Estimation the deterioration of the entire tunnel light luminaires based on the deterioration of the front part

Abstract

In this study, an AI model was constructed to estimate the degree of deterioration of other parts of a tunnel light fixture using the degree of deterioration of the front part and installation environmental factors. Using inspection data from approximately 47,000 lamps in 94 tunnels throughout Shikoku, we constructed four models using a multilayer neural network for each part, which showed significantly better estimation accuracy than naïve estimation. In particular, a high accuracy rate and low misjudgment of the dangerous side were confirmed for hinges, latches, and CR fittings, which are expected to contribute to labor saving in inspection work and improvement of safety. On the other hand, the accuracy of the CR itself remains a problem, and additional input factors and improvements to the model structure are needed in the future.