Data-Driven Modeling of Seismic Energy Dissipation of Rocking Foundations Using Decision Tree-Based Ensemble Machine Learning Algorithms

The objective of this study is to develop data-driven predictive models for seismic energy dissipation of rocking shallow foundations during earthquake loading using decision tree-based ensemble machine learning algorithms and supervised learning technique. Data from a rocking foundation's database consisting of dynamic base shaking experiments conducted on centrifuges and shaking tables have been used for the development of a base decision tree regression (DTR) model and four ensemble models: bagging, random forest, adaptive boosting, and gradient boosting. Based on k-fold cross-validation tests of models and mean absolute percentage errors in predictions, it is found that the overall average accuracy of all four ensemble models is improved by about 25%-37% when compared to base DTR model. Among the four ensemble models, gradient boosting and adaptive boosting models perform better than the other two models in terms of accuracy and variance in predictions for the problem considered.