<p>Highway infrastructure, including
roads/pavements, contributes significantly to a country’s economic growth,
quality of life improvement, and negative environmental impacts. Hence, highway
agencies strive to make efficient and effective use of their limited funding to
maintain their pavement infrastructure in good structural and functional
conditions. This necessitates predicting pavement performance and scheduling
maintenance interventions accurately and reliably by using appropriate
performance modeling and maintenance optimization methodologies, while
considering the impact of influential variables and the uncertainty inherent in
pavement condition data.</p>
<p> </p>
<p>Despite the enormous research efforts
toward stochastic pavement performance modeling and maintenance optimization,
several research gaps still exist. Prior research has not provided a synthesis
of Markovian models and their associated methodologies that could assist
researchers and highway agencies in selecting the Markov methodology that is
appropriate for use with the data available to the agency. In addition, past
Markovian pavement performance models did not adequately account for the
marginal effects of the preventive maintenance (PM) treatments due to the lack
of historical PM data, resulting in potentially unreliable models. The primary
components of a Markov model are the transition probability matrix, number of
condition states (NCS), and length of duty cycle (LDC). Previous Markovian pavement performance
models were developed using NCS and LDC based on data availability, pavement
condition indicator and data collection frequency. However, the selection of
NCS and LDC should also be based on producing pavement performance models with
high levels of prediction accuracy. Prior stochastic pavement maintenance
optimization models account for the uncertainty of the budget allocated to
pavement preservation at the network level. Nevertheless, variables such as
pavement condition deterioration and improvement that are also associated with
uncertainty, were not included in stochastic optimization models due to the
expected large size of the optimization problem.</p><p>The overarching goal of this dissertation
is to contribute to filling these research gaps with a view to improving
pavement management systems, helping to predict probabilistic pavement
performance and schedule pavement preventive maintenance accurately and
reliably. This study reviews Markovian pavement performance models using
various Markov methodologies and transition probabilities estimation methods,
presents a critical analysis of the different aspects of Markovian models as
applied in the literature, reveals gaps in knowledge, and offers suggestions
for bridging those gaps. This dissertation develops a decision tree which could
be used by researchers and highway agencies to select appropriate Markov
methodologies to model pavement performance under different conditions of data
availability. The lack of consideration of pavement PM impacts into
probabilistic pavement performance models due to absence of historical PM data
may result in erroneous and often biased pavement condition predictions,
leading to non-optimal pavement maintenance decisions. Hence, this research
introduces and validates a hybrid approach to incorporate the impact of PM into
probabilistic pavement performance models when historical PM data are limited
or absent. The types of PM treatments and their times of application are
estimated using two approaches: (1) Analysis of the state of practice of
pavement maintenance through literature and expert surveys, and (2) Detection
of PM times from probabilistic pavement performance curves. Using a newly
developed optimization algorithm, the estimated times and types of PM
treatments are integrated into pavement condition data. A non-homogeneous
Markovian pavement performance model is developed by estimating the transition
probabilities of pavement condition using the ordered-probit method. The
developed hybrid approach and performance models are validated using cross-validation
with out-of-sample data and through surveys of subject matter experts in
pavement engineering and management. The results show that the hybrid approach
and models developed can predict probabilistic pavement condition incorporating
PM effects with an accuracy of 87%.</p><p>The key Markov chain methodologies,
namely, homogeneous, staged-homogeneous, non-homogeneous, semi- and hidden
Markov, have been used to develop stochastic pavement performance models. This
dissertation hypothesizes that the NCS and LDC significantly influence the
prediction accuracy of Markov models and that the nature of such influence
varies across the different Markov methodologies. As such, this study develops
and compares the Markovian pavement performance models using empirical data and
investigates the sensitivity of Markovian model prediction accuracy to the NCS
and LDC. The results indicate that the semi-Markov is generally statistically
superior to the homogeneous and staged-homogeneous Markov (except in a few
cases of NCS and LDC combinations) and that Markovian model prediction accuracy
is significantly sensitive to the NCS and LDC: an increase in NCS improves the
prediction accuracy until a certain NCS threshold after which the accuracy
decreases, plausibly due to data overfitting. In addition, an increase in LDC
improves the prediction accuracy when the NCS is small.</p><p>Scheduling pavement
maintenance at road network level without considering the uncertainty of
pavement condition deterioration and improvement over the long-term (typically,
pavement design life) likely results in mistiming maintenance applications and
less optimal decisions. Hence, this dissertation develops stochastic pavement
maintenance optimization models that account for the uncertainty of pavement
condition deterioration and improvement as well as the budget constraint. The
objectives of the stochastic optimization models are to minimize the overall
deterioration of road network condition while minimizing the total maintenance
cost of the road network over a 20-year planning horizon (typical pavement
design life). Multi-objective Genetic Algorithm (MOGA) is used because of its
robust search capabilities, which lead to global optimal solutions. In order to
reduce the number of combinations of solutions of stochastic MOGA models, three
approaches are proposed and applied: (1) using PM treatments that are most
commonly used by highway agencies, (2) clustering pavement sections based on
their ages, and (3) creating a filtering constraint that applies a rest period
after treatment applications. The results of the stochastic MOGA models show
that the Pareto optimal solutions change significantly when the uncertainty of
pavement condition deterioration and improvement is included.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12252716 |
| Date | 07 May 2020 |
| Creators | Mohamed S. Yamany (8803016) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Stochastic_Performance_and_Maintenance_Optimization_Models_for_Pavement_Infrastructure_Management/12252716 |
Page generated in 0.003 seconds