Responsible for about a third of the annual energy consumption and greenhouse gas (GHG) emissions, the U.S. transportation Network needs to attain a higher level of sustainability. This is particularly true for the roadway Network and the design of pavements in it. Vehicle fuel consumption required to overcome resisting forces due to pavement-vehicle interaction (PVI) is an essential part of life-cycle assessment (LCA) of pavement systems. These PVIs are intimately related to pavement structure and material properties. While various experimental investigations have revealed potential fuel consumption differences between flexible and rigid pavements, there is high uncertainty and high variability in the evaluated impact of pavement deflection on vehicle fuel consumption. This report adopts the perspective that a mechanistic model can contribute to closing the uncertainty gap of PVI in pavement LCA. With this goal in mind, a first-order mechanistic pavement model is considered, and scaling relationships between input parameters and the impact of PVI on vehicle fuel consumption are developed. An original calibration-validation method is established through wave propagation using the complete set of Falling Weight Deflectometer (FWD) time history data from FHWA's Long Term Pavement Performance program (LTPP), representing the U.S. roadway Network. Distributions of model parameters are determined on pavement material properties (top layer and subgrade moduli), structural properties (thickness), and loading conditions obtained from model calibration and the LTPP datasets. These input distributions are used in Monte-Carlo simulations to determine the impact of flexible and rigid pavements on passenger car and truck fuel consumption within the roadway Network. It is shown that rigid pavements behave better than flexible ones in regard to PVI due to higher stiffness. A final comparison with independent field data provides a reality check of the order of magnitude estimates of fuel consumption due to PVI as determined by the model. The calculated change in fuel consumption is used in a comparative LCA of flexible and rigid pavements, and it is shown that the impact of PVI deflection becomes increasingly important for high volume flexible roadways and can surpass GHG emissions due to construction and maintenance of the roadway system in its lifetime.

An increasing number of agencies, academic institutes, and governmental and industrial bodies are embracing the principles of sustainability in managing their activities. Life Cycle Assessment (LCA) is an approach developed to provide decision support regarding the environmental impact of industrial processes and products. LCA is a field with ongoing research, development and improvement and is being implemented world-wide, particularly in the areas of pavement, roadways and bridges. Pavement, Roadway, and Bridge Life Cycle Assessment 2020 contains the contributions to the International Symposium on Pavement, Roadway, and Bridge Life Cycle Assessment 2020 (Davis, CA, USA, June 3-6, 2020) covering research and practical issues related to pavement, roadway and bridge LCA, including data and tools, asset management, environmental product declarations, procurement, planning, vehicle interaction, and impact of materials, structure, and construction. Pavement, Roadway, and Bridge Life Cycle Assessment 2020 will be of interest to researchers, professionals, and policymakers in academia, industry, and government who are interested in the sustainability of pavements, roadways and bridges.

The proliferation of technological capability, miniaturization, and demand for aerial intelligence is pushing unmanned aerial systems (UAS) into the realm of a multi-billion dollar industry. This book surveys the UAS landscape from history to future applications. It discusses commercial applications, integration into the national airspace system (NAS), System function, operational procedures, safety concerns, and a host of other relevant topics. The book is dynamic and well-illustrated with separate sections for terminology and web- based resources for further information.

An increasing number of agencies, academic institutes, and governmental and industrial bodies are embracing the principles of sustainability in managing their activities and conducting business. Pavement Life-Cycle Assessment contains contributions to the Pavement Life-Cycle Assessment Symposium 2017 (Champaign, IL, USA, 12-13 April 2017) and discusses the current status of as well as future developments for LCA implementation in project- and network-level applications. The papers cover a wide variety of topics: - Recent developments for the regional inventory databases for materials, construction, and maintenance and rehabilitation life-cycle stages and critical challenges - Review of methodological choices and impact on LCA results - Use of LCA in decision making for project selection - Implementation of case studies and lessons learned: agency perspectives - Integration of LCA into pavement management systems (PMS) - Project-level LCA implementation case studies - Network-level LCA applications and critical challenges - Use-phase rolling resistance models and field validation - Uncertainty assessment in all life-cycle stages - Role of PCR and EPDs in the implementation of LCA Pavement Life-Cycle Assessment will be of interest to academics, professionals, and policymakers involved or interested in Highway and Airport Pavements.

Increased road traffic combined with heavy vehicle loads lead to deterioration and distress of pavements and consequently reduces the life span of the paved roads. As a result, large amounts of financial and labor resources are spent every year to improve and maintain road infrastructures around the world. Traditionally, vehicle and pavement dynamics are treated as two separate areas of research. However, they are strongly coupled together through their contact points. Thus, one of the major concerns is to develop a more reliable dynamic pavement-vehicle interaction model to investigate and evaluate accurately both vehicle and pavement responses, and also to examine the pavement distress due to the severity of traffic loads. One of the most important distress modes in pavements is fatigue cracking. Despite the fact that there have been considerable efforts in recent years in fatigue performance evaluation and the design of flexible pavements, there is still a need for further studies in predicting fatigue cracking in terms of damage distribution considering the uncertainty and variability associated with the input parameters of pavement-vehicle interaction and traffic load repetitions. The main objective of this research study is to carry out an in-depth investigation of the dynamics of the pavement-vehicle interaction and the effect of coupling action on system response, as well as fatigue study of the pavement due to repeated traffic loads. The response of the pavement-vehicle coupled system supported by a linear visco-elastic foundation has been investigated. The vehicle is modeled as a two-degree-of-freedom quarter-vehicle model, and the pavement-foundation system is described by a simply supported Euler-Bernoulli beam resting on Pasternak foundation, while the tire is coupled to the flexible pavement with a single point contact. Galerkin method has been utilized to develop the governing differential equations of motion. Direct numerical integration approach based on implicit Newmark linear average acceleration technique has been used to solve the governing differential equations in order to evaluate the response of the coupled system. Results have been validated with previous research work and also compared with those of conventional uncoupled system. The effects of different parameters such as vehicle speed, road roughness, soil stiffness and suspension damping on the responses are then investigated. For the fatigue study of flexible pavements, a methodology, for modeling pavement damage and predicting fatigue cracking of flexible pavements is presented. The methodology is based on the combination of deterministic method and stochastic approach using Palmgren-Miner's hypothesis in which Poisson process is employed to characterize the actual repetitions of traffic load. Different models are then presented to estimate the fatigue life of the pavement surface layer. The results are compared and discussed.

The sustainable development of our nation's roadway system requires quantitative means to link infrastructure performance to lifecycle energy use and greenhouse gas (GHG) emissions. Beside surface texture and roughness-induced Pavement-Vehicle Interactions (PVI), we herein recognize that the dissipation of mechanical work provided by the vehicle due to viscous deformation within the pavement structure is a relevant factor contributing to the environmental footprint of pavements. Through a combination of dimensional analysis, experiments and model-based simulations of energy dissipation in pavement structures, the key drivers of deflection-induced PVI are identified. Specifically, a novel experimental setup is developed to study the mechanism of deflection-induced PVI on small-scale silicone elastomer representations of two layered pavement systems, through visual observations of the pavement response and measurements of the dissipated energy. It is shown that the energy which is dissipated when a load moves at a constant speed along a pavement can be reduced to a dimensionless form that scales with the square of the vehicle load, the inverse of the vehicle speed, and relevant elastic and viscoelastic material and structural properties. These experimental findings form the basis for model development in which the pavement is represented as a viscoelastic beam on elastic foundation in a moving coordinate system. Using the experimentally identified dimensionless form of the model, the dimensionless energy dissipation is calibrated against actual pavement materials (concrete and asphalt) considering the specific temperature dependence of the viscoelastic deformation rate of these materials. For engineering applications, the analytical model is employed to calibrate the dimensionless dissipation in function of two material and speed-specific invariants representative of the range of values relevant for pavement engineering and road network analysis. We demonstrate that the derived model -when implemented at a network scale - provides a powerful basis for big data analytics of excess-energy consumption and GHG emission by integrating spatially and temporally varying road conditions, pavement structures, traffic loads and climatic conditions. Implemented for 50,000 lane-miles of the interstate highway system of the State of California, we demonstrate that a ranking based on the inferred GHG-emissions exhibits a power-law data distribution, akin to Zipf's Law, which provides a means to map an optimum path for GHG savings per retrofit at network scale.

Climate change is a global challenge with long-term implications. Human activities are changing the global climate system, and the warming of the climate system is undeniable. According to a roadway construction study, the construction of the surface layer of an asphalt pavement alone generates a carbon footprint of 65.8 kg of CO2 per km. Therefore, a sensible approach to study environmental impact from road pavement is crucial. Pavement life cycle assessment (LCA) is a comprehensive method to evaluate the environmental impacts of a pavement section. It features a cradle-to-grave approach assessing critical stages of the pavement's life. Material production, initial construction, maintenance, use and end of life phases exist in an entire pavement life cycle. The thesis consists of three components, which started with finding the environmental impact for different pavement maintenance and rehabilitation (M&R) techniques in the maintenance phase. The second component evaluated the environmental impact due to pavement vehicle interaction (PVI) in the use phase. Finally, the goal of the third component was to develop a set of pavement LCA models. To evaluate environmental impact for four major M&R techniques: rout and sealing, patching, hot in-place recycling (HIR) and cold in-place recycling (CIR), initially a fractional factorial design approach was applied to determine which factors were significant. Considering those significant factors and other necessary data, a hypothetical LCA case study was performed for the city of St. John's. It was found that the global warming potential (GWP) held the highest values among four M&R techniques. CIR technique produced the lowest percentage of GWP (83.87%), and for asphalt patching, the CO2 emission resulted in the highest percentage (92.22%) which became the least suitable option. To understand the PVI effect, the required data and information are collected from the Long-Term Pavement Performance (LTPP) program. Out of 141 Canadian road sections, 22 sections were selected. Several climatic parameters, including annual precipitation, annual temperature, and annual freezing index data, were collected from these 22 sections and further processed for developing clusters using a hierarchical clustering approach. Finally, the Athena Pavement LCA tool was used to measure the environmental impact from the PVI effect for each cluster. It was found that cluster 2 (high annual precipitation, high annual freezing index, and medium annual temperature) experienced the highest rate of IRI increase and therefore, high GWP value. The LCA result also indicated a relatively higher GWP due to pavement roughness from heavy vehicle traffic compared with light vehicle traffic. For the PVI effect due to pavement deflection, cluster 4 (maximum vehicle load and the minimum subgrade stiffness) emitted the highest GWP among all the clusters. Pavement LCA tools require an extensive amount of data to estimate the environmental impact. In the first and second studies, all Canadian road pavement sections were not possible to consider because of the large quantity of time consumption for LCA of each section. Therefore, a database management software, Microsoft SQL Server Management Studio, was used for filtering and data manipulation of the LTPP database considering all Canadian road sections. The manipulated data were further used to develop the LCA models using machine learning algorithms: multiple linear regression, polynomial regression, decision tree regression and support vector regression. The models determined the significant contributors and quantified the CO2 emission in pavement material production, initial construction, maintenance and use phase. Model validation was also performed. The study also revealed the contribution of Canadian provinces' CO2 emission. The proposed LCA models will help the decision-makers in the pavement management system.