Determination of appropriate updating parameters for effective life-cycle management of deteriorating structures under uncertainty

References

• Akgül, F. (2002). Lifetime system reliability prediction for multiple structure types in a bridge network (Ph.D. thesis). Boulder, CO: University of Colorado.
   Google Scholar
• Akiyama, M., Frangopol, D.M., & Takenaka, K. (2017). Reliability-based durability design and service life assessment of reinforced concrete deck slab of jetty structures. Structure and Infrastructure Engineering, 13(4), 468–477. doi:10.1080/15732479.2016.1164725
   Web of Science ®Google Scholar
• Ang, A.H.-S., & Tang, W.H. (2007). Probability concepts in engineering: Emphasis on applications to civil and environmental engineering (2nd ed.). New York, NY: Wiley.
   Google Scholar
• Bartilson, D.T., Jang, J., & Smyth, A. W. (2019). Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization. Mechanical Systems and Signal Processing, 114, 328–345. doi:10.1016/j.ymssp.2018.05.024
   Web of Science ®Google Scholar
• Bang, D., Ince, A., & Noban, M. (2019). Modeling approach for a unified crack growth model in short and long fatigue crack regimes. International Journal of Fatigue, 128, 105182. doi:10.1016/j.ijfatigue.2019.06.042
   Web of Science ®Google Scholar
• Biondini, F., & Frangopol, D.M. (2016). Life-cycle performance of deteriorating structural systems under uncertainty. Journal of Structural Engineering, 142(9), F4016001. doi:10.1061/(ASCE)ST.1943-541X.0001544
   PubMed Web of Science ®Google Scholar
• Chiachío, J., Chiachío, M., Saxena, A., Sankararaman, S., Rus, G., & Goebel, K. (2015). Bayesian model selection and parameter estimation for fatigue damage progression model in composites. International Journal of Fatigue, 70, 361–373. Elsevier, doi:10.1016/j.ijfatigue.2014.08.003
   Web of Science ®Google Scholar
• Cruces, A.S., Mokhtarishirazabad, M., Moreno, B., Zanganeh, M., & Lopez-Crespo, P. (2020). Study of the biaxial fatigue behaviour and overloads on S355 low carbon steel. International Journal of Fatigue, 134, 105466. Elsevier, doi:10.1016/j.ijfatigue.2019.105466
   Web of Science ®Google Scholar
• Cui, C., Zhang, Q., Bao, Y., Bu, Y., & Luo, Y. (2019). Fatigue life evaluation of welded joints in steel bridge considering residual stress. Journal of Constructional Steel Research, 153, 509–518. Elsevier, doi:10.1016/j.jcsr.2018.11.003
   Web of Science ®Google Scholar
• Deng, H., Yeh, C.-H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963–973. doi:10.1016/S0305-0548(99)00069-6
   Web of Science ®Google Scholar
• Dong, Y., & Frangopol, D.M. (2016a). Incorporation of risk and updating in inspection of fatigue-sensitive details of ship structures. International Journal of Fatigue, 82, 676–688. doi:10.1016/j.ijfatigue.2015.09.026
   Web of Science ®Google Scholar
• Dong, Y., & Frangopol, D.M. (2016b). Probabilistic time-dependent multihazard life-cycle assessment and resilience of bridges considering climate change. Journal of Performance of Constructed Facilities, 30(5), 04016034. doi:10.1061/(ASCE)CF.1943-5509.0000883
   Web of Science ®Google Scholar
• Estes, A.C., & Frangopol, D.M. (2003). Updating bridge reliability based on bridge management systems visual inspection results. Journal of Bridge Engineering, 8(6), 374–382. doi:10.1061/(ASCE)1084-0702(2003)8:6(374)
   Web of Science ®Google Scholar
• Fisher, J.W. (1984). Fatigue and fracture in steel bridges. New York: John Wiley & Sons.
   Google Scholar
• Fisher, J.W., Kulak, G.L., & Smith, I.F. (1998). A fatigue primer for structural engineers. Chicago, IL: National Steel Bridge Alliance.
   Google Scholar
• Frangopol, D. M., Strauss, A., & Kim, S. (2008). Use of monitoring extreme data for the performance prediction of structures: General approach. Engineering Structures, 30(12), 3644–3653. Elsevier, doi:10.1016/j.engstruct.2008.06.010
   Web of Science ®Google Scholar
• Frangopol, D.M., Dong, Y., & Sabatino, S. (2017). Bridge life-cycle performance and cost: analysis, prediction, optimization and decision-making. Structure and Infrastructure Engineering, 13(10), 1239–1257. Taylor & Fransic doi:10.1080/15732479.2016.1267772
   Web of Science ®Google Scholar
• Frangopol, D.M., & Kim, S. (2019). Life-Cycle of structures under uncertainty: Emphasis on fatigue-sensitive civil and marine structures. Boca Raton, FL: CRC Press/Taylor & Francis.
   Google Scholar
• Gonzalez, J. A., Andrade, C., Alonso, C., & Feliu, S. (1995). Comparison of rate of general corrosion and maximum pitting penetration on concrete embedded steel reinforcement. Cement and Concrete Research, 25(2), 257–264. Elsevier, doi:10.1016/0008-8846(95)00006-2
   Web of Science ®Google Scholar
• Gu, C., Lian, J., Bao, Y., & Münstermann, S. (2019). Microstructure-based fatigue modelling with residual stresses: Prediction of the microcrack initiation around inclusions. Materials Science and Engineering: A, 751, 133–141. doi:10.1016/j.msea.2019.02.058
   Web of Science ®Google Scholar
• Guan, X., Jha, R., & Liu, Y. (2011). Model selection, updating, and averaging for probabilistic fatigue damage prognosis. Structural Safety, 33(3), 242–249. Elsevier, doi:10.1016/j.strusafe.2011.03.006
   Web of Science ®Google Scholar
• Guedes Soares, C., & Garbatov, Y. (1996). Fatigue reliability of the ship hull girder accounting for inspection and repair. Reliability Engineering & System Safety, 51(3), 341–351. Elsevier, doi:10.1016/0951-8320(95)00123-9
   Web of Science ®Google Scholar
• Hasting, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.
   Web of Science ®Google Scholar
• Heitner, B., Obrien, E. J., Yalamas, T., Schoefs, F., Leahy, C., & Decatoire, R. (2019). Updating probabilities of bridge reinforcement corrosion using health monitoring data. Engineering Structures, 190, 41–51. doi:10.1016/j.engstruct.2019.03.103
   Web of Science ®Google Scholar
• Hwang, C.-L., Lai, Y.-J., & Liu, T.-Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899. Pergamon, doi:10.1016/0305-0548(93)90109-V
   Web of Science ®Google Scholar
• Irwin, G. R. (1958). “The Crack-extension-force for a crack at a free surface boundary.” Report No. 5120. Washington, DC: Naval Research Laboratory.
   Google Scholar
• Kailath, T. (1967). The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communications, 15(1), 52–60. Vol.COMdoi:10.1109/TCOM.1967.1089532
   Web of Science ®Google Scholar
• Kim, S., & Frangopol, D.M. (2011a). Optimum inspection planning for minimizing fatigue damage detection delay of ship hull structures. International Journal of Fatigue, 33(3), 448–459. Elsevier, doi:10.1016/j.ijfatigue.2010.09.018
   Web of Science ®Google Scholar
• Kim, S., & Frangopol, D.M. (2011b). Inspection and monitoring planning for RC structures based on minimization of expected damage detection delay. Probabilistic Engineering Mechanics, 26(2), 308–320. Elsevier, doi:10.1016/j.probengmech.2010.08.009
   Web of Science ®Google Scholar
• Kim, S., Frangopol, D. M., & Soliman, M. (2013). Generalized probabilistic framework for optimum inspection and maintenance planning. Journal of Structural Engineering, 139(3), 435–447. ASCE, doi:10.1061/(ASCE)ST.1943-541X.0000676
   Web of Science ®Google Scholar
• Kim, S., & Frangopol, D.M. (2018). Decision making for probabilistic fatigue inspection planning based on multi-objective optimization. International Journal of Fatigue, 111, 356–368. doi:10.1016/j.ijfatigue.2018.01.027
   Web of Science ®Google Scholar
• Kim, S., Ge, B., & Frangopol, D.M. (2019). Effective optimum maintenance planning with updating based on inspection information for fatigue-sensitive structures. Probabilistic Engineering Mechanics, 58, 103003–103012. doi:10.1016/j.probengmech.2019.103003
   Web of Science ®Google Scholar
• Kim, G.-H., & Park, Y.-S. (2004). An improved updating parameter selection method and finite element model update using multiobjective behavior ion technique. Mechanical Systems and Signal Processing, 18(1), 59–78. Elsevier. doi:10.1016/S0888-3270(03)00042-6
   Web of Science ®Google Scholar
• Kullback, S., & Leibler, R.A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86. doi:10.1214/aoms/1177729694
   Google Scholar
• Kwon, K., & Frangopol, D.M. (2011). Bridge fatigue assessment and management using reliability-based crack growth and probability of detection models. Probabilistic Engineering Mechanics, 26(3), 471–480. Elsevier, doi:10.1016/j.probengmech.2011.02.001
   Web of Science ®Google Scholar
• Kwon, K., & Frangopol, D.M. (2012). System reliability of ship hull structures under corrosion and fatigue. Journal of Ship Research, 56(4), 234–251. doi:10.5957/JOSR.56.4.100038
   Web of Science ®Google Scholar
• Li, Z., Zhang, Y., & Wang, C. (2013). A sensor-driven structural health prognosis procedure considering sensor performance degradation. Structure and Infrastructure Engineering, 9(8), 764–776. doi:10.1080/15732479.2011.614259
   Web of Science ®Google Scholar
• Lim, S., Akiyama, M., & Frangopol, D.M. (2016). Assessment of the structural performance of corrosion-affected RC members based on experimental study and probabilistic modeling. Engineering Structures, 127(15), 189–205. doi:10.1016/j.engstruct.2016.08.040
   Google Scholar
• Liao, Z., Yang, B., Qin, Y., Xiao, S., Yang, G., Zhu, T., & Gao, N. (2019). Short fatigue crack behavior of LZ50 railway axle steel under multi-axial loading in low-cycle fatigue. International Journal of Fatigue, 32, 105366, 1–10.
   Google Scholar
• Liu, M., Frangopol, D.M., & Kwon, K. (2010). Fatigue reliability assessment of retrofitted steel bridges integrating monitored data. Structural Safety, 32(1), 77–89. doi:10.1016/j.strusafe.2009.08.003
   Web of Science ®Google Scholar
• Liu, Y., & Frangopol, D.M. (2019). Utility and information analysis for optimum inspection of fatigue-sensitive structures. Journal of Structural Engineering, 145(2), 04018251–04018212. doi:10.1061/(ASCE)ST.1943-541X.0002257
   Web of Science ®Google Scholar
• Liu, Y., Paggi, M., Gong, B., & Deng, C. (2020). A unified mean stress correction model for fatigue thresholds prediction of metals. Engineering Fracture Mechanics, 223(106787), 106787–106789. Elsevier, doi:10.1016/j.engfracmech.2019.106787
   Google Scholar
• Ma, Y., Zhang, J., Wang, L., & Liu, Y. (2013). Probabilistic prediction with Bayesian updating for strength degradation of RC bridge beams. Structural Safety, 44, 102–109. doi:10.1016/j.strusafe.2013.07.006
   Web of Science ®Google Scholar
• MC2010 (2013). Fib Model Code for Concrete Structures 2010. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Morio, J. (2011). Global and local sensitivity analysis methods for a physical system. European Journal of Physics, 32(6), 1577–1583. doi:10.1088/0143-0807/32/6/011
   Web of Science ®Google Scholar
• Minunno, F., van Oijen, M., Cameron, D.R., & Pereira, J.S. (2013a). Selecting parameters for Bayesian calibration of a process-based model: A methodology based on canonical correlation analysis. SIAM/ASA Journal on Uncertainty Quantification, 1(1), 370–385. doi:10.1137/120891344
   Web of Science ®Google Scholar
• Qiao, G., Hong, Y., & Ou, J. (2014). Quantitative monitoring of pitting corrosion based on 3-D cellular automata and real-time ENA for RC structures. Measurement, 53, 270–276. doi:10.1016/j.measurement.2014.03.045
   Web of Science ®Google Scholar
• Okasha, N. M., & Frangopol, D.M. (2010). Integration of structural health monitoring in a system performance based life-cycle bridge management framework. Structure and Infrastructure Engineering, 8(11), 1–1016. doi:10.1080/15732479.2010.485726
   Web of Science ®Google Scholar
• Paris, P., & Erdogan, F. (1963). A critical analysis of crack propagation laws. Journal of Basic Engineering, 85(4), 528–533. ASME, doi:10.1115/1.3656900
   Google Scholar
• Rastogi, R., Ghosh, S., Ghosh, A. K., Vaze, K. K., & Singh, P. K. (2017). Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation. Fatigue & Fracture of Engineering Materials & Structures, 40(1), 145–156. doi:10.1111/ffe.12486
   Web of Science ®Google Scholar
• Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., … Tarantola, S. (2008). Global sensitivity analysis: The primer. Hoboken, NJ: John Wiley & Sons, 304pp.
   Google Scholar
• Sánchez-Silva, M., Frangopol, D.M., Padgett, J., & Soliman, M. (2016). Maintenance and operation of infrastructure systems: A review. Journal of Structural Engineering, 142(9), 1–13. doi:10.1061/(ASCE)ST.1943-541X.0001543
   Web of Science ®Google Scholar
• Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2009). Uncertainty quantification in fatigue damage prognosis. The Proceedings of the 1^st Annual Conference of the Prognostics and Health Management Society, San Diego, CA, September 27–October 1.
   Google Scholar
• Sankararaman, S., Ling, Y., & Mahadevan, S. (2011). Uncertainty quantification and model validation of fatigue crack growth prediction. Engineering Fracture Mechanics, 78(7), 1487–1504. Elsevier, doi:10.1016/j.engfracmech.2011.02.017
   Web of Science ®Google Scholar
• Sobol, I. M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55, 271–280.
   Web of Science ®Google Scholar
• Strauss, A., Frangopol, D. M., & Kim, S. (2008). Use of monitoring extreme data for the performance prediction of structures: Bayesian updating. Engineering Structures, 30(12), 3654–3666. doi:10.1016/j.engstruct.2008.06.009
   Web of Science ®Google Scholar
• Strauss, A. (2016). Numerical and monitoring based Markov Chain approaches for the fatigue life prediction of concrete structures. Engineering Structures, 112, 265–273. doi:10.1016/j.engstruct.2016.01.020
   Google Scholar
• Strauss, A., Wendner, R., Bergmeister, K., Reiterer, M., & Horvatits, J. (2011). Modellkorrekturfaktoren als “Performance Indikatoren”, für die Langzeitbewertung der integralen Marktwasserbrücke S33.24.” Beton- und Stahlbetonbau. Beton- Und Stahlbetonbau, 106(4), 231–240. doi:10.1002/best.201100003
   Google Scholar
• Strauss, A. (2016). Numerical and monitoring based Markov Chain approaches for the fatigue life prediction of concrete structures. Engineering Structures, 112, 265–273. doi:10.1016/j.engstruct.2016.01.020
   Web of Science ®Google Scholar
• Suo, Q., & Stewart, M. G. (2009). Corrosion cracking prediction updating of deteriorating RC structures using inspection information. Reliability Engineering & System Safety, 94(8), 1340–1348. doi:10.1016/j.ress.2009.02.011
   Web of Science ®Google Scholar
• Soliman, M., & Frangopol, D.M. (2014). Life-cycle management of fatigue-sensitive structures integrating inspection information. Journal of Infrastructure Systems, 20(2), 04014001. doi:10.1061/(ASCE)IS.1943-555X.0000169
   Web of Science ®Google Scholar
• Soliman, M., Frangopol, D.M., & Mondoro, A. (2016). A probabilistic approach for optimizing inspection, monitoring, and maintenance actions against fatigue of critical ship details. Structural Safety, 60, 91–101. doi:10.1016/j.strusafe.2015.12.004
   Web of Science ®Google Scholar
• Staszewski, W. J. (2008). Fatigue crack detection using smart sensor technologies. Fatigue & Fracture of Engineering Materials & Structures, 31(8), 609–610.
   Web of Science ®Google Scholar
• Stewart, M. G. (2004). Spatial variability of pitting corrosion and its influence of structural fragility and reliability of RC beams in flexure. Structural Safety, 26(4), 453–470. doi:10.1016/j.strusafe.2004.03.002
   Web of Science ®Google Scholar
• Steyvers, M. (2011). Computational statistics with Matlab. Lecture. Irvine, CA: University of California Irvine.
   Google Scholar
• Val, D. V., & Melchers, R. E. (1997). Reliability of deteriorating RC slab bridges. Journal of Structural Engineering, 123(12), 1638–1644. doi:10.1061/(ASCE)0733-9445(1997)123:12(1638)
   Web of Science ®Google Scholar
• Wang, C., & Jo, B. (2013). Applications of a Kullback–Leibler divergence for comparing non-nested models. Statistical Modelling, 13(5/6), 409–429. doi:10.1177/1471082X13494610
   PubMed Web of Science ®Google Scholar
• Wan, H. P., & Ren, W. X. (2015). Parameter selection in finite-element model updating by global sensitivity analysis using Gaussian process metamodel. Journal of Structural Engineering, 141(6), 1–11. doi:10.1061/(ASCE)ST.1943-541X.0001108
   Web of Science ®Google Scholar
• Willmott, C., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. doi:10.3354/cr030079
   Web of Science ®Google Scholar
• Yuan, Z., Liang, P., Silva, T., Yu, K., & Mottershead, J. E. (2019). Parameter selection foe model updating with global sensitivity analysis. Mechanical Systems and Signal Processing, 115(15), 483–496. doi:10.1016/j.ymssp.2018.05.048
   Google Scholar
• Zárate, B. A., Caicedo, J. M., Yu, J., & Ziehl, P. (2012). Bayesian model updating and prognosis of fatigue crack growth. Engineering Structures, 45, 53–61. doi:10.1016/j.engstruct.2012.06.012
   Web of Science ®Google Scholar
• Zhang, M., Song, H., Lim, S., Akiyama, M., & Frangopol, D.M. (2019). Reliability estimation of corroded RC structures based on spatial variability using experimental evidence, probabilistic analysis and finite element method. Engineering Structures, 192, 30–52. doi:10.1016/j.engstruct.2019.04.085
   Web of Science ®Google Scholar
• Zhang, Q., Toda-Caraballo, I., Li, Q., Han, J., Han, J., Zhao, J., & Dai, G. (2020). Tension-shear multiaxial fatigue damage behavior of high-speed railway wheel rim steel. International Journal of Fatigue, 133, 105416. doi:10.1016/j.ijfatigue.2019.105416
   Web of Science ®Google Scholar