期刊
ENGINEERING STRUCTURES
卷 216, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2020.110765
关键词
Fundamental period; Masonry infilled framed structures; Machine learning; Artificial neural networks; M5Rules; Nonlinear models
In this work, the estimation of the fundamental period of vibration of masonry infilled RC frame structures is achieved using both Machine Learning techniques and concise nonlinear formulas. The data used are extracted from a recently published extensive database that associates the period with relevant information, such as the height of the structure, the span length between columns, the wall opening ratio, and the masonry wall stiffness. It is shown that, as compared to the utilized data, the proposed methods produce excellent results at the cost of various levels of complexity.
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