PhD Oral Exam - Jawad Mohammad Ta'amneh, Civil Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Pavement Management Systems (PMSs) rely heavily on indirect condition indices, such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), to guide decision making systems prioritize maintenance and rehabilitation (M&R) at the network level. However, these indices are not structural measures from which signs of the decay can be directly recognized; thus, their employment may cause a wrong timing of interventions and higher life-cycle costs. Although there is extensive research on Mechanistic-Empirical Pavement Design Guide (MEPDG) calibration and on PMS optimization, the incorporation of direct distress indicators from MEPDG and their performance within a decision support framework within PMS is missing and needs to be studied and evaluated.

This Thesis develops an approach that tightly links MEPDG-derived distress data which include rutting and cracking with PMSs decision-making. Three very different case studies have been implemented for the validation of the methodology. Firstly, in Alaska, distress datasets from 2020-2022 are used to calibrate rutting and cracking models by which more accurate forecasting and anticipatory M&R scheduling can be realized. Secondly, in Uganda, the integration is reformed by the use of AASHTO-based surrogates and a localized IRI progression model that is informed by regression and expert judgment, where limited structural inputs; a 30-year, budget-constrained program is optimized via linear programming resulting in realistic rehabilitation cycles and credible long-term projections. Lastly, the maturity of a network in Alberta is exploited for the comparison of IRI-triggered and distress-triggered regimens. The development of the distress-based approach finds out earlier identification of structural decline, improvement of predicted pavement conditions, and reduction of life-cycle costs.

Across the three different uses, the optimized PMS showed that it could be very effective in both condition control and resource allocation. The Alaska strategy that was optimized was a very good example as it resulted in more stable spending thus keeping the annual maintenance costs under $2.5 million while at the same time it managed to decrease the average rutting from 0.23 to 0.17 inches and also the cracking from 4.6% to ~3.5% over the 30-year horizon. Even though in Uganda there were some budgetary constraints that required front-loaded investments of more than $70 million in the first few years, the optimized PMS still greatly improved the technical outcomes, as the depth of the ruts was reduced from 9 mm to ~1.5 mm and the cracking from 12% to ~3%, thus, giving a better performance than the reactive strategies. The optimized-PMS performed better than the baseline IRI-based method in Alberta as it managed to lower the average network rutting from ~5 mm to ~3 inches by the end of the analysis period. Overall, the results that were found here demonstrate that the MEPDG-PMS integration has the capacity to significantly improve the accuracy of the predictions, to reduce the rates of the deterioration, and also to optimize the patterns of the expenditures in various regional contexts.

The main contributions encompass: (1) a framework that can be repeated which allows the raising of direct distress measurements to the main decision variables; (2) a clear method for the transfer of knowledge to situations with limited resources; and (3) research findings that strategies initiated by direct distresses perform better in terms of condition outcomes and use of costs than those solely based on indirect damage indicators. Such discoveries promote the progression of PMSs capabilities by facilitating the allocation of resources backed up by the evidence, the improvement of the resilience of the network, and sustainable pavement preservation across diverse data environments for different agencies.