Remaining Service Life Evaluation of Reinforced Concrete Buildings Considering Failure Probability of Members
Source abstract
Abstract
Abstract The durability and structural performance of reinforced concrete (RC) structures decrease over time owing to various factors such as environmental deterioration and increased service loads. Therefore, as the service life of RC structures increases, it is important to derive objective and quantitative evaluation results on their durability and structural performance. However, most previous studies have analyzed only the individual impacts of multiple factors that reduce the durability of RC structures and have not considered the combined effects of these factors. In addition, the durability and structural performance evaluation methods for RC structures proposed by domestic and international institutions are based on the subjective judgments of experts in structural diagnostics, and the evaluation results are generally expressed as grades, posing significant limitations on the effective maintenance of RC structures. Therefore, this study conducted a detailed field investigation on the factors that reduce the durability of 21 RC structures. Based on field investigation data, a remaining service life evaluation model reflecting combined deterioration was developed using an adaptive neuro-fuzzy inference system. In addition, the structural reliability theory was introduced into the proposed model to reflect the failure probability of structural members and the importance of each member, block, and floor. An evaluation procedure was developed to objectively evaluate the safety level of RC structures by comprehensively considering both the durability and structural performance. The procedure is expected to be widely utilized in the field of structural safety diagnostics as it provides a quantitative estimate of the remaining service life.
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Research context
OpenAlex topics: Concrete Corrosion and Durability · Structural Behavior of Reinforced Concrete · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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