Traditional pavement structures are composed of layers with increasing stiffness from the bottom up. On the other hand, inverted pavements (IPs) incorporate a stiffer subbase, typically stabilized with cement, and placed under a less stiff granular base. The surfacing generally comprises a sprayed seal or a thin asphalt concrete (AC) layer. IPs are common in South Africa owing to their low initial construction costs and relatively simple maintenance requirements. In the US, there are only a few documented cases of IP. In addition to potential cost benefits, IPs are considered an environmentally friendly alternative, as they could reduce the carbon footprint of pavements. This paper presents a sustainability assessment of IP case studies in the US using an environmental life cycle assessment (LCA) approach. The results indicate that it is possible to reduce environmental impacts by adopting IPs, although this was not observed in all the case studies. In some cases, the IP performance was inferior to conventional pavement (CP) performance, which could lead to higher life cycle environmental impacts if the IP requires more frequent maintenance and rehabilitation activities.

Wide thermal cracks are a common form of pavement distress affecting primary state and county highways, urban residential streets, and parking lots across the Southwest climatic regions. These cracks are primarily caused by thermal fatigue, driven by diurnal temperature variations despite the lack of extremely cold events. This research aims to identify and analyze the local factors contributing to the initiation and propagation of thermal fatigue cracks. Field cores are collected from 12 sites exhibiting wide thermal cracks in the Phoenix metropolitan area in Arizona to evaluate their volumetric properties and the degree of binder aging. Advanced finite element (FE) models were developed to examine the influence of pavement structures and local climatic conditions on the development of tensile stresses due to thermal fatigue. The FE analysis indicated a high magnitude of thermal stresses due to cyclic temperature variations in Arizona compared to colder regions in the United States. Based on the forensic investigation and analysis performed, the initiation of wide cracks was shown to be primarily due to repeated localized damage from frequent thermal fatigue events on severely aged pavements. This damage is exacerbated by low air voids in mineral aggregate, an insufficient effective binder volume. and excessive binder aging, which compromise the structural integrity of the pavement.

Slippage failure has been observed on asphalt runway pavements at high-speed exits, due to either asphalt mixture instability or delamination at layer interface. This study aims to develop material and construction specification requirements to prevent slippage failure. Airfield asphalt mixtures were collected from the field for testing of dynamic modulus, triaxial shear strength, and interface shear strength. On the other hand, numerical modeling is used to predict normal and shear stresses at pavement near-surface and layer interface under free rolling and braking of aircraft tire. The results from laboratory characterization and mechanistic modeling were used to evaluate shear failure potential of surface mix and layer interface under multi-axial stress states. Furthermore, cyclic loading tests were performed to obtain fatigue life before rapid permanent deformation for asphalt mix and interface delamination at different shear stress ratios, which was used to determine the critical shear stress ratio and the corresponding requirement on shear strength. Finally, the critical pavement temperatures for shear strength requirements are determined by using machine learning models with the inputs of climate variables. The analysis results recommend the thicker asphalt overlay is beneficial for mitigating interface delamination. The required minimum high-temperature indirect tensile (HT-IDT) strength is 22-49 psi to prevent shear flow of asphalt mix with a high value required for the mixture having a lower friction angle. The required minimum interface shear strength is 17 psi (without confinement) to prevent interface delamination. These strength tests need to be tested at the representative high temperature experienced in the airport pavement that can be predicted from site-specific climate data. Full-scale pavement tests are recommended to validate the proposed specification requirements.

This study introduced a machine learning (ML)-based approach to predict the stress intensity factor (SIF) for reflective cracking in asphalt concrete (AC) overlay subjected to aircraft loading. A 3D Generalized Finite Element Method (GFEM) was developed to compute SIF for reflective cracking under aircraft loading. Subsequently, an extensive 3D GFEM pavement database comprising 3,101,679 datapoints was constructed, considering various influential variables. Four ML models were trained and evaluated for SIF prediction. GFEM results demonstrated that ratio and specific values of lateral and longitudinal loading locations exhibited different robustness and sensitivity to simulation results. Consequently, four cases were investigated to determine optimal input and output variables. Artificial Neural Network (ANN) achieved the best overall performance when using specific values of lateral and longitudinal loading locations for predicting all three SIF values. ANN demonstrated superior prediction capability for SIF with different longitudinal loading locations compared to SIF with different lateral loading locations. Sensitivity analysis revealed longitudinal loading location as the most important input variable for predicting all three SIF values, followed by lateral loading location. In conclusion, ANN proved to be an accurate and robust method for predicting SIF of AC reflective cracking due to aircraft loading.

Slippage failure has been observed at high-speed exits of airfield pavements, which may be caused by the instability of the asphalt mixture or interface delamination. This paper investigates the slippage failure mechanism using an integration of laboratory characterization and mechanistic modeling. First, laboratory tests were conducted to measure shear strength parameters of asphalt bulk mix and layer interface at different temperatures. Second, advanced finite element models were developed to calculate airfield pavement responses under moving aircraft tire loading. Finally, the multi-axial stress state criterion was used to quantify shear failure potential under the combined loading of normal stresses and shear stresses, respectively, at pavement near-surface and layer interface under different analysis scenarios. Shear strength of asphalt mixture and interface bonding varied with mix designs and decreased significantly with temperature increase, while the stress states in airfield pavement were more affected by the aircraft loading condition. It was found that shear stress ratios at the interface are greater than those in asphalt surface layer, indicating that there may be a higher possibility of shear failure through interface delamination as compared with plastic deformation in the asphalt surface layer. The analysis findings will help develop materials and/or construction specifications to prevent slippage failure at high-speed exits of airports.

Traditional pavement structures comprise multiple layers with increasing stiffness from foundation to the surface. Inverted pavements (IPs), on the other hand, incorporate a stiffer subbase placed under a weaker base layer. Typically, the stiffer subbase consists of a cement-stabilised material, while the base layer comprises high-quality, unbound crushed rock material. The surface layer is typically a sprayed seal or a thin asphalt concrete (AC) layer. IPs are common in South Africa due to their low initial construction costs and relatively simple maintenance requirements. In the United States, there are only a few documented cases of IP. This paper presents a case study on Interstate-25, in northern New Mexico. The location where the IP was constructed experiences harsh winters and freeze-thaw cycles. It was concluded that IPs can be highly sensitive to moisture and might not be ideal in areas experiencing severe winter climate.

Reflective cracking is a major challenge in asphalt concrete (AC) overlays, particularly in Illinois, which has one of the highest percentages of overlay roads in the U.S. This study focused on optimising AC mix and thickness selection to mitigate reflective cracking of overlays. A survey of Illinois' neighboring state agencies identified four overlay configurations for interstate and non-interstate applications. A large-scale testing device was designed and built in-house to simulate a moving dual-tyre assembly using two hydraulic actuators. Eight large-scale laboratory tests were conducted to assess the combined impact of material and thickness on its performance to control reflective cracking. The study concluded that the most effective overlay combines a high-modulus, flexible wearing surface with a flexible binder course. Key strategies for further mitigation include treating deteriorated PCC joints/cracks, ensuring AC mixtures have adequate flexibility, applying polymer-modified lifts, achieving proper interlayer bonding and maintaining sufficient overlay thickness and density.

Truck platoons could reduce fuel consumption and improve safety; however, they may increase pavement damage because of potential channelized traffic and a reduced rest period. The rest period is a critical parameter, and it is not included in the AASHTOWare Mechanistic-Empirical Pavement Design Guide (MEPDG) framework. This study’s objective was to include the impact of a rest period in the MEPDG framework, utilizing repeated-load permanent-deformation test results. A shift model was developed by extending the time–temperature superposition concept to incorporate rest period using experimental data. A three-dimensional finite-element pavement model was used, and proper pavement material characteristics and loading configurations were considered. A holistic response framework was used to compute pavement distresses as a function of both the wander and the rest period (based on the shift model). To illustrate the holistic framework, a case study of truck platoons distributed uniformly on sublanes was considered. The results indicated platoons could result in lower damage than a conventional trucking operation, with 60-ft spacing between trucks being optimal.

This work proposed an machine learning (ML)-based framework to predict stress intensity factor (SIF) in reflective cracking of asphalt concrete (AC) overlay under thermal loading, to predict overlay fatigue life. A database from 8750 simulations of 3D finite element pavement models was established to predict SIF. Considering the linear relationship between crack opening and SIF, two conditions were explored to predict SIF: (i) Crack opening was included in input variables along with AC layer thickness and modulus, Portland cement concrete (PCC) modulus, and crack propagation; and (ii) crack opening was excluded from input variables, maintaining other variables. Artificial Neural Network (ANN) outperformed other ML models in both conditions. The optimal input variables for SIF prediction should include AC layer thickness, AC modulus, PCC modulus, and crack propagation. In conclusion, ANN could be an accurate and robust method for predicting SIF in reflective cracking of asphalt overlay subjected to thermal loading.

Network-level Life Cycle Assessment (NLCA) offers a strategic perspective for infrastructure decision-making, complementing the detailed insights provided by Project-level Life Cycle Assessment (PLCA). By aligning with state or federal objectives, NLCA broadens the scope of analysis but introduces challenges such as managing diverse pavement sections, varying construction years, and distinct jurisdictional requirements. This study presents a comprehensive NLCA framework designed to integrate with Pavement Management Systems (PMS), combining environmental considerations with established PMS priorities such as cost efficiency, service quality, and safety. The framework incorporates Environmental Product Declarations (EPDs) and accounts for uncertainties in maintenance and rehabilitation strategies, ensuring more resilient decision-making. By bridging environmental impact assessments with traditional pavement management objectives, the framework enables transportation agencies to adopt a holistic approach to network-level planning. A case study using the Arizona pavement network is presented to illustrate the potential use of the proposed NLCA framework.

An accelerated laboratory mixture long-term aging (LTA) protocol is developed for applications in extreme climatic conditions. The Phoenix metropolitan area in Arizona, U.S.A., is selected as the region of interest because of its extended exposure to high summer temperatures. The National Cooperative Highway Research Program (NCHRP) 09-54 project recommended a climatic aging index model that predicts the oven aging period to simulate various degrees of field aging at different pavement depths. The laboratory conditioning is determined to be 17 days of loose mixture aging at 95 degrees C. Five different binder types are selected for the development and verification of the accelerated LTA protocol. An accelerated oven aging temperature of 135 degrees C is selected. The equivalent aging period is estimated using the NCHRP 09-54 project kinetic model. Considering practicality, a 30-h oven aging period is selected for the proposed LTA protocol. The applicability of this protocol is investigated using rheological, chemical, and mixture-level fracture experiments. A multitude of parameters are used to compare the proposed LTA protocol with the NCHRP-recommended protocol. The results demonstrate that both LTA conditions are consistent across all tests performed. For modified binders, the two aging conditions produced nearly identical results. However, some discrepancies are observed, particularly in the rheological parameters of the unmodified binders, verifying the additional aging hours predicted by the kinetics oxidation model. The consistency of the results between the two aging temperatures at the mixture level verifies the feasibility of using 135 degrees C as part of a LTA protocol in the balanced mix design applications.

Turkiye faces significant seismic activity regularly, with major earthquakes inflicting severe damage on the country's infrastructure and economy. The extensive damage sustained by the road network due to a recent earthquake in the country's Southeastern region, along with the types of pavement damage and disruptions to traffic flow and emergency operations, are examined. The paper outlines a proposed approach to enhance the network's resilience to earthquakes by assessing vulnerabilities in the network and planning effective mitigation, response, and recovery ideas. Methods discussed include modeling, performance-based evaluations, and strategies for improving emergency preparedness and long-term recovery planning.

Predicting crack propagation in a composite airfield pavement is a computationally challenging task. This study presents the development of a fracture-based modeling approach to capture crack propagation in an asphalt concrete (AC) overlay on jointed Portland cement concrete (PCC) pavement structure. A four-stage numerical framework was developed to predict thermal induced joint reflective cracking. The framework leverages a combination of finite difference methods, finite element (FE) thermo-mechanical modeling, and the Generalized Finite Element Method (GFEM) coupled with the elastic-viscoelastic correspondence principle (EVCP). FE thermo-mechanical and GFEM fracture simulations were solved in 3-D. Performing the simulations in 3-D domain shows the non-uniformity of PCC joint movement through the depth and width of the concrete slabs. The results show that this non-uniformity is mainly influenced by AC stiffness and thermal expansion/contraction. Simplified design models were prepared for joint opening under different cooling cycles. GFEM s adaptive meshing and global-local analysis enable accurate calculation of stress intensity factors from the elastic solution. Application of EVCP provided the ability to address the critical challenges of incorporating 3-D viscoelastic analysis within the framework. Using EVCP viscoelastic ERR can be calculated for various AC mixtures and cooling cycles using a limited set of elastic solutions. The framework was validated using FAA outdoor test section for joint reflective cracking at the National Airport Pavement Test Facility. Thermal reflective cracking performance for different overlay scenarios was simulated in four different climatic regions, highlighting the framework s ability of capturing the effect of pavement structure and climate on overlay fatigue life.

Cracking resistance of asphalt concrete (AC) is typically determined using monotonic fracture experiments using load-line displacement or crack mouth opening displacements (CMOD) except for low or high-cycle fatigue experiments. The outcome of the monotonic fracture tests is a single energy-based or index parameter limiting the ability to distinguish a range of mixes with different stiffnesses and viscoelastic characteristics. On the other hand, high cycle fatigue, such as beam fatigue, or low cycle fatigue experiments, such as the Texas Overlay test, are time-consuming and can sometimes be less repeatable. A Cyclic CMOD Control fracture (C3F) testing protocol was developed to assess the cracking resistance of various AC mixtures at lower temperatures. The cyclic testing protocol was incorporated with a prescribed and limited number of cycles to parametrically assess the responses of the specimens to loading and unloading cycles. Various fracture parameters could be extracted from the testing protocol to gain a comprehensive characterization of an AC mix during crack initiation and propagation. A much richer assessment of AC mixes' cracking resistance can be achieved to include fracture energy, viscoelastic recovery characteristics, critical tip opening displacement (CTOD), and fracture toughness. However, the CTOD was not determined in this study due to lack of a viscoelastic solution. Specimen responses were coupled with physical crack growth via a vision-based system to trace crack propagation. The test method was applied to three different applications for AC mixes commonly used to enhance cracking resistance. The results show the benefits of polymer modification at the crack initiation stages through recovered energy and delayed cracking, whereas fiber reinforcement's role is at the steady-state crack propagation stage. The airfield mixes exhibited unique recovery characteristics and initial crack retardation behavior due to variations in their mix designs.

Thermal uniformity and in-place density are key quality assurance factors affecting freshly placed asphalt pavement (referred to as mat) performance. Existing techniques like Paver Mounted Thermal Profilers and Intelligent Compaction Technologies quantify thermal non-uniformities and density differentials. In this paper, an alternative protocol was developed using unmanned aerial vehicle (UAV) assisted aerial infrared (IR) image data. A deep-learning object detection model was developed to identify the location of the paved mat and rollers in each thermal image using the YOLOv8 model. The developed framework was used for various sites visited in 2023 and 2024. Three quantification metrics for thermal segregation-Differential Range Statistic (DRS), Thermal Segregation Index (TSI), and Mat Temperature Differential Matrix (MDM)-are compared across all sites' thermal images. A case study based on data from an experimental site is presented, and two lanes were monitored for roller movements. Compaction metrics such as the overall roller pass counts, roller speed, and insufficient roller passes based on the bottom quantile data were obtained from the developed protocol. The presented framework successfully identifies and maps the roller movements while calculating the thermal segregation and compaction metrics. When processed in the field during construction, the metrics can give near-real-time insights into the mat's non-uniformities during paving. This developed protocol can provide actionable feedback to the contractors/agencies on the job site and help improve overall consistency in the quality of construction.

Asphalt concrete (AC) layers in highway pavements are subjected to complex three-dimensional (3D) stress states due to moving load and maneuvering effects of heavy trucks in highway pavements. This paper aims to investigate and characterize the impact of moving load by simulating different scenarios of truck platoons (spacing) on the permanent deformation of AC mixture at various depths within the AC layer. An experimental program was developed using novel design triaxial testing equipment to induce moving load stress states. A custom-designed dynamic pulse configuration was developed by independently applying axial and horizontal stresses in both directions. With the individual pulsing in axial and horizontal directions, stress states simulating platoon moving loads can be compared with conventional flow number experiments with dynamic axial pulsing with constant confinement pressure. The experimental plan includes two mix designs [i.e., fine-dense graded and stone matrix asphalt (SMA) with binder type (PG 76-22SBS), temperature of (54.4 degrees C)], and different rest periods simulating different truck platoons space configurations. Different advanced tests were performed to characterize the permanent deformation and predict rutting in the AC layer using FlexPave software. The results showed that the dynamic multiaxial moving load test exhibited higher permanent deformations than the conventional tests. Furthermore, the results indicated that increasing loading time reduced the effect of rest periods. Additionally, the rutting predicted model (FlexPave) using different structural layers showed that increasing rest periods increased permanent deformation.

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM10 emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM10 using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km−1 veh−1 and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM10 emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer.

This study aimed to apply machine learning (ML) models to predict the energy release rate (ERR) in the Texas Overlay Test (TXOT) for the reflective cracking of asphalt concrete (AC) overlay. The Generalised Finite Element Method model was developed with TXOT experimental results. Subsequently, a TXOT Finite Element database was constructed. Four different cases were formulated, each utilising distinct input variables and the same output variable (i.e. ERR). Five ML models were trained and evaluated for ERR prediction. The model performances were compared across four cases to determine the optimal set of input variables. Shapley Additive exPlanations methods were employed to interpret ML models. The results demonstrated that ML models performed differently across different cases. Artificial Neural Network achieved the highest accuracy in the case which included crack length, crack opening, AC modulus, and sigmoidal function parameters. Therefore, ML models prove to be accurate and reliable for predicting ERR.

Typical monotonic load-line displacement (LLD) control and crack mouth opening displacement (CMOD) control cracking tests are limited to singular loading rates and testing temperatures. These experimental protocols are not designed to derive a fundamental fracture parameter capable of relating to the viscoelastic property and cracking behavior of asphalt concrete (AC). On the other hand, a multi-rate LLD-control monotonic fracture experiment is capable of capturing the viscoelastic cracking behavior of AC by providing the rate-dependent fundamental C* fracture parameter as an output. Crack length and crack speed parameters are combined with reaction loads to calculate the rate-dependent C* parameter. The current C* experiment protocol implements manual processing of video images and exhibits high variability. The study aims to optimize the wedge-split geometry and improve the C* fracture parameter calculation protocol by applying an automated vision-based crack detection system and recording the crack growth path, considering the horizontal and vertical movement of the crack tip with higher accuracy. The three notch lengths (5, 15, and 30 mm) were evaluated. Plant and laboratory-produced mixes with varying stiffness and fracture characteristics were included in the testing program. An optimized notch length for the test geometry was determined based on the on-specimen strain field observed using the digital image correlation (DIC) technique and crack path analysis. The optimum notch length was found to be 15 mm due to consistent strain localization at the crack tip and less crack deviation in the horizontal direction. The main contribution of this paper is the proposed vision-based crack path detection method, which enables measuring the crack path's true crack length and maximum horizontal deviation. The developed protocol and optimization of the testing geometry led to a significant improvement of the existing C* testing protocol, resulting in higher data quality and consistent results. The proposed protocol was validated using three comparable and distinctively different lab-produced mixtures. The results show that the C* experiment effectively differentiated between the mixes based on crack speed and the C* parameter.

The in-place density of asphalt pavements is a key indicator of construction quality, durability, and long-term performance. Differential thermal readings—also known as thermal segregation—and lack of uniformity in mat temperature were identified as key factors in achieving target densities. A protocol was developed in this study to identify and quantify temperature differentials using an unmanned aerial vehicle (UAV) equipped with a thermal sensor. Eight sites were visited to gather thermal data of paving construction ranging from 0 min up to 60 min after the pavement has been placed. A custom-developed Python script was developed to quantify temperature differentials and analyze the data in three sections: 1) identifying and visually quantifying thermal differentials, 2) detecting the non-uniform mat temperatures such as locally segregated spots, and 3) analyzing the cooling pattern of different sites as a function of time. Validation metrics such as the Gini index, percent of non-uniformity, and coefficient of variation were computed in discussing each of the sites’ thermal profiles. The UAV system not only provided the ability to increase spatial coverage but also gave the option to monitor the sublots within the window of compaction. These features were not possible with the existing thermal scanning products. Various temperature anomalies were identified and quantified in the sites visited, including local segregated spots, longitudinal center of lane streaks associated with gearbox and/or chain case segregation, and V-shaped pattern of cold spots indicator of paver stop-and-go operation.