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Nonnormality in the form of skewness and kurtosis was examined in lot acceptance quality characteristics data from seven state highway agencies for their highway construction quality assurance programs. Lot skewness and kurtosis varied significantly. For most lot data sets, skewness values varied in the range of 0.0 ± 1.0, whereas most kurtosis values varied in the range of 0.0 ± 2.0. The analysis also reveals that, on average, 50% of lot test data sets were nonnormal with 15% of lot data sets having skewness greater than ±1.0 and kurtosis greater than ±2.0. This is a significant finding because most state transportation agencies' pay factor algorithms assume normally distributed lot. Further investigation showed that high skewness and kurtosis were associated with higher lot variability. This variability produced misleading results in regard to inflated Type I error and low power for the F-test. However, the t-test was found to be quite robust for distinguishing mean differences. Significant deviation was observed in lot pay factors based on percent within limits between assumed normal data and normalized data. Effects of nonnormal distribution on the lot pay factor were found to be varied on the basis of the specification limits, the distribution of defective materials on the tails in the case of two-sided limits, and the orientation of the nonnormal distribution itself.

To address the challenges associated with urban expansion and environmental changes, innovative interlocking concrete block pavement (ICBP) is being researched for usage in urban areas. The ICBP is designed to have higher durability and better long-term performance compared to traditional asphalt pavement. Using recycled concrete aggregates (RCA) and supplementary cementing materials (SCMs) can provide many environmental benefits. The objective of this research is to investigate the mechanical properties of concrete with recycled materials. This also involves the assessment of deflection and stresses associated with ICBP using the finite element method. Four concrete mixtures with different RCA and SCMs contents were designed and cast. The RCA replacement levels were 20% and 40%, while slag and glass pozzolan were added to improve mechanical properties. The results showed that the use of RCA had adverse impacts on workability. The 28 days compressive strength of the Control Mix was 40 MPa. The compressive strength of Mix 3 was 40.5 MPa which was the highest strength among all mixtures. It demonstrated that a 40% RCA replacement level could have a non-negative effect on mechanical properties when the SCMs are added. A three-dimensional pavement model was established using ABAQUS software. The orthogonal experimental design was used to evaluate the effects of the length/width ratio of blocks, the block thickness, the elastic modulus, and the laying pattern of blocks on the deflection and von Mises stress of all ICBP models under the vertical load. Considering the deflection of the loading area, the length/width ratio had the greatest effect, then comes with thickness, elastic modulus, and laying pattern according to the Range Analysis. The bigger block size and higher elastic modulus of blocks could provide even better performance. Overall, the herringbone laying pattern is recommended as the optimum laying pattern with minimum deflection. It also contributes to better load spreading. / Thesis / Master of Applied Science (MASc)

Simulative tests, such as asphalt pavement analyzer (APA), Hamberg rut tester etc. have been widely used to evaluate the performance of asphalt mixtures. However, simulative tests to evaluate the performance of the mixtures cannot give fundamental properties of Asphalt Concrete (AC) due to the complex stress and strain fields. On the other hand, due to the availability of high-performance computing systems and software, numerical techniques are gaining popularity. This dissertation presents a computational simulation method of the APA tests in order to evaluate the rutting potential of asphalt mixtures based on actual microstructure reconstructed from X-ray tomography images. In the study, the microstructure of AC is obtained through the analysis of X-ray images, which included the digital information of the microstructure for the scanned specimen. In the simulations the three phases, mastic (asphalt binder with mineral filler), aggregates, and voids are assigned with different material properties. Aggregates are modeled as an elastic material, and air voids are removed during the loading steps. The adopted two-layer model is only used to represent the rate and temperature dependent behavior of the mastics. The parameters are obtained with inverse methods. Based on the sensitivity analysis of the parameters, an iterative procedure is performed to optimize the parameters using the experimental measurement and results of the model simulations. A parametric study is also conducted to study the effect of major parameters such as the stiffness ratio of the networks on the macro response of the model. The simulation results obtained shows a good agreement with the experimental results. The dissertation also presents a method to measure micro strains in asphalt mixture. An automated procedure using tomography images to reconstruct three-dimensional particles is developed. The translations of the particles are obtained from the coordinate differences of particles' mass centers before and after the APA testing. The micro and macro strains in the mixture are calculated based on the particle translations. A good correlation is found between measured strains and experimental result. / Ph. D.

Simulative tests refer to the Full-scale accelerated pavement testing (APT) and laboratory wheel tracking testing, which are widely used for evaluation of pavement responses and performance under a controlled and accelerated damage conditions in a compressed limited time. This dissertation focuses on comparative evaluations under ALF, MMLS 3 and APA tests, in terms of rut depth, strain response, seismic stiffness, and contact stress using both experimental and numerical simulation results. Test slabs extracted from the ALF test lanes, are trafficked with the MMLS3 under comparable environmental conditions at laboratory in Virginia Tech. Some specimens were cut from the slabs for APA tests at VTRC. It is found that the monitored parameters yielded by the MMLS 3 test were comparable to the related full-scale ALF test results in terms of intrinsic material characteristics and pavement performance. The wireless sensor network based on Internet of things technology is implemented in laboratory for the MMLS 3 test, which provides a convenient solution for researchers on long-term observation and monitoring without being physically presented. The numerical simulations of ALF, MMLS 3 and APA in ABAQUS are used to supplement the investigation on the pavement response and performance under repeated moving loading. The viscoelastic-viscoplastic model is adopted to characterize rate and temperature dependent properties of asphalt mixtures. The 3D finite element models are capable of predicting the pavement response at critical locations while underestimates the rut depth because the permanent deformation induced by volumetric change cannot be represented in simulation. According to the test results, a power law function fits well for the accumulated rut depth versus number of load repetitions before the material reaches tertiary stage in MMLS 3 test. The rut depth development of APA tests exhibits a close-to-liner regression with number of load cycles after the initial 500 load repetitions. A regression model for predicting rut depth after 500 loads has a satisfying agreement with the experimental measurement. The calibrated MEPDG fatigue model can be used to estimate the allowable load repetitions in MMLS 3 trafficking. Besides, the effects of tire configuration, tire pressure, axle load amplitude, wheel load speed and temperature on pavement responses are investigated in this dissertation using the finite element model. It is concluded that MMLS 3 is an effective, economic and reliable trafficking tool to characterize rutting and fatigue performance of pavement materials with due regard to the relative structures. MMLS 3 test can be employed as the screen testing for establishing full-scale testing protocols as desired or required, which will significantly enhance economics of APT testing. / Ph. D.

This thesis compared macrotexture measurements obtained using the volumetric method (Sand Patch) and three laser-based devices: MGPS system, ICC laser profiler, and Circular Texture Meter (CTMeter). The study used data from three sources: two controlled experiments conducted at the Virginia Smart Road, field data collected on eight newly constructed hot-mix-asphalt (HMA) roadway surfaces, and data collected on airport surfaces at the Wallops flight facility, Virginia. The data collected at the Virginia Smart Road, a controlled-access two-lane road that includes various HMA and concrete surfaces, was used for the main analysis. The other two sets of data were used for verification and validation of the model developed. The analysis of the data collected at the Virginia Smart Road showed that the CTMeter mean profile depth (MPD) has the highest correlation with the volumetric (Sand Patch) mean texture depth (MTD). Furthermore, texture convexity had a significant effect on the correlation between the measurements obtained with different devices. Two sets of models for converting the laser-based texture measurements to an estimated MTD (ETD) were developed. One set of equations considered all the data collected at the Virginia Smart Road, and the other excluded the measurements on the Open-Graded Friction Course (OGFC). The developed models were tested using measurements collected at eight roadway sections throughout Virginia and the Wallops flight facility. The model, excluding the OGFC section, was successfully applied to other sites. / Master of Science

Tire-Pavement Interaction Noise (TPIN) becomes the main noise source contributor for passenger vehicles traveling at speeds above 40 kph. Therefore, it represents one of the main contributors to noise environmental pollution in residential areas nearby highways. TPIN has been subject of exhaustive studies since the 1970s. Still, almost 50 years later, there is still not an accurate way to model it. This is a consequence of a large number of noise generation mechanisms involved in this phenomenon, and their high complexity nature. It is acknowledged that the main noise mechanisms involve tire vibration, and air pumping within the tire tread and pavement surface. Moreover, TPIN represents the only vehicle noise source strongly affected by an external factor such as pavement roughness. For the last decade, new machine learning algorithms to model TPIN have been implemented. However, their development relay on experimental data, and do not provide strong physical insight into the problem. This research studied the correct configuration of such tools. More specifically, Artificial Neural Network (ANN) configurations were studied. Their implementation was based on the problem requirements (acoustic sound pressure prediction). Moreover, a customized neuron configuration showed improvements on the ANN TPIN prediction capabilities. During the second stage of this thesis, tire noise test was undertaken for different tires at different pavements surfaces on the Virginia Tech SMART road. The experimental data was used to develop an approach to account for the pavement profile when predicting TPIN. Finally, the new ANN configuration, along with the approach to account for pavement roughness were complemented using previous work to obtain what is the first reasonable accurate and complete tool to predict tire noise. This tool uses as inputs: 1) tire parameters, 2) pavement parameters, and 3) vehicle speed. Tire noise narrowband spectra for a frequency range of 400-1600 Hz is obtained as a result. / Master of Science / Tire-Pavement Interaction Noise (TPIN) becomes the main noise source contributor for passenger vehicles traveling at speeds above 40 kph. Therefore, it represents one of the main contributors to noise environmental pollution in residential areas nearby highways. TPIN has been subject of exhaustive studies since the 1970s. Still, almost 50 years later, there is still not an accurate way to model it. This is a consequence of a large number of noise generation mechanisms involved in this phenomenon, and their high complexity nature. It is acknowledged that the main noise mechanisms involve tire vibration, and air pumping within the tire tread and pavement surface. Moreover, TPIN represents the only vehicle noise source strongly affected by an external factor such as pavement roughness. For the last decade, machine learning algorithms, based on the human brain structure, have been implemented to model TPIN. However, their development relay on experimental data, and do not provide strong physical insight into the problem. This research focused on the study of the correct configuration of such machine learning algorithms applied to the very specific task of TPIN prediction. Moreover, a customized configuration showed improvements on the TPIN prediction capabilities of these algorithms. During the second stage of this thesis, tire noise test was undertaken for different tires at different pavements surfaces on the Virginia Tech SMART road. The experimental data was used to develop an approach to account for the pavement roughness when predicting TPIN. Finally, the new machine learning algorithm configuration, along with the approach to account for pavement roughness were complemented using previous work to obtain what is the first reasonable accurate and complete computational tool to predict tire noise. This tool uses as inputs: 1) tire parameters, 2) pavement parameters, and 3) vehicle speed.

Sustainable pavement management implies maintaining acceptable condition of pavements while also considering the tradeoff between cost, environmental impacts and social impacts of pavement investments. Typical pavement management practices only consider economic considerations, and environmental mitigation techniques are employed after the selection of the maintenance action is complete. This dissertation presents a series of papers that demonstrate the impact of decision making on the environmental impact of the pavements both at the project and network levels of pavement management. An analysis was conducted of two models that relate pavement properties to vehicle rolling resistance and fuel consumption. These models were used, along with other tools to evaluate the impact of including the use phase of a pavement into pavement lifecycle assessments. A detailed project level lifecycle assessment was conducted, and it was found that the vehicles on the pavement during the use phase contribute the most to environmental pollutants by a significant margin over other phases of the lifecycle. Thus, relatively small improvements in the factors which contribute to rolling resistance may significantly influence the environmental impacts of the pavement. Building on this, a network level lifecycle assessment method was proposed to probabilistically quantify energy consumption for a given set of expected maintenance actions. It was shown that, although maintenance actions require a certain amount of energy consumption, this energy can be offset by improved road conditions leading to reduced rolling resistance. However, this tradeoff of reduced energy consumption also includes increased costs for a given network condition. In other words, the lowest energy consumption values did not tend to fall along the line defined by minimizing the cost divided by the pavement condition. In order to demonstrate how this tradeoff should be addressed, a novel decision analysis framework was developed, and implemented on a specific pavement network. Finally, a survey of transportation professionals was evaluated to determine their optimal points within the solution space defined by minimizing costs and energy consumption while maximizing pavement condition. It was found that the solution space could be greatly reduced by implementing their responses using the proposed decision analysis framework. / Ph. D.

Pavement is usually composed of aggregate, asphalt binder, and air voids; rigid pavement is built with hydraulic cement concrete; reinforced pavement contains steel. With these wide ranges of materials, different mechanical behaviors need to be defined in the pavement simulation. But so far, there is no research providing a comprehensive introduction and comparison between various contact models. This paper will give a detail exploration on the contact models that can be potentially used in DEM pavement simulation; in the analysis, it includes both a theoretical part, simulation results and computational time cost, which can reveal the fundamental mechanical behaviors for the models, and that can be a reference for researchers to choose a proper contact model. A new contact model—the power law viscoelastic contact model is implemented into software PFC 3D and is numerically verified. Unlike existing linear viscoelastic contact models, the approach presented in this thesis provides a detailed exploration of the contact model for thin film power-law creeping materials based on C.Y Chueng's work. This model is aimed at simulating the thin film asphalt layer between two aggregates, which is a common structure in asphalt mixtures. Experiments with specimens containing a thin film asphalt between two aggregates are employed to validate the new contact model. / Master of Science

Modeling pavement deterioration and predicting the pavement performance is crucial for optimum pavement network management. Currently only a few models exist that incorporate the structural capacity of the pavements into deterioration modeling. This thesis develops pavement deterioration models that take into account, along with the age of the pavement, the pavement structural condition expressed in terms of the Modified Structural Index (MSI). The research found MSI to be a significant input parameter that affects the rate of deterioration of a pavement section by using the Akaike Information Criterion (AIC). The AIC method suggests that a model that includes the MSI is at least 10^21 times more likely to be closer to the true model than a model that does not include the MSI. The developed models display the average deterioration of pavement sections for specific ages and MSI values. Virginia Department of Transportation (VDOT) annually collects pavement condition data on road sections with various lengths. Due to the nature of data collection practices, many biased measurements or influential outliers exist in this data. Upon the investigation of data quality and characteristics, the models were built based on filtered and cleansed data. Following the regression models, an empirical Bayesian approach was employed to reduce the variance between observed and predicted conditions and to deliver a more accurate prediction model. / Master of Science