Structural Identification for Performance Prediction Considering Uncertainties: Case Study of a Movable Bridge
Publication: Journal of Structural Engineering
Volume 139, Issue 10
Abstract
Structural identification (St-Id) can be described simply as estimating the properties of a structural system based on a correlation of inputs and outputs for decision making. For a complete St-Id process, establishing the decision-making needs, developing analytical and numerical models, and conducting field measurements, along with parameter identification using the experimental data for model calibration, are carried out. One important consideration is evaluation of the limitations and adequacy of using a single calibrated model before leveraging it for decision making, such as the reliability of the structural system for the remainder of its design life. The uncertainties in the data collected, determined by means of intermittent testing or monitoring; the limitations of the models; and the nonstationary nature of structural behavior need to be considered. These uncertainties can be incorporated by using of a family of parent and offspring models. The objective of this paper is to illustrate the use of a family of models that incorporates the uncertainties and makes predictions in terms of load rating and system-level reliability with the help of structural health monitoring (SHM) data. First, a finite-element (FE) model of a movable bridge is calibrated with SHM data, and parent FE models are created to best represent the measurements. At the same time, uncertainties in critical structural parameters such as boundary conditions are considered by offspring models. The family-of-models approach is employed to estimate load rating and system reliability by considering the probability of failure of the system with different correlations among the safety margins of the components. Finally, future performance of the movable bridge in the case of damage and deterioration is estimated for demonstration of structural identification for performance prediction by considering uncertainties. Such results are expected to provide a set of solutions for the performance of a structure for optimal decision making.
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Acknowledgments
The research project described in this paper was supported by the Federal Highway Administration (FHWA) Cooperative Agreement Award DTFH61-07-H-00040. The authors express their profound gratitude to Dr. Hamid Ghasemi of the FHWA for his support of this research. In addition, the Florida Department of Transportation supported the research project as well as helped with field coordination. The authors also acknowledge the support and contributions of their research sponsors, collaborators, and research team. From the University of Central Florida, Dr. Yunus Dere and Mr. Thomas Terrell were instrumental in the collection and analysis of the monitoring data, and Mr. Taha Dumlupinar was instrumental in the development of the finite-element models and other Visual Basic programs for collecting the data from FE models for the family of models. Dr. Kirk Grimmelsman from University of Arkansas also made important contributions for the field implementation of the monitoring system. From Lehigh University, Mr Alberto Deco, graduate research assistant, was instrumental in using the RELSYS program for analysis of the data obtained from the FE models. The authors greatly appreciate the contributions of these researchers and graduate students. The opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.
References
AASHTO. (2004). LRFD bridge design specifications, Washington, DC.
Albrecht, P., and Naeemi, A. H. (1984). Performance of weathering steel in bridges, Transportation Research Board, Washington, DC.
Ang, A. H. S., and De Leon, D. (2005). “Modeling and analysis of uncertainties for risk-informed decisions in infrastructures engineering.” Struct. Infrastruct. Eng., 1(1), 19–31.
Ang, A. H.-S., and Tang, W. H. (1984). Probability concepts in engineering planning and design, Vol. II, Wiley, New York.
Beck, J. L., and Au, S.-K. (2002). “Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation.” J. Eng. Mech., 128(4), 380–391.
Beck, J. L., and Katafygiotis, L. S. (1998). “Updating models and their uncertainties. I: Bayesian statistical framework.” J. Eng. Mech., 124(4), 455–461.
Beck, J. L., and Yuen, K. V. (2004). “Model selection using response measurements: Bayesian probabilistic approach.” J. Eng. Mech., 130(2), 192–203.
Bell, E. S., Sanayei, M., Chitra, N. J., and Eugene, S. (2007). “Multiresponse parameter estimation for finite-element model updating using nondestructive test data.” J. Struct. Eng., 133(8), 1067–1079.
Brownjohn, J. M. (2003). “Ambient vibration studies for system identification of tall buildings.” Earthq. Eng. Struct. Dynam., 32(1), 71–95.
Catbas, F. N., et al. (2010). “Long term bridge maintenance monitoring demonstration on a movable bridge: A framework for structural health monitoring of movable bridges.” Final Rep. for Contract BD548-23, Florida Dept. of Transportation (FDOT), Tallahassee, FL.
Catbas, F. N., and Aktan, A. E. (2002). “Condition and damage assessment: Issues and some promising indices.” J. Struct. Eng., 128(8), 1026–1036.
Catbas, F. N., Ciloglu, S. K., Hasancebi, O., Grimmelsman, K. A., and Aktan, A. E. (2007a). “Limitations in structural identification of large constructed structures.” J. Struct. Eng., 133(8), 1051–1066.
Catbas, F. N., Gokce, H. B., and Gul, M. (2012). “Practical approach for estimating distribution factor for load rating: Demonstration on reinforced concrete T-beam bridges.” J. Bridge Eng., 17(4), 652–661.
Catbas, F. N., Gokce, H. B., Gul, M., and Frangopol, D. M. (2011). “Movable bridges: Condition, modeling and damage simulations.” Institution of Civil Engineers (ICE)-Bridge Engineering Journal, 164(3), 145–155.
Catbas, F. N., Susoy, M., and Frangopol, D. M. (2008). “Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data.” Eng. Struct., 30(9), 2347–2359.
Catbas, F. N., Zaurin, R., Susoy, M., and Gul, M. (2007b). “Integrative information system design for Florida Department of Transportation: A framework for structural health monitoring of movable bridges.” Final Rep. for Contract No. BD548-11, Florida Dept. of Transportation (FDOT), Tallahassee, FL.
Catbas, N., Gokce, H. B., and Frangopol, D. M. (2013). “Predictive analysis by incorporating uncertainty through a family of models calibrated with structural health-monitoring data.” J. Eng. Mech., 139(6), 712–723.
Ching, J., and Hsieh, Y.-H. (2009). “Updating real-time reliability of instrumented systems with stochastic simulation.” Probab. Eng. Mech., 24(2), 242–250.
Der Kiureghian, A., and Ditlevsen, O. (2009). “Aleatory or epistemic? Does it matter?” Struct. Saf., 31(2), 105–112.
Ditlevsen, O., and Jacob Tarp-Johansen, N. (1999). “Choice of input fields in stochastic finite elements.” Probab. Eng. Mech., 14(1–2), 63–72.
Estes, A. C., and Frangopol, D. M. (1998). “RELSYS: A computer program for structural system reliability analysis.” Struct. Eng. Mech., 6(8), 901–919.
Estes, A. C., and Frangopol, D. M. (1999). “Repair optimization of highway bridges using system reliability approach.” J. Struct. Eng., 125(7), 766–775.
Frangopol, D. M. (2011). “Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges.” Struct. Infrastruct. Eng., 7(6), 389–413.
Garrett, G. P. (2007). “Analytical load rating of an open-spandrel arch bridge: Case study.” J. Bridge Eng., 12(1), 13–20.
Ghanem, R. G., and Spanos, P. D. (1991). “Spectral stochastic finite-element formulation for reliability analysis.” J. Eng. Mech., 117(10), 2351–2372.
Gokce, H. B., Catbas, F. N., and Frangopol, D. M. (2011). “Evaluation of load rating and system reliability of movable bridge.” Transportation Research Record 2251, Transportation Research Board, Washington, DC, 114–122.
Goulet, J., Kripakaran, P., and Smith, I. F. (2010). “Multimodel structural performance monitoring.” J. Struct. Eng., 136(10), 1309–1318.
Guan, X. L., and Melchers, R. E. (1999). “A load space formulation for probabilistic finite element analysis of structural reliability.” Probab. Eng. Mech., 14(1–2), 73–81.
Gul, M., and Catbas, F. N. (2008). “Ambient vibration data analysis for structural identification and global condition assessment.” J. Eng. Mech., 134(8), 650–662.
Haukaas, T., and Gardoni, P. (2011). “Model uncertainty in finite-element analysis: Bayesian finite elements.” J. Eng. Mech., 137(8), 519–526.
Huang, S., Mahadevan, S., and Rebba, R. (2007). “Collocation-based stochastic finite element analysis for random field problems.” Probab. Eng. Mech., 22(2), 194–205.
Katafygiotis, L. S., and Beck, J. L. (1998). “Updating models and their uncertainties. II: Model identifiability.” J. Eng. Mech., 124(4), 463–467.
Katafygiotis, L. S., Papadimitriou, C., and Lam, H.-F. (1998). “A probabilistic approach to structural model updating.” Soil. Dyn. Earthq. Eng., 17(7–8), 495–507.
Koglin, T. L. (2003). Movable bridge engineering, Wiley, Hoboken, NJ.
Kripakaran, P., and Smith, I. F. C. (2009). “Configuring and enhancing measurement systems for damage identification.” Adv. Eng. Inform., 23(4), 424–432.
LeBeau, K., and Wadia-Fascetti, S. (2007). “Comparative probabilistic initial bridge load rating model.” J. Bridge Eng., 12(6), 785–793.
Levenberg, K. (1944). “A method for the solution of certain problems in least squares.” Q. Appl. Math., 2, 164–168.
Ljung, L. (1999). System identification: Theory for the user, Prentice-Hall, Upper Saddle River, NJ.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Math., 11(2), 431–441.
Miller, R. A., Aktan, A. E., and Shahrooz, B. M. (1994). “Destructive testing of decommissioned concrete slab bridge.” J. Struct. Eng., 120(7), 2176–2198.
Moon, F. L., and Aktan, A. E. (2006). “Impacts of epistemic uncertainty on structural identification of constructed systems.” Shock Vib. Dig., 38(5), 399–420.
Nowak, A. S. (1999). “Calibration of LRFD bridge design code.” NCHRP Rep. 368, Transportation Research Board, Washington, DC.
Nowak, A. S., and Collins, K. R. (2000). Reliability of structures, McGraw-Hill, New York.
Papadimitriou, C., Beck, J. L., and Au, S.-K. (2000). “Entropy-based optimal sensor location for structural model updating.” J. Vib. Control, 6(5), 781–800.
Papadimitriou, C., Beck, J. L., and Katafygiotis, L. S. (2001). “Updating robust reliability using structural test data.” Probab. Eng. Mech., 16(2), 103–113.
Reich, G. W., and Park, K. C. (2001). “A theory for strain-based structural system identification.” J. Appl. Mech., 68(4), 521–527.
Robert-Nicoud, Y., Raphael, B., Burdet, O., and Smith, I. F. C. (2005). “System identification through model composition and stochastic search.” J. Comput. Civ. Eng., 19(3), 239–247.
Sanayei, M., Bell, E. S., Javdekar, C. N., Edelmann, J., and Slavsky, E. (2006). “Damage localization and finite-element model updating using multiresponse NDT data.” J. Bridge Eng., 11(6), 688–698.
Schuëller, G. I. (2001). “Computational stochastic mechanics: Recent advances.” Comput. Struct., 79(22–25), 2225–2234.
Schuëller, G. I., and Jensen, H. A. (2008). “Computational methods in optimization considering uncertainties: An overview.” Comput. Methods Appl. Mech. Eng., 198(1), 2–13.
Shinozuka, M., and Deodatis, G. (1988). “Response variability of stochastic finite element systems.” J. Eng. Mech., 114(3), 499–519.
Smith, I. F. C., and Saitta, S. (2008). “Improving knowledge of structural system behavior through multiple models.” J. Struct. Eng., 134(4), 553–561.
Sohn, H., and Law, K. H. (1997). “A Bayesian probabilistic approach for structure damage detection.” Earthq. Eng. Struct. Dynam., 26(12), 1259–1281.
Soize, C. (2005). “A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in structural dynamics.” J. Sound Vib., 288(3), 623–652.
Soize, C. (2013). “Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances.” J. Sound Vib., 332(10), 2379–2395.
Soize, C., Capiez-Lernout, E., Durand, J. F., Fernandez, C., and Gagliardini, L. (2008). “Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation.” Comput. Methods Appl. Mech. Eng., 198(1), 150–163.
Sudret, B., and Der Kiureghian, A. (2002). “Comparison of finite element reliability methods.” Probab. Eng. Mech., 17(4), 337–348.
Zhang, J., and Ellingwood, B. (1996). “SFEM for reliability of structures with material nonlinearities.” J. Struct. Eng., 122(6), 701–704.
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Published In
Journal of Structural Engineering
Volume 139 • Issue 10 • October 2013
Pages: 1703 - 1715
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 14, 2011
Accepted: Mar 2, 2012
Published online: Mar 6, 2012
Published in print: Oct 1, 2013
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