Data-Driven Seismic Analysis of Structures Using Machine Learning

Abstract

Reinforced concrete shear walls are among the most important building structural components for supporting lateral loads. In spite of its significance, the inadequate safety margins of shear walls have been brought to light by post-earthquake reconnaissance and current experimental investigations. Current shear walls cannot have their failure modes quickly identified due to the absence of models based on mechanics and empirical data. To find out how shear walls fail depending on geometric configurations, material qualities, and reinforcing details, this research uses machine learning (ML), which has recently made some strides. Results from 395 experiments with shear walls of different geometric configurations make up the study's exhaustive database. In this research, the optimal prediction method was determined by evaluating eight machine learning methods, which included K Nearest Neighbours (KNN), Naive Bayes, Randomized Forest, XG Boost, Decision Tree, Ada Boost, Cat Boost and LightGBM. An exhaustive examination led to the proposal of a Random Forest based ML method in this research. When it comes to determining how shear walls break, the suggested approach is 87% accurate. According to the study, aspect ratios, bordering element reinforcement indices, and wall length to wall ratio of thickness are key factors in shear wall failure. Lastly, this research offers a data-driven categorization approach that is open-source and may be utilized by design firms worldwide. Additional experimental data that provide new insights may be easily included into the suggested method.