Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

Abstract

The damage state of a bridge has significant implications on the post-earthquake emergency traffic and recovery operations and is critical to identify the post-earthquake damage states without much delay. Currently, the damage states are identified either based on visual inspection or pre-determined fragility curves. Although these methodologies can provide useful information, the timely application of these methodologies for large scale regional damage assessments is often limited due to the manual or computational efforts. This paper proposes a methodology for the rapid damage state assessment (green, yellow, or red) of bridges utilizing the capabilities of machine learning techniques. Contrary to the existing methods, the proposed methodology accounts for bridge-specific attributes in the damage state assessment. The proposed methodology is demonstrated using two-span box-girder bridges in California. The prediction model is established using the training set, and the performance of the model is evaluated using the test set. It is noted that the machine learning algorithm called Random Forest provides better performance for the selected bridges, and its tagging accuracy ranges from 73% to 82% depending on the bridge configuration under consideration. The proposed methodology revealed that input parameters such as span length and reinforcement ratio in addition to the ground motion intensity parameter have a significant influence on the expected damage state.