Ex-Post Evaluation of the Effects of Simple Proactive Repairs with a Deterioration Prediction Model on Steel and Concrete Bridges Considering Sample Dropping Bias

During periodic inspections of bridges, the inspector’s first-aid works are sometimes implemented to improve safety and enhance preventative maintenance. It is important to evaluate the effectiveness of the inspector’s first-aid works, but since these works are implemented for damage with relatively high deterioration rates, inspection data samples with such damage that did not receive the inspector’s first-aid works cannot be obtained, which results in sample dropping bias in the data. In this study, this problem is solved by using a deterioration prediction model that takes into account the dropped samples. Then, the effect of the inspector’s first-aid works is quantitatively evaluated after incorporating a deterioration control effect of inhibiting the progression of deterioration by comparing the deterioration processes of when the inspector’s first-aid works are implemented and when they are not. Lastly, through an application case study using visual inspection data of an actual highway viaduct, the effect of the inspector’s first-aid works on inhibiting deterioration is quantitatively evaluated, and the timing of implementation of the inspector’s first-aid works is discussed.