled impairments are identified. A matrix of different locations and measurement types determines the instrumentation schedule. Through experimental observation and iterative structural analysis, diagnostic data streams are created that serve as (1) patterns for training neural diagnostic algorithms and (2) patterns to be interrogated to evaluate a testbed structure.

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Acknowledgments

The authors greatly acknowledge significant financial and in-kind support provided by the owner of the drawbridge used as a testbed in this project.

References

ABAQUS [Computer software]. Providence, RI, SIMULIA.
Bocca, M., Eriksson, L. M., Mahmood, A., and Jantti, R. (2011). “A synchronized wireless sensor network for experimental modal analysis in structural health monitoring.” Comput. Aided Civ. Infrastruct. Eng., 26(7), 483–499.
Catbas, F. N., et al. (2009). “Structural health monitoring of bridges: Fundamentals, application case study and organizational considerations.” Proc., Structures Concress, ASCE, Reston, VA, 108–118.
Chang, C. C., Chang, T. Y. P., and Xu, Y. G. (2000). “Structural damage detection using an iterative neural network.” J. Intell. Mater. Syst. Struct., 11(1), 32–42.
Doebling, S. W., Farrar, C. R., and Prime, M. B. (1997). “A summary review of vibration-based damage identification methods.” Technical Rep., LA-UR-98-0375, Los Alamos National Laboratory, Los Alamos, NM.
Fang, X., Luo, H., and Tang, J. (2005). “Structural damage detection using neural network with learning rate improvement.” Comp. Struct., 83(25–26), 2150–2161.
Farrar, C. R., Park, G., Allen, D. W., and Todd, M. D. (2006). “Sensor network paradigms for structural health monitoring.” J. Struct. Control Health Monit., 13(1), 210–225.
Frýba, L. (1996). Dynamics of railway bridges, T. Telford, London.
Graydon, E. R. (1949). “Counterweight replacement on the Cherry Street bascule bridge.” Eng. J., 32(3), 126–129.
Hagan, H. T., Demuth, H. B., and Beale, M. (1996). Neural network design, PWS Publishing, Boston.
Haykin, S. (1999). Neural networks, Macmillian Publishing, Englewood Cliffs, NJ.
Hool, G. A., and Kinne, W. S. (1923). Moveable and long-span steel bridges, McGraw Hill, New York.
Hyland, D. C., and Davis, L. D. (2002). “Toward self-reliant control for adaptive structures.” Acta Astronaut, 51(1–9), 89–99.
Inaudi, D., Manetti, L., and Glisic, B. (2009). “Integrated systems for structural health monitoring.” Proc., 4th Int. Conf. on Structural Health Monitoring and Intelligent Infrastructure (SHMII), SHMII, Zurich, Switzerland, 1–12.
Jiang, X., and Adeli, H. (2007a). “Neuro-genetic algorithm for non-linear active control of structures.” Int. J. Numer. Methods Eng., 75(7), 770–786.
Jiang, X., and Adeli, H. (2007b). “Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings.” Int. J. Numer. Methods Eng., 71(5), 606–629.
Kaufman, P. L. (1912). “The heel trunnion bascule bridge.” Eng. News, 67(18), 830–833.
Koglin, T. L. (2003). Movable bridge engineering, Wiley, Hoboken, NJ.
Li, H. N., Sun, H. M., and Song, G. B. (2004). “Damage diagnosis of framework structure based on wavelet packer analysis and neural network.” Smart structures and materials 2004: Sensors and smart structures technologies for civil, mechanical, and aerospace systems, Proc. SPIE, S-C. Liu, ed., 5391, 533–542.
Liu, M., Frangopol, D. M., and Kim, S. (2009). “Bridge system assessment from structural health monitoring: A case study.” J. Struct. Eng., 733–742
Osornio-Rios, R. A., Amezquita-Sanchez, J. P. A., Romero-Troncoso, J., and Garcia-Perez, A. (2012). “MUSIC-ANN analysis for locating structural damages in a truss-type structure by means of vibration.” Comput. Aided Civ. Infrastruct. Eng., 27(9), 687–698.
Pandey, P. C., and Barai, S. V. (1995). “Multilayer perception in damage detection of bridge structures.” Comp. Struct., 54(4), 597–608.
Qiao, L., Esmaeily, A., and Melhem, H. G. (2012). “Signal pattern recognition for damage diagnosis in structures.” Comput. Aided Civ. Infrastruct. Eng., 27(9), 699–710.
Rytter, A. (1993). “Vibration based inspection of civil engineering structures.” Ph.D. dissertation, Aalborg Univ., Aalborg, Denmark.
Salawu, O. S. (1997). “Detection of structural damage through changes in frequency: A review.” Eng. Struct., 19(9), 718–723.
SAP2000 [Computer software]. Berkeley, CA, Computers and Structures.
Sohn, H., et al. (2001). “A review of structural health monitoring literature: 1996–2001.” Technical Rep. LA-13976-MS, Los Alamos National Laboratory, Los Alamos, NM.
Story, B. A. (2012). “Structural impairment detection using arrays of competitive artificial neural networks.” Ph.D. dissertation, Texas A&M Univ., College Station, TX.
Worden, K., and Dulieu-Barton, J. M. (2004). “An overview of intelligent fault detection in systems and structures.” Int. J. Struct. Health Monitor., 3(1), 85–98.
Yeung, W. T., and Smith, J. W. (2005). “Damage detection in bridges using neural networks for pattern recognition of vibration signatures.” J. Eng. Struct., 27(5), 685–698.

Information & Authors

Information

Published In

Go to Journal of Bridge Engineering
Journal of Bridge Engineering
Volume 19Issue 4April 2014

History

Received: Apr 10, 2013
Accepted: Sep 13, 2013
Published online: Sep 16, 2013
Published in print: Apr 1, 2014
Discussion open until: May 19, 2014

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Authors

Affiliations

Brett A. Story [email protected]
Assistant Professor, Dept. of Civil and Environmental Engineering, Southern Methodist Univ., Dallas, TX 75275; formerly, Postdoctoral Research Associate, Texas A&M Univ., Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843 (corresponding author). E-mail: [email protected]
Gary T. Fry
Associate Professor, Texas A&M Univ., Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX 77843.

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