Development of a streamlined framework for probabilistic and comparative life cycle assessment of road pavements

Abstract

This study presents a novel framework to address the challenges of extensive data requirements and uncertainties in road pavement life cycle assessment (LCA). We introduce a streamlined framework that employs a structured data underspecification approach. In this study, we categorized the required data into four specificity levels (M1 to M4) to manage data uncertainty in a systematic way. Using 10 case studies with various data specificity levels, our method proves effective in adapting to different degrees of data scarcity. The proposed framework supports a confident decision-making process, in which the comparison indicator shows discerned results at least 90 % of Monte Carlo runs by reducing the data collection burden by 85.7 %. For example, in the Boston pavement scenarios, one can achieve a 90 % reliability level by selectively specifying the maintenance and repair (M&R) activities to an M3-level specificity, while maintaining all other data at a lower level of detail, i.e., M2.