Data-Driven Asset Management for Highway Sign Support Systems: A Case Study of Prioritizing Repair and Replacement Decisions

Road sign support systems are not usually well managed because bridges and pavement have budget and maintenance priority while the sign boards and sign supports are considered as miscellaneous items. The authors of this paper developed a simple deterioration prediction model for sign support systems. Asset risk analysis was performed to develop the repair priority list. The data were collected from the Connecticut Department of Transportation (CTDOT) asset management database. There are many advanced and complicated deterioration prediction models; however, authors adopted the Weibull function for the deterioration prediction curve because it is a proven method used in reliability theory and a simple probability function that fits for simple structures like sign supports. Asset risks and repair priority lists were developed using the probability of failure and commuter impacts that are functions of ages, structure types and materials, number of traffic, population, etc. This paper shows the complete cycle of infrastructure asset management, starting from asset condition database management, deterioration prediction modeling, risk analysis, and repair priority decision-making.