Browsing by Subject "Pavements--Deterioration"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Modeling heterogeneity in transportation infrastructure deterioration(2007-05) Hong, Feng, 1977-; Prozzi, Jorge AlbertoOne of the key elements for managing transportation infrastructure is to accurately capture and predict the performance of the facility through well established deterioration models. A sound deterioration model should incorporate 1) physical principle that reflects the deterioration mechanism; 2) relevant variables affecting the deterioration process; and 3) rigorous statistical approach to estimating the model. This dissertation aims at addressing these critical issues with focus on highway pavements. Data collected from in-service pavement sections are adopted to capture the real-world pavement deterioration process. A widely used pavement performance indicator, riding quality in terms of International Roughness Index (IRI) is used. A nonlinear model with a hierarchical parameter structure is formulated to effectively account for both observed and unobserved heterogeneity. The model is estimated through an econometric technique, Maximum Simulated Likelihood estimation. Simulation is employed to solve the computationally challenging problem of multi-dimensional integration. Engineering implications based on estimation results are discussed. The findings are not only consistent with engineering judgment but also helpful to reveal and enhance understanding of the pavement deterioration mechanism. Furthermore, the proposed methodology provides flexibility to obtain both parameters reflecting deterioration for all units and each individual unit of the population. The second part of the dissertation establishes and evaluates optimal maintenance policy on the basis of realistic deterioration models. The optimal policy is obtained so that the total cost, agency plus user cost, is minimized. A steady state resurfacing problem is investigated in the case study. In particular, the effect of model accuracy related to unobserved heterogeneity on total cost is discussed. This study makes a contribution to transportation infrastructure management and design in the following sense. From a management viewpoint, the proposed methodology with hierarchical parameters can accommodate both network and project levels of management. It also facilitates decision making for budget planning and resource allocation. From a design viewpoint, model estimation results can be used to update the current AASHTO pavement design equation by incorporating other critical factors.Item Modeling heterogeneity in transportation infrastructure deterioration: application to pavement(2007) Hong, Feng; Prozzi, Jorge A.Item A probabilistic and adaptive approach to modeling performance of pavement infrastructure(2005) Li, Zheng; Zhang, Zhanmin, 1962-Accurate prediction of pavement performance is critical to pavement management agencies. Reliable and accurate predictions of pavement infrastructure performance can save significant amounts of money for pavement infrastructure management agencies through better planning, maintenance, and rehabilitation activities. Pavement infrastructure deterioration is a dynamic, complicated, and stochastic process with its outcome as the aggregated impact from various factors such as traffic loading, environmental condition, structural capacities, and some unobserved factors. However, existing performance prediction models are still constrained by inadequate consideration of the dynamic and stochastic characteristics of pavement infrastructure deterioration. The goal of this research is to develop a probabilistic and adaptive methodological framework that is capable of capturing the dynamic and stochastic nature of pavement deterioration processes. The ordered probit model and the sequential logit model as probabilistic models are proposed to directly predict the performance of pavements in terms of their condition states by relating the performance to the structural, traffic, and environmental variables. The proposed probabilistic models were pilot-tested with pavement performance data collected during the AASHO Road Test, yielding promising preliminary results. In addition, these models were further enhanced as mechanistic-empirical models by incorporating certain primary response variables of pavements as explanatory variables. The comparison results show that the proposed models yield better predictions than the previously developed models. Then, a structural state space model is proposed to characterize the dynamic nature of pavement deterioration. The structural model allows the prediction of pavement deterioration to be adaptively updated with new inspection data, taking advantage of a polynomial trend filter and the Kalman filter algorithm. The preliminary results from a simulation case study indicate that the adaptive algorithm is robust and responsive to structural deviations of the pavement deterioration process.