Time period estimation of masonry infilled RC frames using machine learning techniques. (December 2021)

• Record Type: Journal Article
• Authors: Somala, Surendra Nadh; Karthikeyan, Karthika; Mangalathu, Sujith
• Is Part Of: Structures. Volume 34(2021)
• Page Start: 1560
• Page End: 1566
• Publication Date: 2021-12
• Subjects: Fundamental period -- Machine learning -- Random forest -- XGBoost -- kNN -- Neural network -- SHAP -- XAI; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.08.088
• Languages: English
• ISSNs: 2352-0124

Abstract

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.