Time period estimation of masonry infilled RC frames using machine learning techniques

The accurate estimation of the fundamental time period is critical for the error-free risk and reliability estimation of infrastructure systems. Although complex empirical models are available in the literature, this paper estimates the application of machine learning approaches for the time period estimation. Recently, a good database of masonry-infilled RC frames and their fundamental period exist in literature and preliminary approaches like artificial neural networks have been tried out on them. In this work, we use advanced machine learning algorithms based on bagging and boosting approaches, and the comparison of our results with those already published shows that these methods can outperform the existing ones. The contribution of each variable to the fundamental time period is explained locally and globally using Shapely Additive Explanations.

Automated summary

This paper explores the use of machine learning algorithms for accurate estimation of fundamental time period in infrastructure systems, demonstrating superior performance compared to existing methods through advanced techniques such as bagging and boosting.