Damage predicting algorithms for regular RC structures
In this paper a 2-phase decision tree algorithm is developed to qualitatively predict damage in RC buildings based on earthquake characteristics and structural properties. To this end, the structural properties considered are the natural period of the fundamental mode, the ductility, and the normalized yielding strength. The earthquake characteristics, on the other hand, are the surface magnitude, site-to-source distance, and the peak ground acceleration at the building’s site. Reinforced concrete buildings are modelled as single-degree-of-freedom systems and various time-history nonlinear analyses are performed to create a dataset of damage indices. Subsequently, two decision trees are trained using the qualitative interpretations of those indices. The first decision tree determines whether damage occurs in an RC building. Consequently, the second decision tree predicts the severity of damage as repairable, beyond repair, or collapse.