Robustness and Convergence of a Stochastic Deterioration Prediction Model for Tunnel Lighting Facilities: A Japanese Case Study Based on Systematized Expert Knowledge and Large-Scale Monte Carlo Simulation

Much of Japan’s social infrastructure was concentrated during the period of rapid economic growth around the 1970s and is now facing the serious problem of simultaneous aging. To address this, a shift from conventional reactive maintenance to asset management utilizing various data is urgently needed. However, a critical barrier in practice is that inspection data predominately exists in analog paper formats or unstructured files, relying on subjective expert ratings (e.g., scale A to D). This lack of digital structure and the presence of “Assessor Bias” make it difficult to secure the robustness required for direct statistical analysis. To advance data analysis under these constraints, it is crucial to first verify whether a mathematical model can handle such uncertainty. This paper focuses on verifying the robustness and convergence of a deterioration prediction model designed to bridge this gap. Specifically, it incorporates the expert’s “judgment variation” into the model as probabilistic fluctuations (e.g., Beta-distributed noise) and uses large-scale Monte Carlo simulations to verify whether the model can produce stable results. This establishes a foundation for advancing future data analysis. Based on these verifications, we promote not only the “micro-segmentation of homogeneous statistical populations” but also the “micro-detailing of analytical models.” Such efforts are expected to drive the shift from qualitative methods reliant on human intuition to quantitative methods in modern society.