Incorporating Life Cycle Science into Asphalt Pavement Maintenance Decision Making

In last few years, a great progress has been achieved in the highway development in China. Consequently, on one hand, the highway network scale expands rapidly which brings a huge highway maintenance task. On another hand, the available maintenance fund is limited; meanwhile, the policy of energy conservation and emission reduction is implemented effectively, and as a result, the requirements both on maintenance efficiency and environmental impact are relatively high. Under this background, this research focused on the asphalt pavement maintenance decision making methodology based on the life cycle science consisting of life cycle assessment (LCA) and life cycle cost analysis (LCCA). The pavement performance as well as life cycle cost and environmental impact were taken into consideration when the decision making index system was built, including benefit cost ratio (BCR), benefit energy consumption ratio (BER), and benefit greenhouse gas emission (GHG) ratio (BGR). The decision making models of project-level and network-level pavement maintenance as well as the optimal maintenance timing were developed. Finally, the decision making and optimization methods were applied in factual highway maintenance projects in Jiangsu province with case studies. Research results show that, considering the cost and environmental impacts of road users, when milling and repaving (M&R) and hot in-place recycling (HIR) are compared as for maintenance measures selection, the benefit is associated with traffic volume. M&R has higher BCR, BER, and BGR than HIR at a low traffic condition, but when the traffic grows to a certain degree, the HIR has a higher BCR instead. There exists an optimal maintenance trigger value where the comprehensive benefit is the most. For the maintenance timing optimization, the maintenance triggered level should be improved to keep the pavement a good performance level at a high traffic condition, so that the life cycle cost can be reduced as well as energy consumption and GHG emission. The multi-purpose optimization method for network can be used to solve the maintenance decision making problems under some constraints.