Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Prediction of remaining service life of reinforced concrete bridges exposed to carbonate corrosion using Bayesian network

Document Type : Original Article

Authors
1 Ph.D. Candidate, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
2 Professor, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
3 Assistant Professor, Faculty of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran
Abstract
The durability of reinforced concrete bridges can be compromised by environmental factors, with carbonation being a pivotal contributor to their degradation. Carbonation-induced corrosion, particularly in severe weather conditions, critically undermines the structural integrity of bridges. This phenomenon, characterized by the reaction of atmospheric CO2 with the alkaline components of concrete, precipitates a reduction in pH from above 13 to below 9, thereby compromising the protective layer encasing the steel reinforcement. Assessing the residual service life of such structures is essential for timely maintenance, reinforcement, or reconstruction. This study explores a hybrid approach to estimate the remaining useful life of reinforced concrete bridges affected by carbonation corrosion. We propose a novel predictive model utilizing Bayesian networks, informed by the principles of failure physics and structural reliability. An algorithm was developed to generate a database for Bayesian network application, which also facilitated the establishment of a correlation to ascertain the bridge's lifespan. The efficacy of the proposed model was validated through a case study on an existing bridge, demonstrating congruence with prior research and underscoring its potential as a reliable predictive tool for infrastructure longevity.The proposed relationship based on the Bayesian network obtained the remaining useful life of the concrete bridge deck with the R-Squared error index (R2) equal to 0.97, which indicates the acceptable accuracy of the proposed method.
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Subjects


[1] Seyed Hosseini S.M., Baharshahi M., Shahanqi K. (2018). presenting a data-driven model for estimating the remaining useful life using the combination of turbofan sensor data, Journal of Industrial Engineering Research in Production Systems, 2018, serial 15. (In Persian)
[2] Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150-172.
[3] Kunche, S., Chen, C., & Pecht, M. (2012). A review of PHM system’s architectural frameworks. In: The 54th meeting of the society for machinery failure prevention technology, dayton.
[4] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical systems and signal processing, 104, 799-834.
[5] Zhang, J., Jiang, Y., Wu, S., Li, X., Luo, H., & Yin, S. (2022). Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliability Engineering & System Safety, 221, 108297.
[6] Medvedev, V., & Pustovgar, A. (2023). A Review of Concrete Carbonation and Approaches to Its Research under Irradiation. Buildings, 13(8), 1998.
[7] H.R. Fotso Lele, H. Beushausen, M.G. Alexander,2023, A practical carbonation model for service life design of reinforced concrete structures, Scientific African, Volume 20, e01677, ISSN 2468 -2276, https://doi.org/10.1016/j.sciaf.2023.e01677.
[8] Londhe, S., Kulkarni, P., Dixit, P., Silva, A., Neves, R., & de Brito, J. (2022). Tree based approaches for predicting concrete carbonation coefficient. Applied Sciences, 12(8), 3874.
[9] Shen, Y., Wang, Y., Xu, X., & Ruan, F. (2023). Study on carbonation of construction joints through field tests on a 30-year-old bridge and accelerated carbonation tests. Case Studies in Construction Materials, 19, e02231.
[10] Abichou, B., Flórez, D., Sayed-Mouchaweh, M., Toubakh, H., François, B., & Girard, N. (2014). Fault diagnosis methods for wind turbines health monitoring: a review. In: PHM Society European Conference (Vol. 2, No. 1).
 [11] Baur, M., Albertelli, P., & Monno, M. (2020). A review of prognostics and health management of machine tools. The International Journal of Advanced Manufacturing Technology, 107, 2843-2863.
[12] Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, 178, 255-268.
[13] Cai, B., Shao, X., Liu, Y., Kong, X., Wang, H., Xu, H., & Ge, W. (2019). Remaining useful life estimation of structure systems under the influence of multiple causes: Subsea pipelines as a case study. IEEE Transactions on Industrial Electronics, 67(7), 5737-5747.
[14] Lee, J., Wu, F., Zhao,W., Ghaffari, M. Liao, L.,Siegel, D. (2014).“Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications,” Mechanical Systems and Signal Processing, 42(1):314-334.
[15] Chun P.J., Inoue T., Seto D., Ohga M. (2012). Prediction of bridge deterioration using GIS-based Markov transition matrix, Internet Journal of Society for Social Management Systems, 8, 1–9.
[16] Morcous, G., Lounis, Z., & Cho, Y. (2010). An integrated system for bridge management using probabilistic and mechanistic deterioration models: Application to bridge decks. KSCE Journal of Civil Engineering, 14, 527-537.
[17] Ranjith, S., Setunge, S., Gravina, R., & Venkatesan, S. (2013). Deterioration prediction of timber bridge elements using the Markov chain. Journal of Performance of Constructed Facilities, 27(3), 319-325.
[18] Kosgodagan, A. (2017). High-dimensional dependence modelling using Bayesian networks for the degradation of civil infrastructures and other applications (Doctoral dissertation, Ecole nationale supérieure Mines-Télécom Atlantique).
[19] Kosgodagan‐Dalla Torre, A., Yeung, T. G., Morales‐Nápoles, O., Castanier, B., Maljaars, J., & Courage, W. (2017). A two‐dimension dynamic Bayesian network for large‐scale degradation modeling with an application to a bridges network. Computer‐Aided Civil and Infrastructure Engineering, 32(8), 641-656.
[20] Srikanth, I., & Arockiasamy, M. (2020). Deterioration models for prediction of remaining useful life of timber and concrete bridges: A review. Journal of traffic and transportation engineering, 7(2), 152-173.
[21] von Greve-Dierfeld, S., Lothenbach, B., Vollpracht, A., Wu, B., Huet, B., Andrade, C. & De Belie, N. (2020). Understanding the carbonation of concrete with supplementary cementitious materials: a critical review by RILEM TC 281-CCC. Materials and Structures, 53(6), 136.
[22] Wang, Xinhao, Qiuwei Yang, Xi Peng, and Fengjiang Qin. 2024. "A Review of Concrete Carbonation Depth Evaluation Models" Coatings 14, no. 4: 386. https://doi.org/10.3390/coatings14040386
[23] Li, B., Tian, Y., Zhang, G., Liu, Y., Feng, H., Jin, N. & Wang, J. (2023). Comparison of detection methods for carbonation depth of concrete. Scientific Reports, 13(1), 19980.
[24] Melchers, R. E., & Beck, A. T. (2018). Structural reliability analysis and prediction. John Wiley & sons.
[25] Guide Manual for the Seismic Vulnerability Assessment and Retrofit of Bridge. (2011). Publication No. 511, Vice President of Strategic Supervision Executive technical system office. (In Persian)
[26] Fang, W., Zhang, W., Ma, L., Wu, Y., Yan, K., Lu, H. & Yuan, B. (2023). An efficient Bayesian network structure learning algorithm based on structural information. Swarm and Evolutionary Computation, 76, 101224.
[27] Neapolitan, R. E. (2004). Learning Bayesian networks (Vol. 38). Upper Saddle River: Pearson Prentice Hall.
[28] Ann, K. Y., Pack, S. W., Hwang, J. P., Song, H. W., & Kim, S. H. (2010). Service life prediction of a concrete bridge structure subjected to carbonation. Construction and Building Materials, 24(8), 1494-1501.

  • Receive Date 05 April 2024
  • Revise Date 30 June 2024
  • Accept Date 24 July 2024