Document Type : Original Article

Authors

• Shima Mahboubi
• Amirali Abdolmaleki

Faculty of Civil,Water and Environmental Engineering, Shahid Beheshti University,Tehran,Iran.

Abstract

Abstract— Performance-based design of bridges requires prediction of different damage states of components. (RC) piers are key components in bridge system, which may experience severe damages during earthquakes. Therefore, seismic damage assessment of RC bridges depends strongly on predicting failure modes RC piers. Using machine learning for damage evaluation of structures is becoming increasingly popular in earthquake engineering. This study implements three different machine learning techniques to capture different damage limit states of RC bridge piers under seismic loading. For this purpose, three machine learning techniques including K-Nearest Neighbors (KNN), Artificial Neural Networks (ANNs) and decision tree regressions were utilized for predicting four damage states of a RC bridge piers tested experimentally under seismic excitations based on drift limits. The efficiency of the three algorithms in damage prediction of RC piers were compared.

The efficiency of the three algorithms in damage prediction of RC piers were compared. The efficiency of the three algorithms in damage prediction of RC piers were compared.

Keywords

• Keywords— Machine learning algorithm
• RC Bridge pier
• Damage states
• Seismic performance

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing