Incorporating uncertainty into life-cycle sustainability assessment of pavement alternatives

Current life-cycle sustainability assessment (LCSA) methodology for pavement alternatives is deterministic, which generally reduces the reliability of the results. This study proposed an uncertainty-based LCSA framework for pavement alternatives to capture the uncertainties associated with economic, environmental, and social pillars during the life cycle. After identifying and characterizing the uncertainties, the probabilistic LCSA results associated with eight impact categories were obtained using Monte Carlo simulation. Subsequently, an improved multi-criteria decision-making approach was presented to unify the random outcomes of the eight impact categories, where the probabilistic integrated sustainability degrees (ISDs) were obtained to represent the sustainability level for the alternatives. Meanwhile, the probability of being best option for each alternative was obtained. A case study was applied to perform the uncertainty analysis of LCSA including hot mix asphalt (HMA) pavement, warm mix asphalt (WMA) pavement, and reclaimed asphalt pavement (RAP). The results showed the economic performance of RAP alternative was significantly less than that of HMA and WMA alternatives at confidence levels of 97% and 99%, respectively. Moreover, there were no statistically significant differences among three alternatives with respect to relevant environmental impact categories. Three pavement alternatives denoted positive social impacts with 95% confidence interval. In addition, the satisfactory ISD was obtained for RAP alternative with 95% confidence interval, while RAP alternative was selected as the best option with a high probability of 91.71%. In addition, it was found that the ISDs were sensitive to the economic and social weights in the case study. This study extends the existing pavement LCSA framework by capturing the multivariate potential uncertainties, and thus represents a valuable, reliable tool for decision makers. (C) 2020 Elsevier Ltd. All rights reserved.