Machine learning for the prediction of evaluation of existing reinforced concrete structures performance against earthquakes

Abstract

Earthquakes are challenging disasters that pose a huge threat to the urbanized world. In particular, the majority of the existing reinforced concrete (RC) building stock in developing countries such as Turkey is under huge seismic risk. These structures are at risk of partial or complete collapse under the effects of strong ground motions, due to some deficiencies in the structures. Therefore, seismic evaluation of existing buildings with a predominantly RC structural system is vital to reduce the potential seismic risk. In this study, machine learning (ML) techniques have been used for the prediction of the existing RC buildings’ performance against earthquake. The k-fold cross-validation has been employed to check the accuracy of the ML techniques. Random Forest (RF) provided the highest performance among the other ML techniques used. Sensitivity analysis have also been performed to determine the most significant factors in the prediction of the performance of the buildings. The results show that the building age, concrete compression strength, maximum column stirrup distance, steel yield strength, and the existence of corrosion have a high impact on the assessment of building performance.