Multi-objective decision support system for large-scale network pavement maintenance and rehabilitation management to enhance sustainability

Process of sustainable pavement maintenance and rehabilitation (M&R) management can be conducted using various technological and organizational systems. Such systems must meet the implementation conditions of a given project involving pavement M&R, and trade-offs between economic, environmental, and social impacts from various aspects involved. Hence, this study examines a systematic framework for large-scale pavement network decision-making considering sustainability dimensions, constructs a multi-stage decision model considering different modes and options using attribute mapping techniques, and develops a decision support system that (1) determines comprehensive sustainability drivers and quantification methods in the field of pavement management to evaluate the sustainability characteristics of predetermined M&R strategies; and (2) allows the execution of decision space dimensionality reduction process for any given network scales and M&R techniques dimensions considering spatial-temporal distribution, then generate a specific set of sustainable budget allocation and M&R strategy combination scenarios using a redesigned multi-objective optimization model, which can coordinate segment-level and network-level management. A case study was conducted to demonstrate the application of the proposed system. Results show that the approach can significantly reduce the computational cost compared to the scenario without dimensionality reduction, and also facilitates the improvement of agency costs, environmental impacts, as well as user costs from the life cycle perspective. Furthermore, the balance of M&R budget allocation depends on the mapped attributes selected in the dimensionality reduction process, the sustainability and reliability of the decision results can be improved by selecting as many attributes as possible while satisfying the pavement management system. The study provides an exciting opportunity to support decision-making for large-scale transportation infrastructure management.