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Impacts of Different Deterioration Processes on Structural Time-dependent Reliability via Dynamic Bayesian Networks | ||
| AUT Journal of Civil Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 09 دی 1404 اصل مقاله (1.22 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2025.23682.5895 | ||
| نویسندگان | ||
| Pouyan Zargar1؛ Azad Yazdani* 1؛ Mohammad-Rashid Salimi1؛ Mohammad-Sadegh Shahidzadeh2 | ||
| 1Department of Civil Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran | ||
| چکیده | ||
| Engineering structures are typically subjected to time-dependent deterioration processes, such as corrosion, fatigue, and carbonation, which gradually reduce their service life and reliability. This study investigates the time-dependent reliability of structures under different deterioration mechanisms using Dynamic Bayesian Networks (DBNs). This analysis has the potential to significantly influence future decisions about the structure's usage. Three deterioration models: deterministic, stochastic, and Gamma process are implemented to represent distinct degradation behaviors. The methodology involves discretizing the resistance variable in DBN and comparing reliability indices obtained from DBN and Monte Carlo simulation (MCS) to validate the approach. The DBN results are validated against Monte-Carlo simulations, showing a maximum discrepancy of 3%, as well as providing standard deviation (0.0211) and root-mean-square error (0.023) of differences that demonstrates the DBN approach's validity and precision. This paper calculates the time-dependent reliability of a portal frame structure experiencing resistance deterioration, influenced by various deterioration models. Finally, it presents a comparison of the results from time-dependent reliability analysis utilizing various deterioration processes. Among the models, the Gamma process yields the highest reliability index over a 40-year period, while deterministic and stochastic models exhibit slightly lower reliability. Estimates derived from measurements are more realistic than those based on design values. The findings demonstrate the capability of DBN to incorporate measurement evidence, providing a robust basis for lifetime reliability assessment and maintenance planning of deteriorating structures also DBN effectively captures deterioration effects and probabilistic uncertainty over time, offering a computationally and time-efficient alternative to Monte-Carlo simulations. | ||
| کلیدواژهها | ||
| Deterioration؛ Deterministic and stochastic process؛ Time-dependent reliability؛ Gamma distribution؛ Dynamic Bayesian Networks | ||
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