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

Somala, Surendra Nadh and Karthikeyan, Karthika and Mangalathu, Sujith (2021) Time period estimation of masonry infilled RC frames using machine learning techniques. Structures, 34. pp. 1560-1566. ISSN 2352-0124

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Abstract

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. © 2021 Institution of Structural Engineers

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IITH Creators:

IITH Creators	ORCiD
Somala, Surendra Nadh	https://orcid.org/0000-0003-2663-3351

Item Type:

Article

Additional Information:

The authors thank the two anonymous reviewers for their comments and suggestions which has helped to improve the quality of the manuscript. This is a contribution of the Ministry of Earth Sciences project MoES/P.O.(Seismo)/1(304)/2016.

Uncontrolled Keywords:

Fundamental period; kNN; Machine learning; Neural network; Random forest; SHAP; XAI; XGBoost

Subjects:

Civil Engineering

Divisions:

Department of Civil Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

06 Oct 2022 09:46

Last Modified:

06 Oct 2022 09:46