Classification of Seismic Vulnerability Based on Machine Learning Techniques for RC Frames

Abstract

Seismic vulnerability means the inability of historical and monumental buildings to withstand the effects of seismic forces. This article presents a classification model to specify the damage state of the Reinforced Concrete (RC) frames based on a collection of datasets from the damaged buildings in Bingol earthquake of Turkey for use in the learning process of the algorithm. The proposed model uses two classifiers including the redundancy and also the construction quality of the buildings to estimate the class of damage from four categories including none, light, moderate and severe. The available database of the considered earthquake includes the information of 27 damaged RC buildings which are published in the literature. The model provided a simple structure for engineers to predict the class without complex calculations in which it needs a few steps to determine the class of damage for RC frames. The results show that the presented model can estimate the class of each input vector with an acceptable error.

Keywords

• damage state
• reinforced concrete
• machine learning
• classification
• redundancy