Abstract
The development of transportation infrastructure systems is an essential part of modern civilization. Any functional deficiency of these systems may cause severe economic losses and social distress. Bridges are among the most vulnerable components of transportation networks. They are exposed to several deterioration processes and traffic loading scenarios during their service life, exacerbating the significant uncertainties on the life-cycle prediction of their structural response. Structural Health Monitoring (SHM) measurements can give significant information and support the damage detection of aging bridges, reducing the uncertainties associated to the structural performance and improving structural reliability of deteriorating systems. This paper presents a life-cycle probabilistic approach to incorporate SHM measurements via Bayesian updating in simulation-based reliability assessment of deteriorating bridges. The proposed methodology is applied to reinforced concrete (RC) bridges exposed to corrosion. The uncertainties of the corrosion model are updated based on SHM data. The Metropolis-Hastings (MH) algorithm is used to update the statistical parameters of the deterioration damage index at the prescribed observation time. The application to time-variant reliability assessment of a RC box-girder continuous bridge under corrosion shows the benefits of SHM to improve the accuracy of life-cycle prediction models.
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References
ASCE: Report card for America’s infrastructure, Reston, VA (2021)
Chen HP (2015) Monitoring-based reliability analysis of aging concrete structures by Bayesian updating. J Aerosp Eng 30(2):1–10
Ellingwood BR (2005) Risk-informed condition assessment of civil infrastructure: state of practice and research issues. Struct Infrastruct Eng 1(1):7–18
Biondini F, Frangopol DM (2016) Life-cycle performance of deteriorating structural systems under uncertainty: review. J Struct Eng 142(9):1–17
Biondini F, Bontempi F, Frangopol DM, Malerba PG (2006) Probabilistic service life assessment and maintenance planning of concrete structures. J Struct Eng 132(5):810–825
Apostolopoulos CA, Papadakis VG (2008) Consequences of steel corrosion on the ductility properties of reinforcement bar. Constr Build Mater 22(12):2316–2324
Jacinto L, Neves LC, Santos LO (2016) Bayesian assessment of an existing bridge: a case study. Struct Infrastruct Eng 12(1):61–77
Strauss A, Frangopol DM, Kim S (2008) Use of monitoring extreme data for the performance prediction of structures: Bayesian updating. Eng Struct 30(12):3654–3666
Enright MP, Frangopol DM (1999) Condition prediction of deteriorating concrete bridges using Bayesian updating. J Struct Eng 125(10):1118–1125
Kim CW, Zhang Y, Wang Z, Oshima Y, Morita T (2018) Long-term bridge health monitoring and performance assessment based on a Bayesian approach. Struct Infrastruct Eng 14(7):883–894
Biondini F, Vergani M (2015) Deteriorating beam finite element for nonlinear analysis of concrete structures under corrosion. Struct Infrastruct Eng 11(4):519–532
Granata RD, Wilson JC, Fisher JW (1996) Assessing corrosion on steel structures using corrosion coulometer. J Infrastruct Syst 2(3):139–144
Naus DJ, Oland CB, Ellingwood BR, Hookham CJ, Graves Iii HL (1999) Summary and conclusions of a program addressing aging of nuclear power plant concrete structures. Nucl Eng Des 194(1):73–96
Sousa H, Rozsas A, Slobbe A, Courage W (2020) A novel pro-active approach towards SHM-based bridge management supported by FE analysis and Bayesian methods. Struct Infrastruct Eng 16(2):233–246
Grosser P (2006) Ultrasonic detection and measurement of isolated or pitting corrosion (IPC). In: Asia-Pacific conference on NDT, Auckland, New Zealand, pp 1–7
Andruschak N, Saletes I, Filleter T, Sinclair A (2015) An NDT guided wave technique for the identification of corrosion defects at support locations. NDT E Int 75:72–79
Atlam A, Omar AA, Waheed AF Shafy M, Habashy M (2010) Non destructive testing measurement for monitoring pitting corrosion using DCPD techniques. In: 4th environmental physics conference, Hunghada, Egypt, pp 10–14
Park S, Kim J, Lee C, Lee J (2014) Magnetic flux leakage sensing-based steel cable NDE technique. Shock Vib 1–8
Ang AH-S, Tang WH (2007) Probability concepts in engineering planning and design: emphasis on application to civil and environmental engineering, 2nd edn. Wiley
Okasha NM, Frangopol DM (2012) Integration of structural health monitoring in a system performance based life-cycle bridge management framework. Struct Infrastruct Eng 8(11):999–1016
MathWorks (2009) Toolbox, statistic 7 user’s guide Natick. The MathWorks, Inc., MA, p 2112
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Yilmaz, M.F., Anghileri, M., Capacci, L., Biondini, F. (2022). Simulation-Based Life-Cycle Structural Reliability of Deteriorating RC Bridges Using Bayesian Updating. In: Pellegrino, C., Faleschini, F., Zanini, M.A., Matos, J.C., Casas, J.R., Strauss, A. (eds) Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures. EUROSTRUCT 2021. Lecture Notes in Civil Engineering, vol 200. Springer, Cham. https://doi.org/10.1007/978-3-030-91877-4_156
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DOI: https://doi.org/10.1007/978-3-030-91877-4_156
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