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• W4362678082 abstract "Quantification of seismic damage to reinforced concrete (RC) members in terms of damages states is important for condition assessment and performance-based seismic design. This paper presents a classification for axially-loaded flexural-dominant well-detailed RC members into clearly-differentiated damage states based on change in failure mode, principles of mechanics, and loss in lateral load capacity. The paper considers extreme fiber compressive strain as the Engineering Response Parameter (ERP) for classification of the damage states. The identification of member damage states based on compressive strain can be very useful for seismic assessment of the existing buildings and performance-based design of new buildings. Published experimentally observed cyclic force–displacement relationships of RC columns have been numerically simulated and are used to develop statistics of compressive strain. These are further used to develop semi-empirical expressions for extreme fiber compressive strain in terms of RC member properties, viz. axial load ratio, longitudinal reinforcement ratio, and confinement reinforcement ratio. It is shown that the proposed expressions accurately predict experimentally observed compressive strain at different damage states. The fragility functions of damage states, that provide the probability of occurrence of the strain limits, are also presented. It is seen that the proposed expressions for strain and their limits accurately identify the damage states on the monotonic stress–strain relationship of confined concrete and validate the use of strain as an ERP. The present study also provides strain limits for different damage states with consistent level of conservatism." @default.
• W4362678082 created "2023-04-07" @default.
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• W4362678082 date "2023-04-07" @default.
• W4362678082 modified "2023-09-30" @default.
• W4362678082 title "Seismic damage classification for axially-loaded well-detailed reinforced concrete frame members based on compressive strain" @default.
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• W4362678082 doi "https://doi.org/10.1007/s10518-023-01681-3" @default.
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