<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>

This thesis implements the empirical pavement design procedures for flexible as well as rigid pavement by American Association of State Highways and Transportation Officials (AASHTO) into two MATLAB modules of MULTI-PAVE. MULTI-PAVE was developed as a teaching tool that performs pavement thickness design for multiple design procedures using a common input file and a common output format. The AASHTO components were developed in accordance with the 1993 AASHTO Pavement Design Guide, and verified against the original design method. The thicknesses of the Asphalt Concrete, Base Course and Sub-base Course are the design outputs for flexible pavement. For rigid pavement, the thickness of slab is determined for various types of concrete pavements. The modules will be included in a MULTI-PAVE framework to compare the design outputs with other design methods.

The Mechanistic-Empirical Pavement Design Guide (MEPDG) which is widely used for the rational design of pavements has three different design levels (i.e., Level 1, Level 2, and Level 3) that are typically based on the resources and the level of risk associated for a given project. Specifically, Level 2 design requires the estimation of the resilient modulus, MR (which is the key parameter in the mechanistic design procedures) from simple experiments such as the California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS) and R-value tests. In this study, a technique is proposed for estimation of MR from CBR that can be used in Level 2 designs of pavements. The California Bearing Ratio (CBR) is a relatively inexpensive laboratory test which provides a measure of the strength of a soil. The CBR test can easily be performed as the experimental procedure is relatively straightforward to execute. The CBR test procedure widely used and is simple, however the fundamental engineering principles governing CBR tests do not realistically describe the mechanical behavior of pavements. Due to this reason, there has been a significant interest to design pavements using a mechanistic approach such as the resilient modulus (MR). The MR test method provides an indication of the stiffness of pavement materials under cyclic loads, which closely represents the typical loading conditions that are experienced by pavements. MR is a reliable method as it considers the cyclic loading (i.e., resilient response) of pavements. However, it has one major drawback as the triaxial testing equipment used for measurement of the MR is relatively costly, testing is complex and requires trained professional to perform them. The CBR and MR are both used in present day practice to evaluate the strength of pavement materials. However, the CBR is widely used because of its relatively low cost and the vast experience with its use in the design of pavements. The common trend in today’s practice is to estimate the MR from CBR as evident in most pavement design procedures used around the world. For instance, the Mechanistic-Empirical Pavement Design Guide (AASHTO 2008) suggests that the MR may be estimated from standard tests like the CBR for design of Level 2 pavements. Numerous studies in the literature propose relationships between CBR and MR, but only a hand full of these studies takes account of the effect of matric suction, 𝜓 which is a key stress state variable that describes the rational behavior of unsaturated soils. This thesis document includes the explanation of a modified CBR test equipment capable of measuring unsaturated properties (𝜓 and water content) of specimens subjected to wetting and drying. In addition, some correlations were developed using the measured CBR data and the data of MR from other studies. The results provide useful information for Level 2 mechanistic-empirical design of pavement structures for various soils in the province of Ontario.

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The purpose of this research was to determine the effect of full-depth reclamation (FDR) on the strength and durability of aggregate base layers in a coordinated approach involving both field and laboratory testing. Field comparisons between the pre-reclamation neat base and post-reclamation blended base were supplemented with laboratory experiments conducted to determine the effects of reclaimed asphalt pavement (RAP) content, compaction effort, and heating on the strength and durability of roadways reconstructed using FDR with a portable asphalt recycling machine (PARM). Also, the effect of reclamation on the spatial uniformity of the pavement structures was explored by comparing variability in the pre- and post-reclamation material properties. Test sites in Orem, Utah; San Marcos, Texas; and South Jordan, Utah, were selected for this research. The results of field testing indicate that the FDR process significantly increased the stiffness and/or strength of the base material at two of the test locations and did not significantly change the third base material. An evaluation of spatial variability indicated that the FDR process produced equivalent or lower spatial variability with respect to both base modulus and California bearing ratio (CBR) values at one site, while the other two sites exhibited equivalent or higher spatial variability after FDR. The results of laboratory testing for all three locations indicate that specimens compacted using the modified Proctor method exhibit significantly higher CBR values and dry densities than specimens compacted using the standard Proctor method. Also, the CBR values for specimens tested in the dry condition were significantly higher than those obtained from specimens tested at optimum moisture content. These results demonstrate the value of achieving a high level of compaction during construction and preventing water ingress into the pavement over time. The blended material exhibited a significantly lower CBR value than that of the neat material at only one location; the addition of RAP to materials at the other locations did not significantly change the CBR values of those materials. In the tube suction test (TST), most of the specimens were classified as marginally or highly moisture-susceptible, and the effect of RAP on the dielectric value in the TST was of no practical importance. The use of PARMs in the FDR process is an acceptable, economical, and environmentally friendly approach to reconstruction of flexible pavements. To ensure satisfactory performance of FDR projects, engineers and managers should carefully follow recommended guidelines for project selection, pavement testing, material characterization, design, construction, and quality assurance testing.

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No description available.

This master thesis deals with the pavement management system as a tool for a management and maintenance of the roads in the Liberec region. The aim is to collect road failures within the network pavement management system level on roads of 2nd and 3rd class. The resulting data will be used for evaluate pavement condition and make plans of pavement maintenance and rehabilitation in variants. Furthermore, the thesis deals with individual variants in the process of data evaluation when evaluating their suitability and accuracy. The theoretical part summarises the information needed to understand the functioning of the road management system, as well as a description of the software used to collect and subsequent work with the data. In the practical part are presented results of the thesis and moreover the questions arising from the goals set are answered.

Master‘s thesis deals with pavement management system applied on roads of 2nd and 3rd class in region of Hradec Králové. Its task is to collect failures of pavement, evaluate pavement condition and make plans of pavement maintenance and rehabilitation in variants. One of tasks is also to evaluate attitude of pavement management system in regional level, but also to evaluate the contributions and weaknesses of systematic steps and attitude of access of stakeholders.

The research described herein is composed of four major areas of practice. It examines the overall performance of runways and provides tools designed to improve current runway operations and management with particular emphasis on contaminated surfaces. Presented in this thesis is an overview of how to design airport pavements in order to achieve optimal friction by specifically focusing on material selection and construction techniques for rigid and flexible pavements. Rubber buildup and the impact rubber accumulation has on decreasing runway friction, particularly in a range of climatic conditions, is discussed. Four commonly used rubber removal techniques are presented and evaluated. Through this research, an analytical hierarchy process (AHP) decision making protocol was developed for incorporation into airport pavement management systems (APMS). Runway surface condition reporting practices used at the Region of Waterloo International Airport are evaluated and recommendations for improving current practices are identified. Runway surface condition reporting can be improved by removing subjectivity, reporting conditions to pilots in real time, standardizing terminology and measurement techniques, and including runway pictures or sketches to identify contaminant locations where possible. Reports should be incorporated and stored in the APMS. Aircraft braking systems and their effects on landing distances under contaminated conditions are discussed. This thesis presents a proposed solution for monitoring and measuring contaminated runway surfaces and identifying the risks associated with aircraft landing through using the Braking Availability Tester (BAT). Also proposed in this thesis is a testing framework for validating the Braking Availability Tester. The proposed BAT measures interaction between aircraft antiskid braking systems and runway contaminants to determine landing distances more accurately. Finally, this thesis includes a discussion explaining how pavement design, contaminant removal, results from friction tests, and results from the BAT can be incorporated into airport pavement management systems. APMS data can be analyzed to economically optimize and prioritize scheduling of pavement maintenance, preservation and rehabilitation treatments to maintain a high level of service, thereby contributing to runway safety and optimization.