Modeling of Rocking Induced Permanent Settlement of Shallow Foundations Using Machine Learning Algorithms

The objective of this study is to develop data-driven predictive models for permanent settlement of rocking shallow foundations during seismic loading using multiple machine learning algorithms and supervised learning technique. Data from a rocking foundation database consisting of dynamic base shaking experiments conducted on centrifuges and shaking tables have been used for the development of k-nearest neighbors regression, support vector regression, and random forest regression models. Based on repeated k-fold cross validation tests of models and mean absolute percentage errors in their predictions, it is found that all three models perform better than a baseline multivariate linear regression model in terms of accuracy and variance in predictions. The average mean absolute errors in predictions of all three models are around 0.005 to 0.006, indicating that the rocking induced permanent settlement can be predicted within an average error limit of 0.5% to 0.6% of the width of the footing.