Social Impact Assessment of Infrastructure Maintenance Based on Stochastic Deterioration Prediction: Minimizing Public Health Risks and Deriving Pareto Optimal Solutions

The aging of social infrastructure, intensively constructed during periods of rapid economic growth, is a pressing challenge facing modern society. Conventional infrastructure asset management has disproportionately emphasized a “managerial financial perspective,” aiming to maintain physical functions within limited budgets. However, the malfunction of road appurtenances such as tunnel lighting facilities induces severe traffic accidents and chronic congestion, resulting in public health risks for users (physical trauma, psychological stress, and the deterioration of Disability-Adjusted Life Years: DALYs) as well as massive socio-economic losses. The primary novelty of this study lies in bridging the gap between stochastic engineering deterioration models—specifically, discrete-time Markov chain models predicting physical degradation—and socio-economic stakeholder value chains. This study constructs a “Social Life Cycle Cost (LCC) Optimization Model” that directly incorporates these social losses and stakeholder risk disparities into the evaluation function, addressing the limitations of conventional financial-centric LCC models. By conducting robust uncertainty and global sensitivity analyses via large-scale Markov Chain Monte Carlo simulations (number of trials N=105), we reveal that a corrective maintenance strategy inheres a critical “fat-tail risk” of stochastically incurring catastrophic social losses. Conversely, preventive intervention at State C minimizes the expected total cost with statistical significance (p<0.001) and drastically decouples engineering costs from social risks. This research provides quantitative evidence that early infrastructure intervention functions as an indispensable “social investment” for mitigating public health risks under the specific parameters of the proposed model.