Innovations in PMS — State of Ohio & Kingdom of Saudi Arabia

Abstract

Two different Pavement Management Systems (PMS) are discussed in this paper. One developed for the Ohio Department of Transportation (ODOT) has deterministic degradation models based on historical data. The ODOT system is optimized using a 0–1 optimization. This is solved using linear programming techniques including generalized upper bounding to considerably improve the computational efficiency. The second PMS discussed is part of a Highway Maintenance Management System developed for the Kingdom of Saudi Arabia that integrates a PMS, a Bridges & Structures Management System, and a Nonpavement Management System. It is a stochastic optimization system using initial prediction models partially based on expert opinion. The Saudi Arabian system is a Markovian based optimization that uses Lagrange methods to link together the various strata within the system. The use of Lagrange methods combined with parametric programming efficiently solves very large problems. An algorithm is presented for updating degradation models for pavements. Bayesian statistical procedures are given that automatically update the degradation models with new survey data. These procedures continually self-adjust the PMS to fit the specific conditions found in the network. This results in improved prediction models and a better utilization of resources.