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Title: Machine Learning Driven Drift Capacity Model for Reinforced Concrete Walls

Author(s): Muneera Aladsani, Henry Burton, Saman Abdullah, and John Wallace

Publication: Symposium Paper

Volume: 350


Appears on pages(s): 16-26

Keywords: reinforced concrete walls, special boundary elements, drift capacity, machine learning, extreme gradient boosting, artificial intelligence

DOI: 10.14359/51734309

Date: 11/1/2021

Many modeling approaches in engineering are based on physical principles. The input and output relationships are developed using physical laws (e.g., Newton's laws of motion and conservation of mass and energy). However, in many situations, the development of physically-based models requires simplifying assumptions due to the complicated nature of the systems, which could lead to a large degree of uncertainty. In these situations, data can be used to formulate models by detecting relationships between the system’s variables (inputs and outputs) without explicitly knowing the physical behavior of the system. Therefore, there is a paradigm shift from physically-based models to data-driven models. The objective of this study is to develop a drift capacity prediction model for structural walls with special boundary elements using the extreme gradient boosting (XGBoost) machine learning algorithm. The resulting prediction model is compared with the recently developed empirical model presented in literature i.e., the Abdullah & Wallace (2019) model. The results reveal the proposed model’s superior predictive capabilities relative to the empirical model.