Ultra-Thin Continuously Reinforced Concrete Pavement (UTCRCP) is an innovative pavement type that has the potential to fulfil South Africa’s pavement repair strategy requirements. It consists of a 50 mm thick High Strength Steel Fibre Reinforced Concrete (HS-SFRC) layer that is reinforced with 50 x 50 mm aperture steel bar mesh, placed on a newly constructed or rehabilitated pavement. The current design procedure for UTCRCP was extrapolated from conventional concrete design procedures, incorporating the improved flexural properties of the HS-SFRC to design for fatigue cracking. However, the alternative nature of the thin HS-SFRC layer in comparison to a thick normal strength concrete layer has led to the proposal that the response of UTCRCP to traffic loading should be reconsidered to improve its design approach. A literature study revealed that the wheel load configuration, the relative stiffness of the concrete layer and its foundation and the complex response of foundation materials to stress influence the response of pavements to traffic loading. The effect of these aspects was investigated by making use of scaled physical modelling, as well as Finite Element (FE) modelling that incorporated Linear Elastic (LE) and advanced material models. The effect of load configuration on thin asphalt and thin concrete layers was investigated using LE FE modelling. A three-layer system of bound layer, base and subgrade was modelled. It was found that the response of a thin concrete layer is similar to that of a thin asphalt layer subjected to axle loading in that the maximum deflection is at the load location and that a hogging type deflection is induced in the axle centreline. The stress induced at the top of the concrete layer due to this hogging moment was high, indicating the necessity of including significant steel in both the transverse and longitudinal directions of UTCRCP. The difference in substructure response (in terms of horizontal and vertical displacement), modelled using single wheel or axle loading, showed that the compression of the substructure in the axle centreline can be critical, while it is ignored when load configurations are simplified to single wheel loading. A multivariable analysis of the concrete layer thickness and base material stiffness was conducted using LE FE modelling. A similar three-layer system of bound layer, base and subgrade was used. It was found that the location of the maximum deflection is in the axle centreline for pavements incorporating thick concrete layers, while the maximum deflection is in the wheel centreline for pavements incorporating thin concrete layers. The response of thin concrete pavements was more dependent on the substructure. The physical modelling consisted of 1:10 scale models tested in a geotechnical centrifuge. The models consisted of either thick concrete layer or thin concrete layer on compacted dry sand, as well as a thin concrete layer on a cement stabilized base supported by compacted dry sand. The most notable finding was that a rut forms in the wheel path of thin concrete layers on sand, although it cannot be observed from the surface. It was observed that the concrete layer rebounds when the pavement is unloaded, forming a gap between the concrete layer and substructure. An advanced material model for sand was used to explore the effect of incorporating the stress- dependent, elasto-plastic behaviour of granular materials in UTCRCP. A FE model, similar to the three-layer system of the LE FE modelling, was used and the base layer was modelled using a soil model called Nor-Sand, which is based on the critical state framework. The initial void ratio, lateral earth pressure at-rest and overconsolidation ratio were varied. It was found that the combination of the strain hardening and the stress dependence of the elastic material stiffness resulted in higher induced stresses close to the load location. The gap formation observed during the physical modelling was confirmed by the capability of Nor-Sand to model permanent deformation. Overall, the results of this investigation indicate that the response of UTCRCP to traffic loading differs significantly from that of rigid concrete pavements. The thin HS-SFRC layer is subjected to high tensile stresses and deflects significantly into the substructure at the load location. It is proposed that UTCRCP should be designed to limit rutting, as well as that stress dependent, elasto-plastic material models should be used to optimize its layer arrangement. / Thesis (PhD)--University pf Pretoria, 2020. / Southern African Transport Conference / Newton Fund UK (Grant ES/N013905) / Civil Engineering / PhD (Civil Engineering) / Unrestricted

Over the years pavement design has been based on empirical equations developed from the American Association of State Highway Transportation Officials (AASHTO) road tests. The various editions of the AASHTO pavement design guide have served well for several decades; nevertheless many serious limitations existed for their continued use as the nation's primary pavement design procedure. For example, the traffic loads and truck sizes have increased over the years, the AASHTO design equations were derived based on the climatic conditions present at the Road Tests site, and the issue of aging materials was not addressed in the design. To overcome these limitations AASHTO finally proposed the AASHTO 2002 design guide which is based on mechanistic – empirical approach and serves to address the shortcomings and limitations of the earlier empirical design equations developed from the Road Tests. In this report, sensitivity analyses were conducted of the new AASHTO 2002 method for both flexible and rigid pavements, to understand its performance with respect to the various design parameters. Several important design parameters were selected and were varied one at a time and their effect on the pavement distresses was found. The sensitivity analysis included different amount of traffic loads, base materials, base material thicknesses, surface/slab layer thicknesses and subgrade materials. Some of the illogical results obtained from the sensitivity analyses were also addressed.

It is important for highway agencies to monitor the pavement friction periodically and systematically to support their safety management programs. The collected data can help implement preservation policies that improve the safety of the roadway network and decrease the number of skidding-related crashes. This dissertation introduces new approaches to effectively use tire-pavement friction data for supporting asset management decisions. It follows a manuscript format and is composed of five papers. The first chapter of the dissertation discusses the principles of tire pavement friction and surface texture. Methods for measuring friction and texture are further discussed in this chapter. The importance of friction in safety design of highways is also highlighted. The second chapter discusses a case study on developing pavement friction management program. The proposed approach in this chapter can be used by highways agencies to develop pavement friction management program. Contrary to general perception, that friction is only influencing wet condition crashes, this study indicated that friction is associated with both wet and dry condition crashes. The third and fourth chapters of the dissertation introduce a soft-computing approach for pavement friction management. Artificial Neural Network and Fuzzy Logic approach are presented. The learning ability of Neural Network makes it appealing as it can learn from examples; however, Neural Network is generally complicated and hard to understand for practical purposes. The Fuzzy system on the other hand is easy to understand. The advantage of Fuzzy system over Artificial Neural Network is that it uses linguistic and human like rules. Sugeno Neuro-Fuzzy approach is used to tune the proposed Fuzzy Logic model. Neuro-Fuzzy approach has the benefit of incorporating both 'learning ability' of neural network and human ruled based decision making aspect of fuzzy logics. The application of the fuzzy system in real-time slippery spot warning system is demonstrated in chapter five. Finally, the sixth chapter of the dissertation evaluates the potential of grinding and grooving technique to restore friction properties of the pavement. Once sleek spots are identified through pavement friction management program, this technique can be used to restore the friction without compromising the roadway smoothness. / Ph. D.

The objective of this research was to develop a structural index for use in network-level pavement evaluation, which facilitates the inclusion of the pavements structural condition in many pavement management applications. The primary goal of network-level pavement management is to maintain an acceptable condition of the pavements within the network using available, and often limited, resources. Pavement condition is described in terms of functional and structural condition, and the current widespread practice is to only consider the functional condition during network-level evaluation. This practice results in treatments that are often under-designed or over-designed when considered in more detail at the project-level. The disagreement may be reduced by considering the structural capacity of the pavements as part of the network-level decision process. This research was conducted by identifying various structural indices, choosing an appropriate index, and then applying data from the state of Virginia to modify the index and show example application for the index. It was concluded that the Modified Structural Index best met the research objectives. Project-level and network level data were used to conduct a sensitivity analysis on the index, and example applications were presented. The results indicated that the inclusion of the Modified Structural Index into the network-level decision process minimized the errors between network-level and project-level decisions, when compared to the current network-level decision making process. Furthermore, the Modified Structural Index could be used in various pavement management applications, such as network-level structural screening, and developing structural performance measures. / Master of Science

This research sought to integrate measurement and prediction of surface macrotexture for use in pavement management systems. This was achieved through two experiments, the first modeled the behavior of a binder-rich stone matrix asphalt when subjected to traffic loading using a heavy vehicle simulator to report the effect on pavement macrotexture. The second experiment compared high-speed macrotexture measurement devices on a variety of surfaces and under various operating conditions. The change in macrotexture due to traffic loading showed that as the cumulative load increased, the macrotexture decreased due to bleeding on the pavement's surface. A regression model determined that, on average, the macrotexture's root mean square (RMS) decreased 0.14 mm per million equivalent single axle load applied. A comparison of RMS and mean profile depth (MPD) outputs indicated that RMS was more sensitive to changes in macrotexture due to bleeding. In comparing devices, pairwise device agreement was evaluated using a Limits of Agreement. The results demonstrate good repeatability for each of the devices tested. The agreement analysis showed that not all high-speed devices can be used interchangeably for all pavement surfaces. Data acquisition speed was found to be a factor in macrotexture parameter calculation for two of the devices. The effect of speed was found to be worse on randomly textured surfaces than on transversely textured surfaces. / Master of Science / This thesis sought to integrate the collection and prediction of a pavement surface property known as macrotexture for use in the management of pavement networks. This was achieved through two experiments, the first of which modeled the behavior of asphalt concrete with a higher than typical asphalt content when subjected to simulated traffic to determine the effect on pavement macrotexture. The second experiment compared five high-speed macrotexture measurement devices on a variety of pavement surface types and under various operating conditions. The change in macrotexture due to traffic loading showed that as the cumulative traffic increased, the macrotexture decreased due to the asphalt coming out on the surface, referred to as bleeding. For the comparison of measurement devices data were processed using current industry standards. The results demonstrate good repeatability for each of the devices tested. The analysis also showed that not all high-speed devices can be used interchangeably for all pavement surface types. Vehicle speed was found to be a factor for two of the devices. The effect of speed was found to vary by surface type. Finally, vehicle acceleration was shown to influence the parameters produced by one of the devices, demonstrating that care should be taken to gather high-quality datasets for the critical pavement characteristic of macrotexture.

Stochastic analysis provides a rationale for the treatment of uncertainties, founded on the principles of probability theory and statistics, and is concerned with a quantifiable measure of the confidence or the reliability associated with any design process. In this thesis, a stochastic approach is employed in the design of flexible pavement structures, to facilitate the development of safe and reliable pavement structures. The important aspects that have been explored in sufficient detail include the system reliability and global sensitivity analysis, and the spatial and temporal uncertainties that pervade the life of pavements. Chapter 1 of the thesis provides an introduction to the stochastic modelling of flexible pavements and its significance in the present day. Highlighting the need for this study, this chapter also enumerates its objectives and presents an overview of the organization of the thesis. Chapter 2 provides a review of the existing literature of the design of flexible pavements and the approaches adopted to deal with the various sources of uncertainties in a probabilistic setting. The estimation of the uncertainties in fundamental pavement design inputs and their integration into the general performance prediction procedures has become a required component of the modern Mechanistic-Empirical pavement design methodology, which has been described in detail. This chapter also provides the scope of the thesis by identifying the areas of stochastic analysis that have received little attention in the flexible pavement design, which include the effect of spatial variability on the pavement structural responses and the techniques of global sensitivity analysis. Chapter 3 provides a detailed overview of the various methodologies adopted in this thesis to carry out the stochastic modelling of flexible pavements. The fundamental technique adopted for the analysis of reliability is the Monte Carlo Simulation (MCS), which relies upon a numerical/analytical model of the physical system, i.e. the pavement model and a probabilistic description of the design parameters represented by random variables or random fields. The high computational expense associated with the MCS, particularly in the case of random fields, is tackled by the use of meta-models based on the stochastic response surface methodology. The chapter outlines the steps followed to develop the meta-models in the form of Polynomial Chaos Equations (PCEs) and its extension to the Sparse PCE that can conveniently represent the spatial variability of the pavement fields. Chapter 4 deals with the probabilistic modelling of flexible pavements, where the design parameter and model uncertainties are quantified based on the available literature studies. The global sensitivity analysis, which aims to study the impact of the input uncertainty on the variation of a model output (critical pavement responses) through uncertainty propagation, is achieved by the construction of the Polynomial Chaos Equations (PCEs). To implement the global sensitivity analysis in a system reliability framework, a generalized approach based on Bayes’ theorem and the concept of entropy as a sensitivity measure, has been proposed in this chapter. Chapter 5 deals with the characterization of the spatial variability inherent in the pavement layer by employing random fields and analyzing the effect on the pavement responses. The discretization of the random field into a vector of random variables is achieved through the simple Midpoint Discretization and the efficient Expansion Optimal Linear Estimation method. Since the computational effort in stochastic problems is proportional to the number of random variables involved, it is desirable to use a small number of random variables to represent the random field. To achieve this, the principle of transformation of the original random variables into a set of uncorrelated random variables through an eigenvalue orthogonalization procedure is adopted. To further increase the computational efficiency of generating random fields for Monte Carlo Simulation, the variance reduction technique of Latin Hypercube Sampling and the meta-modelling technique using Sparse Polynomial Chaos Equations (SPCEs) are implemented. The primary focus of this chapter is to analyze the influence of the spatial variability of the pavement layer moduli, including its anisotropic characteristics on the pavement structural responses. Chapter 6 focuses on the time-dependent reliability of the pavement structures as they age in service, with due consideration given to degradation of strength with traffic loading. The study is concerned with the fatigue reliability and thereby only the decrease in the asphalt modulus with time is considered as a function of the accumulated damage due to repeated loading, whose uncertainty is determined by the uncertainties of material parameters and the traffic loading. The time-dependent model adopted in this chapter can be quite effortlessly embedded in the Mechanistic-Empirical design framework, and provides a tool to effectively schedule the maintenance of the pavement structure and ensure that the reliability level remains at the desired level for the entire design life of the structure. Chapter 7 summarizes the various studies reported in this thesis and highlights the important conclusions.

This thesis presents the results of developing road pavement deterioration models for the State Highway network in New Zealand pavement deterioration models are an integral part of pavement management systems, which are used to forecast long-term maintenance needs and funding requirements on a road network. As part of this research, a Long-term Pavement Performance (LTPP) programme has been established on 63 sections of the State Highways. These sections are representative of typical road sections and climatic conditions on New Zealand roads. Data collection on these sections is undertaken on an annual basis and consists of high accuracy manual measurements. These measurements include road roughness, rutting, visual defect identification and strength testing with a Falling Weight Deflectometer. Based on the LTPP data, new model formats for New Zealand conditions were developed including a crack initiation model and a three-stage rut progression model. The rut progression model consists of three stages, initial densification, stable rut growth and a probabilistic model to predict accelerated rut progression. The continuous probabilistic model developed predicts the initiation of pavement failure events such as crack initiation and accelerated rutting. It has been found that this model type has a strong agreement with actual pavement behaviour as it recognises a distribution of failure on roads rather than failure occurring at an particular point in time, namely, a year. The modelling of rut progression in the three stages including, initial densification, stable rut progression and accelerated rutting has resulted in a significant increased understanding of this defect, especially for thin flexible chip seal pavements. It has been established that the in-service performance of these pavements is relatively predictable. However, incorporating both the in-service performance and the failure of pavements into one model was unrealistic. Therefore, by having the different stages of rutting, resulted into a more accurate forecasting of this defect. Although this research has covered the two priority pavement models including cracking and rutting prediction, it has established the model framework for other pavement models to be developed. As more data become available, further work can be undertaken to refine the models and to extend the research into the performance of alternative construction materials.

The transportation infrastructure systems in the United States were built between the 50's and 80's, with 20 years design life. As most of them already exceeded their original life expectancy, state transportation agencies (STAs) are now under increased needs to rebuild deteriorated transportation networks. For major highway maintenance projects, a federal rule enforces to perform a life-cycle cost analysis (LCCA). The lack of analytical methods for LCCA creates many challenges of STAs to comply with the rule. To address these critical issues, this study aims at developing a new methodology for quantifying the future maintenance cost to assist STAs in performing a LCCA. The major objectives of this research are twofold: 1) identify the critical factors that affect pavement performances; 2) develop a stochastic model that predicts future maintenance costs of flexible-type pavement in Texas. The study data were gathered through the Pavement Management Information System (PMIS) containing more than 190,000 highway sections in Texas. These data were then grouped by critical performance-driven factor which was identified by K-means cluster analysis. Many factors were evaluated to identify the most critical factors that affect pavement maintenance need. With these data, a series of regression analyses were carried out to develop predictive models. Lastly, a validation study with PRESS statistics was conducted to evaluate reliability of the model. The research results reveal that three factors, annual average temperature, annual precipitation, and pavement age, were the most critical factors under very low traffic volume conditions. This research effort was the first of its kind undertaken in this subject. The maintenance cost lookup tables and stochastic model will assist STAs in carrying out a LCCA, with the reliable estimation of maintenance costs. This research also provides the research community with the first view and systematic estimation method that STAs can use to determine long-term maintenance costs in estimating life-cycle costs. It will reduce the agency's expenses in the time and effort required for conducting a LCCA. Estimating long-term maintenance cost is a core component of the LCCA. Therefore, methods developed from this project have the great potential to improve the accuracy of LCCA.

In 2007, the Mechanistic-Empirical Pavement Design Guide (MEPDG) was successfully approved as the new American Association of State Highway and Transportation Officials (AASHTO) pavement design standard (Von Quintus et al., 2007). Calibration and validation of the MEPDG is currently in progress in several provinces across Canada. The MEPDG will be used as the standard pavement design methodology for the foreseeable future (Tighe, 2013). This new pavement design process requires several parameters specific to local conditions of the design location. In order to perform an accurate analysis, a database of parameters including those specific to local materials, climate and traffic are required to calibrate the models in the MEPDG. In 1989, the Canadian Strategic Highway Research Program (C-SHRP) launched a national full scale field experiment known as the Canadian Long-Term Pavement Performance (C-LTPP) program. Between the years, 1989 and 1992, a total of 24 test sites were constructed within all ten provinces. Each test site contained multiple monitored sections for a total of 65 sections. Each of these sites received rehabilitation treatments of various thicknesses of asphalt overlays. The C-LTPP program attempted to design and build the test sections across Canada so as to cover the widest range of experimental factors such as traffic loading, environmental region, and subgrade type. With planned strategic pavement data collection cycles, it would then be possible to compare results obtained at different test sites (i.e. across traffic levels, environmental zones, soil types) across the country. The United States Long-Term Pavement Performance (US-LTPP) database is serving as a critical tool in implementing the new design guide. The MEPDG was delivered with the prediction models calibrated to average national conditions. For the guide to be an effective resource for individual agencies, the national models need to be evaluated against local and regional performance. The results of these evaluations are being used to determine if local calibration is required. It is expected that provincial agencies across Canada will use both C-LTPP and US-LTPP test sites for these evaluations. In addition, C-LTPP and US-LTPP sites provide typical values for many of the MEPDG inputs (C-SHRP, 2000). The scope of this thesis is to examine the existing data in the C-LTPP database and assess its relevance to Canadian MEPDG calibration. Specifically, the thesis examines the dynamic modulus parameter (|E*|) and how it can be computed using existing C-LTPP data and an Artificial Neural Network (ANN) model developed under a Federal Highway Administration (FHWA) study (FHWA, 2011). The dynamic modulus is an essential property that defines the stiffness characteristics of a Hot Mix Asphalt (HMA) mixture as a function of both its temperature and rate of loading. |E*| is also a primary material property input required for a Level 1 analysis in the MEPDG. In order to perform a Level 1 MEPDG analysis, detailed local material, environmental and traffic parameters are required for the pavement section being analyzed. Additionally, it can be used in various pavement response models based on visco-elasticity. The dynamic modulus values predicted using both Level 2 and Level 3 viscosity-based ANN models in the ANNACAP software showed a good correlation to the measured dynamic modulus values for two C-LTPP test sections and supplementary Ontario mixes. These findings support previous research findings done during the development of the ANN models. The viscosity-based prediction model requires the least amount data in order to run a prediction. A Level 2 analysis requires mix volumetric data as well as viscosity testing and a Level 3 analysis only requires the PG grade of the binder used in the HMA. The ANN models can be used as an alternative to the MEPDG default predictions (Level 3 analysis) and to develop the master curves and determine the parameters needed for a Level 1 MEPDG analysis. In summary, Both the Level 2 and Level 3 viscosity-based model results demonstrated strong correlations to measured values indicating that either would be a suitable alternative to dynamic modulus laboratory testing. The new MEPDG design methodology is the future of pavement design and research in North America. Current MEPDG analysis practices across the country use default inputs for the dynamic modulus. However, dynamic modulus laboratory characterization of asphalt mixes across Canada is time consuming and not very cost-effective. This thesis has shown that Level 2 and Level 3 viscosity-based ANN predictions can be used in order to perform a Level 1 MEPDG analysis. Further development and use of ANN models in dynamic modulus prediction has the potential to provide many benefits.

The aggregate base layer is a vital part of the flexible pavement system. Unlike rigid pavements, the base layer provides a substantial contribution to the load bearing capacity in flexible pavements, and this contribution is complex: stress dependent, moisture dependent, particle size dependent, and is anisotropic in nature. Furthermore, the response of the aggregate layer in the pavement structure is defined not only by resilient properties of the base layer but also by permanent deformation properties of the aggregate layer. Before the benefits of revolutionary changes in the typical pavement structures, such as deep unbound aggregate base (UAB) layers under thin hot mix asphalt surfaces and inverted pavement systems can be justified, an accurate assessment of the UAB is required. Several researchers identified that in order to properly assess the contribution of the UAB in the pavement structure, it is necessary to consider not only the vertical modulus but also the horizontal modulus as this substantially impacts the distribution of stresses within the pavement structure. Anisotropy, which is defined as the directional dependency of the material properties in unbound granular bases, is inherent even before the aggregate layer is subjected to traffic loads due to random arrangement of particles upon compaction. Distribution of particle contacts is dominated by the geometry of the aggregates as well as the compaction effort at the time of construction. Critical pavement responses and therefore performance of flexible pavements are significantly influenced by the level of anisotropy of aggregate layers. There are several ways to characterize the level of anisotropy in unbound aggregate systems. Previous research at Texas A&M University suggests functions of fitting parameters in material models (kvalues) as characterizers of the level of anisotropy. In the realm of geotechnical engineering, the ratio of the horizontal modulus to vertical modulus is commonly referred to as the level of anisotropy. When the vertical and horizontal moduli are equal, the system is isotropic, but when they differ, the system is anisotropic. This research showed that the level of anisotropy can vary considerably depending on aggregate mix properties such as gradation, saturation level, and the geometry of the aggregate particles. Cross anisotropic material properties for several unbound and stabilized aggregate systems were determined. A comprehensive aggregate database was developed to identify the contribution level of aggregate features to the directional dependency of material properties. Finally a new mechanistic performance protocol based on plasticity theory was developed to ensure the stability of the pavement foundations under traffic loads.