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Understanding uncertainty : a reinforcement learning approach for project-level pavement management systems

University of British Columbia

Transportation agencies have limited fiscal resources to manage their pavement infrastructure. Planning for the future includes uncertainty, such as the uncertainty of future traffic levels, cost of rehabilitation actions, price indices, among others. Deterioration modeling also includes uncertainty, such as random and measurement uncertainty. Failing to consider these uncertainties may lead to sub-optimal management policies that are unable to adapt to the future. Thus, the objective of this thesis is to develop a reinforcement learning algorithm to manage pavement systems at the project-level that minimizes the life-cycle cost. The deterioration model developed uses an iterative-methods approach to estimate infrastructure performance models based on sampling theory. The model addresses the issue around measurement uncertainty underlying infrastructure condition assessments for continuous distress indicators and its effect on the parametric models underlying decision-support tools. Through a case study of pavement roughness data collected as part of Federal Highway Administration’s long-term pavement performance program, the new approach reduces the unexplained variance that would typically enter decision-support tools by 14%. It also addresses concerns around heteroscedasticity surrounding conventional methods, allowing modelers to recover efficiency in their statistical estimates. Finally, the Q-learning algorithm with an ε-greedy policy efficiently learns an optimal management policy for infrastructure assets while simultaneously incorporating several sources of uncertainty. An important advantage of this approach is that it is model-free and non-parametric, imposing no restrictions on the structure of the uncertain inputs. This study subsequently implements the Q-learning approach across three separate case studies. The proposed algorithm leads to the selection of a management policy that, on average, reduces expected life-cycle costs between 3% and 15% compared to traditional infrastructure management approaches. This research contributes to the pavement management literature by creating improved performance models and providing a holistic view of uncertainties in the management process. There are several opportunities to expand upon this research which are discussed.

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2020-04-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74184

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/