Road networks are valuable assets that deteriorate over time and need to be preserved to an acceptable service level. Pavement management systems and pavement condition assessment have been implemented widely to routinely evaluate the condition of the road network, and to make recommendations for maintenance and rehabilitation in due time and manner. The problem with current practices is that pavement evaluation requires qualified raters to carry out manual pavement condition surveys, which can be labor intensive and time consuming. Advances in computing capabilities, image processing and sensing technologies has permitted the development of vehicles equipped with such technologies to assess pavement condition. The problem with this is that the equipment is costly, and not all agencies can afford to purchase it. Recent researchers have developed smartphone applications to address this data collection problem, but only works in a restricted set up, or calibration is recommended. This dissertation developed a simple method to continually and accurately quantify pavement condition of an entire road network by using technologies already embedded in new cars, smart phones, and by randomly collecting data from a population of road users. The method includes the development of a Ride Quality Index (RQI), and a methodology for analyzing the data from multi-factor uncertainty. It also derived a methodology to use the collected data through smartphone sensing into a pavement management system. The proposed methodology was validated with field studies, and the use of Monte Carlo method to estimate RQI from different longitudinal profiles. The study suggested RQI thresholds for different road settings, and a minimum samples required for the analysis. The implementation of this approach could help agencies to continually monitor the road network condition at a minimal cost, thus saving millions of dollars compared to traditional condition surveys. This approach also has the potential to reliably assess pavement ride quality for very large networks in matter of days.

The use of reinforcing fibers in asphalt concrete (AC) has been documented in many studies. Published studies generally demonstrate positive benefits from using mechanically fiber reinforced asphalt concrete (M-FRAC); however, improvements generally vary with respect to the particular study. The widespread acceptance of fibers use in the asphalt industry is hindered by these inconsistencies. This study seeks to fulfill a critical knowledge gap by advancing knowledge of M-FRAC in order to better understand, interpret, and predict the behavior of these materials. The specific objectives of this dissertation are to; (a) evaluate the state of aramid fiber in AC and examine their impacts on the mechanical performance of asphalt mixtures; (b) evaluate the interaction of the reinforcement efficiency of fibers with compositions of asphalt mixtures; (c) evaluate tensile and fracture properties of M-FRAC; (d) evaluate the interfacial shear bond strength and critical fiber length in M-FRAC; and (e) propose micromechanical models for prediction of the tensile strength of M-FRAC. The research approach to achieve these objectives included experimental measurements and theoretical considerations. Throughout the study, the mechanical response of specimens with and without fibers are scrutinized using standard test methods including flow number (AASHTO T 378) and uniaxial fatigue (AASHTO TP 107), and non-standard test methods for fiber extraction, direct tension, semi-circular bending, and single fiber pull-out tests. Then, the fiber reinforcement mechanism is further examined by using the basic theories of viscoelasticity as well as micromechanical models.

The findings of this study suggest that fibers do serve as a reinforcement element in AC; however, their reinforcing effectiveness depends on the state of fibers in the mix, temperature/ loading rate, properties of fiber (i.e. dosage, length), properties of mix type (gradation and binder content), and mechanical test type to characterize M-FRAC. The outcome of every single aforementioned elements identifies key reasons attributed to the fiber reinforcement efficiency in AC, which provides insights to justify the discrepancies in the literature and further recommends solutions to overcome the knowledge gaps. This improved insight will translate into the better deployment of existing fiber-based technologies; the development of new, and more effective fiber-based technologies in asphalt mixtures.