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Get Free AccessOver the past decades, the seismic vulnerability assessment and strengthening of existing reinforced concrete buildings with masonry infills have been topics of great interest for engineers worldwide. Often, such buildings have been designed disregarding lateral loads and the influence of masonry infills on the local and global response, resulting in severe structural damage or collapses in earthquakes. In the context of improving the seismic performance of old reinforced concrete framed buildings, this study draws attention to a seismic retrofit scheme based on cross-laminated timber panels. The effectiveness of the proposed retrofit method has been previously investigated by the authors through advanced numerical modelling. This paper presents the results from an experimental campaign comprising a series of strength tests on materials (e.g., diagonal compression tests on wallettes) and connections (e.g., timber-to-timber and timber-to-concrete connections) employed to realise the retrofit scheme in question. These tests were carried out to improve the predictive accuracy of the numerical models in simulating the coupled behaviour of old reinforced concrete elements, masonry infills, and retrofitting materials. The numerical models were further fine-tuned using data from cyclic quasi-static tests on four full-scale single-bay, single-storey frames. The numerical analysis results appear promising, showing that the proposed retrofit scheme considerably improves the seismic behaviour of reinforced concrete framed structures and that the numerical models can simulate the effect of the retrofit interventions accurately.
Francesco Smiroldo, Giovanni Sommacal, Stylianos Kallioras, Dionysios A. Bournas, Maurizio Piazza, Ivan Giongo (2023). Material characterisation for the numerical modelling of a timber-based seismic retrofit for RC buildings. , 44, DOI: https://doi.org/10.1016/j.prostr.2023.01.242.
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Type
Article
Year
2023
Authors
6
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.prostr.2023.01.242
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