Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models
International Journal of Structural Integrity
ISSN: 1757-9864
Article publication date: 31 August 2023
Abstract
Purpose
Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.
Design/methodology/approach
The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.
Findings
The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.
Originality/value
The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.
Keywords
Citation
Wani, F.M., Vemuri, J. and Chenna, R. (2023), "Prediction of storey drift for reinforced concrete structures subjected to pulse-like ground motions using machine learning classification models", International Journal of Structural Integrity, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJSI-06-2023-0054
Publisher
:Emerald Publishing Limited
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