Date of Award


Degree Name

Doctor of Philosophy


Civil Engineering


Carlos A. Chang-Albitres


Pavement management decision-making is the most important task for officials in transportation agencies, who develop maintenance and rehabilitation programs. Information about historical pavement network performance is needed for decision-making; pavement management systems (PMS) provide means to organize road network data to improve the pavement network condition. PMS have prediction performance models to forecast the future condition of the pavement networks with information required for decision-making; these models can be deterministic or probabilistic. Pavement performance deterministic models are commonly used in PMS, but they do not consider the uncertainty in forecasting pavement performance. Pavement performance depends on many random factors like traffic loads and environmental effects.

This research presents a stochastic approach to address the variability of the random factors involved in pavement performance prediction. The stochastic approach consists in two methods: probability-based performance curves and probabilistic performance-based scenarios considering different pavement deterioration rates over time. Data form PMS of the Metropolitan Transportation Commission (MTC) in the San Francisco, California Bay Area was used to develop the stochastic approach.

The new approach will aid transportation agencies to be aware of the possible performance scenarios which will affect treatment selection and budget needs estimate in the planning horizon for maintenance and rehabilitation programs.




Received from ProQuest

File Size

247 pages

File Format


Rights Holder

Rafael Arturo Ramirez Flores