Abstract:
Machine learning has become a powerful tool in structural and earthquake engineering to accurately predict
structural design parameters; however, these complex algorithms are highly inexplainable. In this work, two
machine learning algorithms, i.e., Neural Network and eXtreme Gradient Boosting are used to predict the
fundamental period of vibration of masonry infilled reinforced concrete frames. The input parameters considered
for predicting the fundamental period of vibration of masonry infilled reinforced concrete frames are the number
of storeys, opening ratio, span length, number of spans, and masonry wall stiffness. The model predictions are
explained globally and locally, using the partial dependence plots, the accumulated local effects, and the game
theory-based approach of Shapely values. The performance of the trained machine learning models is compared
with expressions existing in the literature and building codes to predict the fundamental period of vibration of
masonry infilled reinforced concrete frames. Finally, the trained machine learning models and the existing
equation in the literature are used as surrogates to optimize the opening ratio and masonry wall stiffness of
buildings present in the database using the genetic algorithm approach. Therefore, the adopted methodology
results in a new database of buildings created together by machine learning and optimization algorithms, and the
methodology can be used to optimize the opening ratio and masonry wall stiffness to achieve a targeted
fundamental period. The dashboard for predicting and optimizing the fundamental period using the trained
machine learning models are also developed as a part of this study.
