Abstract
The utilization of Structural Health Monitoring (SHM) for performance-based evaluation of structural systems requires the integration of sensing with appropriate data interpretation algorithms to establish an expected performance related to damage or structural change. In this study, a hybrid data interpretation framework is proposed for the long-term performance assessment of structures by integrating two data analysis approaches: parametric (model-based, physics-based) and non-parametric (data-driven, model-free) approaches. The proposed framework employs a network of sensors through which the performance of the structure is evaluated and the corresponding maintenance action can be efficiently taken almost in real-time. The hybrid algorithm proposed can be categorized as a supervised classification algorithm. In the training phase of the algorithm, a Monte-Carlo simulation technique along with Moving Principal Component Analysis (MPCA) and hypothesis testing are employed for simulation, signal processing, and learning the underlying distribution, respectively. The proposed approach is demonstrated and its performance is evaluated through both analytical and experimental studies. The experimental study is performed using a laboratory structure (UCF 4-Span Bridge) instrumented with a Fiber Brag Grating (FBG) system developed in-house for collecting data under common bridge damage scenarios. The proposed hybrid approach holds potential to significantly enhance sensor network design, as well as continuous evaluation of the structural performance.
Similar content being viewed by others
References
Bozorgnia Y, Bertero VV (eds) (2004) Earthquake engineering: from engineering seismology to performance-based engineering. Crc Press, Boca Raton
Krawinkler H, Miranda E (2004) Performance-based earthquake engineering. In: Bertero VV, Bozorgnia Y. (eds) Earthquake engineering: from engineering seismology to performance-based engineering, chap 9. CRC Press, Boca Raton
Priestley MJN (2000) Performance based seismic design. Bulletin of the New Zealand society for earthquake engineering 33(3):325–346
Frangopol DM, Kong JS, Gharaibeh ES (2001) Reliability-based life-cycle management of highway bridges. J Comput Civ Eng 15(1):27–34
Vink ET, Rabago KR, Glassner DA, Gruber PR (2003) Applications of life cycle assessment to NatureWorks™ polylactide (PLA) production. Polym Degrad Stab 80(3):403–419
Sage AP, Rouse WB (2011) Handbook of systems engineering and management. Wiley, New York
Chang PC, Flatau A, Liu SC (2003) Review paper: health monitoring of civil infrastructure. Struct Health Monit 2(3):257–267
Catbas FN, Shah M, Burkett J, Basharat A (2004) Challenges in structural health monitoring. In: Proceedings of the 4th International Workshop on Structural Control, pp 10–11
Aktan AE, Catbas FN, Grimmelsman KA, Tsikos CJ (2000) Issues in infrastructure health monitoring for management. J Eng Mech 126(7):711–724
Balageas D, Fritzen CP, Güemes A (2010) Structural health monitoring, vol 90. Wiley, New York
Brownjohn JM (2007) Structural health monitoring of civil infrastructure. Philos Trans R Soc A Math Phys Eng Sci 365(1851):589–622
Catbas FN, Susoy M, Frangopol DM (2008) Structural health monitoring and reliability estimation: long span truss bridge application with environmental monitoring data. Eng Struct 30(9):2347–2359
Flynn EB, Todd MD (2010) A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing. Mech Syst Signal Process 24(4):891–903
Ko JM, Ni YQ (2005) Technology developments in structural health monitoring of large-scale bridges. Eng Struct 27(12):1715–1725
Catbas FN, Malekzadeh M, Khuc T (2013) Movable bridge maintenance monitoring. Report submitted to florida department of transportation, Contract No. BDK78-977-10
Spencer BF, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: opportunities and challenges. Struct Control Health Monit 11(4):349–368
Sohn H (2007) Effects of environmental and operational variability on structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):539–560
Catbas FN, Gul M, Zaurin R, Gokce HB, Terrell T, Dumlupinar T, Maier D (2010) Long term bridge maintenance monitoring demonstration on a movable bridge: a framework for structural health monitoring of movable bridges
Jang S, Jo H, Cho S, Mechitov K, Rice JA, Sim SH, Agha G (2010) Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation. Smart Struct Syst 6(5–6):439–459
Malekzadeh M, Gul M, Catbas FN (2012) Use of FBG sensors to detect damage from large amount of dynamic measurements. In: topics on the dynamics of civil structures, vol 1. Springer, New York, pp 273–281
Farrar CR, Lieven NA (2007) Damage prognosis: the future of structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):623–632
Aktan AE, Tsikos CJ, Catbas FN, Grimmlsman K, Barrish R (1999) Challenges and opportunities in bridge health monitoring. In: Proceedings of 2nd Int. Workshop on Structural Health Monitoring vol 1(999). pp 461–473
Hall SR (1999) The effective management and use of structural health data. In: Proceedings of the 2nd International Workshop on Structural Health Monitoring, pp 265–275
Sun FP, Chaudhry ZA, Rogers CA, Majmundar M, Liang C (1995) Automated real-time structure health monitoring via signature pattern recognition. In: Proceedings of SPIE smart structures and materials 1995: smart structures and integrated systems, vol 2443
Masri SF, Sheng LH, Caffrey JP, Nigbor RL, Wahbeh M, Abdel-Ghaffar AM (2004) Application of a web-enabled real-time structural health monitoring system for civil infrastructure systems. Smart Mater Struct 13(6):1269
Posenato D, Lanata F, Inaudi D, Smith IF (2008) Model-free data interpretation for continuous monitoring of complex structures. Adv Eng Inform 22(1):135–144
Sohn H, Farrar CR, Hunter NF, Worden K (2001) Structural health monitoring using statistical pattern recognition techniques. Trans Am Soc Mech Eng J Dyn Syst Meas Control 123(4):706–711
Sohn H, Czarnecki JA, Farrar CR (2000) Structural health monitoring using statistical process control. J Struct Eng 126(11):1356–1363
Farrar CR, Worden K (2012) Structural health monitoring: a machine learning perspective. Wiley, New York
Doebling SW, Farrar CR, Prime MB, Shevitz DW (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review (No. LA–13070-MS). Los Alamos National Lab., NM, US
Liu TY, Chiang WL, Chen CW, Hsu WK, Lu LC, Chu TJ (2011) Identification and monitoring of bridge health from ambient vibration data. J Vib Control 17(4):589–603
Aktan AE, Farhey DN, Helmicki AJ, Brown DL, Hunt VJ, Lee KL, Levi A (1997) Structural identification for condition assessment: experimental arts. J Struct Eng 123(12):1674–1684
Catbas FN, Kijewski-Correa T (2013) Structural identification of constructed systems: collective effort toward an integrated approach that reduces barriers to adoption. J Struct Eng 139(10):1648–1652
Worden K, Manson G (2007) The application of machine learning to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):515–537
Hu F, Hao Q (eds) (2012) Intelligent sensor networks: the integration of sensor networks, signal processing and machine learning. CRC Press, Boca Raton
Posenato D, Kripakaran P, Inaudi D, Smith IF (2010) Methodologies for model-free data interpretation of civil engineering structures. Comput Struct 88(7):467–482
Catbas FN, Gokce HB, Gul M (2012) Nonparametric analysis of structural health monitoring data for identification and localization of changes: concept, lab, and real-life studies. Structural Health Monitoring 11(5):613–626
Malekzadeh M, Gul M, Catbas FN (2013) Application of multivariate statistically based algorithms for civil structures anomaly detection. In Topics in Dynamics of Civil Structures, vol 4. Springer, New York, pp 289–298
Kwon IB, Malekzadeh M, Ma Q, Gokce H, Terrell TK, Fedotov A, Catbas FN (2011) Fiber optic sensor installation for monitoring of 4 span model bridge in UCF. In: rotating machinery, structural health monitoring, shock and vibration, vol 5. Springer, New York, pp 383–388
Malekzadeh M, Gul M, Kwon IB, Catbas FN (2014) An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis. Smart Structures and Systems vol 14(5), pp 917–942. doi:10.12989/sss.2014.14.5.917
Laory I, Trinh TN, Smith IF (2011) Evaluating two model-free data interpretation methods for measurements that are influenced by temperature. Adv Eng Inform 25(3):495–506
Posenato D, Lanata F, Inaudi D, Smith IF (2006) Model free interpretation of monitoring data. In: intelligent computing in engineering and architecture, Springer, Berlin, pp 529–533
Santos JP, Orcesi AD, Crémona C, Silveira P (2014) Baseline-free real-time assessment of structural changes. Structure and Infrastructure Engineering, pp 1–17, (ahead-of-print)
Gokce HB, Catbas FN, Gul M, Frangopol DM (2013) Structural identification for performance prediction by considering uncertainties through a family of models. J Struct Eng ASCE 139(10):1703–1716
Catbas FN, Gokce HB, Frangopol DM (2013) Predictive analysis by incorporating uncertainty through a family of models calibrated with structural health monitoring data. J Eng Mech ASCE 139(6):712–724
Poor HV (1988) An introduction to signal detection and estimation, vol 1. Springer, New York, p 559
Kano M, Hasebe S, Hashimoto I, Ohno H (2001) A new multivariate statistical process monitoring method using principal component analysis. Comput Chem Eng 25(7):1103–1113
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Malekzadeh, M., Atia, G. & Catbas, F.N. Performance-based structural health monitoring through an innovative hybrid data interpretation framework. J Civil Struct Health Monit 5, 287–305 (2015). https://doi.org/10.1007/s13349-015-0118-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13349-015-0118-7