A metamodel framework for selecting transportation asset management decision-support tools

Decision-support tools are fundamental to planning road and highway infrastructure maintenance, guiding treatment interventions and their timing. Despite the development of numerous complex decision-support tools, public sector agencies frequently hesitate to adopt them. This paper presents a metamodel framework designed to bridge this gap by matching transportation asset management decision-support tools to specific decision-making contexts or scenarios, thereby promoting use of the most appropriate tools. The framework uses machine learning methods to characterise scenarios and select the most suitable model. The framework is applied to three distinct decision-support tools to determine maintenance policies for pavements and bridges: a comprehensive tool that minimises total costs (agency, user, and disruption) over a planning horizon using Monte Carlo simulation (in which random sampling is used to represent different condition states) and network equilibrium, a simplified fixed policy tool, and a tool of intermediate complexity. Trained and validated using pseudo-data from 400 scenarios across 10 simulated networks, and further tested on a realistic network, the metamodel classifier accurately predicts the most preferred decision-support tool for previously unseen scenarios.