Application of ANN for Estimating Time-Variant Structural Reliability of Reinforced Concrete Structures Using Approximate Approach
Publication: Practice Periodical on Structural Design and Construction
Volume 29, Issue 2
Abstract
Deterioration of RC structures with the passage of time due to aggressive environment leads to reduction of strength, stiffness, and reliability of the structure. Generally, for assessing reliability or failure probability of aging structures that are time-dependent, one must consider the uncertainty in the structural degradation in association with nonstationarity in the load distribution process. In this paper, an approximate approach has been used for evaluating the effects of structural degradation and live-load variations on time-dependent failure probability of aging structures. In order to assess the time-dependent failure probability, the service life of the structure is considered to be 50 years. Due to heavy computational requirements that need a long and tedious completion process, soft computing, e.g., artificial neural networks (ANNs), have been used, which act as a physical system for rapid prediction of the structural reliability with reasonable accuracy. The predicted outputs obtained from the neural network model were validated with the actual outputs and were found to yield good results, making it suitable for estimating long-term time-variant behavior of aged structures under a significant loading process.
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Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Author contributions: Abhijeet Dey: study conception and design; material preparation; model building; analysis; writing–first draft; review–first draft; and approval–final manuscript. Arjun Sil: study conception and design; material preparation; model building; analysis; review–first draft; and approval–final manuscript.
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Published In
Practice Periodical on Structural Design and Construction
Volume 29 • Issue 2 • May 2024
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© 2024 American Society of Civil Engineers.
History
Received: Jul 5, 2023
Accepted: Nov 29, 2023
Published online: Feb 5, 2024
Published in print: May 1, 2024
Discussion open until: Jul 5, 2024
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