Multi-Criteria Decision-Making for Urban Bridge Assessment Using Fuzzy MARCOS Under Environmental Stressors

Abstract

This paper introduces a novel decision-making framework based on the Fuzzy MARCOS method for prioritizing maintenance of urban bridges under environmental stressors. Using a real-world SHM time-series dataset from Kaggle, four key criteria—degradation score, forecasted condition score, humidity, and wind speed—were analyzed. Fuzzy triangular weights were applied to capture uncertainty, and MARCOS scores were computed for each bridge. Bridge 23 achieved the highest score (0.926), while Bridge 36 ranked lowest (0.317). A strong positive correlation (R = 0.84) between MARCOS scores and forecasted condition confirms the model’s reliability. Sensitivity analysis on humidity weight showed minimal impact on ranking, indicating robustness. The approach enables scalable, interpretable, and sensor-driven maintenance planning, with potential for future expansion to include real-time streaming, traffic loads, and image-based diagnostics.