Advancing deterioration prediction accuracy through wide-area data integration by infrastructure management authorities

Abstract

In Japan, bridge deterioration is progressing, and local governments are facing challenges such as a shortage of engineers and financial constraints. Therefore, it is necessary to make effective use of limited resources and determine repair priorities based on statistical deterioration prediction. In this study, we adopt the concept of regional infrastructure group management, specifically focusing on wide-area collaboration, which has gained attention in recent years. By integrating bridge inspection data from multiple management entities and applying a mixed Markov deterioration hazard model, we estimate the expected service life for each management entity. Furthermore, when the condition assessment criteria differ among management entities, data integration becomes challenging. To address this issue, we propose a hidden Markov deterioration hazard model that formulates the relationship between condition ratings before and after integration, thereby enabling data integration and achieving more accurate deterioration prediction.