Abstract
One of the most common structural systems in earthquake prone areas are Reinforced Concrete (R/C) buildings with masonry infills. The observation of post-earthquake damages has led to conclusion that the masonry infills can greatly modify the seismic performance of these buildings. In the context of the direct assessment of the buildings’ seismic vulnerability, many researches have been conducted aiming to use the capacities of artificial intelligence, such as the Artificial Neural Networks (ANNs). The present study examines the influence of the infills’ irregular distribution on the seismic damage level of R/C buildings using Multilayer Feedforward Perceptron (MFP) ANNs. More specifically, a 5-storey R/C building with a large number of different masonry infills’ distributions possessing several degrees of irregularities is analyzed by means of Nonlinear Time History Analysis for 65 actual ground motions. The optimum configured and trained networks are applied for the rapid estimation of the damage of the examined building. The results of these applications show that the best configured and trained networks are capable to adequately estimate the damage of the R/C buildings with asymmetry caused by the irregular location of masonry infills.
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Kostinakis, K., Morfidis, K. (2020). Application of Artificial Neural Networks for the Assessment of the Seismic Damage of Buildings with Irregular Infills’ Distribution. In: Köber, D., De Stefano, M., Zembaty, Z. (eds) Seismic Behaviour and Design of Irregular and Complex Civil Structures III. Geotechnical, Geological and Earthquake Engineering, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-33532-8_23
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DOI: https://doi.org/10.1007/978-3-030-33532-8_23
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