Application of Machine Learning for Predicting Seismic Damage in Base-Isolated Reinforced Concrete Buildings

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived.