Airfield and Highway Pavements 2023: Innovation and Sustainability in Airfield and Highway Pavements Technology

Joints and cracks in pavement structures represent a weak localization and will experience high stress/strain and provide a path for water to enter and further weaken the pavement structure. Joint/crack sealant materials have been used for many years to assist with extending the longevity of pavement. However, many of the materials in common use are not optimized for performance but rather are designed to meet the minimum requirement specified by agencies. This paper explores the effect of significant rheological enhancement that can be made to the design of materials to achieve optimal performance at various pavement conditions. Preliminary stress calculations are made with computation of the relaxation modulus from a fix of the discrete spectra to rheological master curves.

While electrically conductive concrete (ECON) is a viable alternative to traditional snow removal methods involving deicers, this technology has no comprehensive design codes, standards, or guidelines. In developing a mechanistic design approach for ECON HPS, understanding the relationship between ECON layer thickness to its thermal performance is pivotal to avoiding overdesign and waste of resources. Therefore, in this study, five 355 mm long and 100 mm wide carbon fiber (CF)-reinforced ECON beams with thicknesses varying from 37.5 to 87.5 mm at 12.5 mm increments were tested to evaluate the effect of thickness on thermal-energy production. According to the results, the thermal energy produced from ECON changes with its layer thickness. A model was developed for estimating thermal energy from ECON layer thickness, but more comprehensive studies are required to develop a representative model to be utilized in ECON HPS design.

Chemical stabilization of marginal materials with calcium-based agents is a sustainable means to improve the engineering properties of multi-layer structures. In the stabilization process, hydration reactions form cementitious compounds creating strong interparticle bonds, which in turn lead to a more efficient load distribution capacity of the pavement foundations. Mixture design anomalies give rise to the continuous consumption of water in exothermic hydration reactions, which results in the volumetric instability and cracking of the cementitiously stabilized layers. Such cracks can potentially propagate to other layers and jeopardize the longevity and performance of the pavement structures. Therefore, it deems necessary to characterize the shrinkage cracking potential of cement-treated layers properly. Traditionally, a limiting threshold of the unconfined compressive strength test during the mixture design is defined to predict the cracking behavior of the stabilized layers. This study, however, explores practical techniques to measure the shrinkage strain in cement-treated granular materials with specific thermal cycle provisions for virgin aggregates, recycled materials, and full depth reclamation (FDR) mixtures. The main focus of this research was to study the impact of aggregate type and cement dosage on the shrinkage strain of prismatic specimens in the laboratory and to develop relationships between the volumetric strain, unconfined compressive strength, and indirect diametrical tensile strength of cement-treated marginal materials. To accomplish these objectives, a laboratory program was developed considering four different aggregate sources and three increasing levels of stabilizer dosage. The post-processed laboratory results showed the inconsistency of predictions of cracking potential using the traditional strength tests. The deviation from the expected norms was more pronounced for the FDR materials with potential contaminations of the mixes with plastic subgrade soils during the milling process. The outcome of this research can provide valuable insight into the laboratory mixture design of pavement foundations using virgin, recycled, and reclaimed materials treated with cementitious stabilizers.

The availability of large volumes of crushed waste glass (CWG) is drawing attention to exploring its use as a potential synthetic alternative to natural sand, particularly in asphalt mixes. Such green asphalt mixes with CWG have been characterized for essential structural and durability properties in the laboratory by researchers globally. However, the use of CWG in asphalt mixes in India is still limited as it is co-disposed in municipal solid waste (MSW) landfills. This paper investigates the geotechnical properties and morphological characteristics of CWG, and a conventional aggregate from India—Badarpur sand. Marshall tests were conducted on asphalt mixes prepared without fillers using Badarpur sand and 100% replacement of this sand with CWG. Results showed that CWG-based mixes have a comparable peak Marshall stability with sand-based mixes but at a lower asphalt content. Surface roughness of aggregates caused a significant difference in absorption characteristics of both mixes, while effect of shape was seen on other Marshall parameters. Therefore, CWG meets the essential criteria for use in asphalt mixes, and the obtained results are encouraging for the same.

Every year, almost eight million metric tons of plastic wastes pollute the oceans, affecting the environment. Therefore, researchers are constantly looking for a sustainable approach of using ocean plastic wastes (OPW), and to address this problem is to create an asphalt additive made from OPW, which might lower greenhouse gas emissions by reducing the need for new petrochemical polymers. This study shows the feasibility of using ocean plastic waste to improve binder performance. Near-shore and ocean-bound plastic were analyzed using near infra-red (NIR) spectroscopy. Analysis and the characterization of plastics modified binder with viscosity-temperature susceptibility and stability test were performed to understand the plastic types and influence on the binder, respectively. Thirty-three different plastics modified binders composed of high-density polyethylene (HDPE), nylon, and polypropylene were tested. Modified HDPE and certain types of nylon were found to enhance binder properties enabling for the sustainable utilization of OPW.

Five types of waste plastic streams were investigated for their compatibility with a PG 64-22 asphalt binder. The waste plastics included high-density polyethylene (HDPE) pails; low-density polyethylene (LDPE) food packaging waste; polypropylene (PP) from various waste sources, such as plastic cups, medicine bottles, blackboards, and whiteboards; polystyrene (PS) from food packaging; and plastic number 7 from various sources, including polycarbonate, CD cases, and polycarbonate fibers. The plastics were characterized using Fourier-transform infrared spectroscopy (FTIR). Thermogravimetric analysis was performed to optimize the blending. Storage stability of waste plastic–modified binders was evaluated using the cigar tube test (CTT). Rheological and multiple stress creep recovery (MSCR) tests were performed for predicting rutting and cracking potential. No significant chemical changes were noticed when plastics were added to the binder. However, some of the waste plastic–modified binders appeared to be unstable. The rheological properties of waste plastic–modified binder vary with respect to unmodified binder; most plastics exhibited degradation. The MSCR test showed no improvement in the elasticity of the nonrecoverable strain for tested waste plastic–modified binders. This suggests that wet mixing of waste plastic with binder could be ineffective in some cases.

The utilization of waste material as aggregates in the pavement industry can allow the dispersal of waste material as well as help to prevent environmental pollution. It provides a great economical solution to overcome the scarcity of natural aggregate in Bangladesh. The purpose of the present study is to explore the opportunity to find the feasibility of steel slag as an alternative aggregate by analyzing and comparing the properties of steel slag with other pavement construction materials. In this study, it was observed that the physical and mechanical properties of steel slag were satisfactory. The CBR value of coarse slag has escalated by 26% and 79% from crushed stone and brick chips, respectively. Moreover, the steel slag mix performed well in the case of compressive and tensile strength. So, the successful substitution of crushed stone and brick chips with steel slag as coarse and fine aggregate provided a more reliable and cost-effective strategy for aggregate resources while lessening the menace of solid wastes, resulting in a more sustainable environment. This will shift the gear in sustainable pavement construction as well as economical and engineering aspects, which are essential in today’s energy-strapped world.

It is estimated that only about 20% of approximately 50 million tons of annually generated electronic waste (e-waste) is recycled. Burning and landfilling e-waste, as common disposal methods, pose a risk to the environment and communities’ health. Given its substantial annual production rate and the scale of pavement construction projects, hot-mix asphalt (HMA) has been successfully used to utilize different types of waste materials and industrial byproducts. So far, waste materials such as plastics and scrap tires have been successfully used in new asphalt pavements. Therefore, HMA was identified as a candidate for recycling e-waste. To this end, this study was undertaken to investigate the feasibility of utilizing e-waste as synthetic aggregate in HMA to replace mineral aggregates partially. For this purpose, e-waste collected from an electronics manufacturer was crushed. HMA mixes containing about 15% recycled e-waste (by volume) and a mix without any e-waste (control mix) were produced in the lab. Rutting and cracking potentials of the laboratory-compacted HMA mixes were determined by conducting wheel tracking and semicircular bend tests, respectively. The findings of this study are expected to provide preliminary data as a means for facilitating the utilization of e-waste in HMA, which, in turn, will result in economic and environmental benefits.

Airport pavements are subjected to high-impact loads, and conventional design wisdom prescribes pavement thicknesses utilizing the lowest modulus obtainable for subgrade soil over a given wet-dry cycle. This is due to variations in strain, stress, deformation, and reduction in strength when subjected to moisture changes. By engineering water repellency in pavement soils, moisture conditions can be kept fairly uniform, allowing for easier design, reducing speculation of performance, and saving material and construction costs. Temperature and water content measurements were obtained from soil obtained from the Charlotte Douglas International Airport treated with organosilane and compared with untreated soil under varying moisture conditions. Results show a reduction in moisture content as well as variations in the treated soil sample compared to the untreated sample. Samples were exposed to two winter storm events, and a maximum heave of ~6 mm was measured for the untreated soil, while the treated soil sample did not heave.

Concrete grinding residue (CGR) is a byproduct of the diamond grinding processes commonly used to rehabilitate concrete pavements. This study investigated the potential for reusing CGR to stabilize roadside shoulder soil through comprehensive CGR field implementation trials in Washington County, Iowa. Two methods of CGR application were evaluated: (1) mixing CGR with the shoulder soil and (2) placing CGR on top of the shoulder soil. The optimum CGR content was determined by performing an unconfined compressive strength test on CGR mixed soil specimens following a seven-day curing period. Since the highest unconfined compressive strength was achieved by mixing 20% CGR by weight, this mixture was selected as the CGR application rate during field applications. Dynamic cone penetrometer and lightweight deflectometer tests have been conducted for two years to determine the California Bearing Ratio and composite elastic modulus of shoulder soils. While the UCS value of CGR-treated soil was improved by 50%, no significant strength improvement of the treated shoulder soil was found at the field demonstration site, although the CGR reclaimed section exhibited a modest increase in strength. The blending of CGR into the shoulder material was found to be the most efficient stabilization method for field application.

The bitumen stabilization technology offers a sustainable pavement engineering solution that enhances reclaimed pavement materials and available quarried aggregates. It has become increasingly important to properly characterize commonly selected parent materials in order to make the most efficient use of the stabilization technology. This study examines the geometric properties of reclaimed asphalt pavement (RAP), recycled concrete aggregate (RCA), crushed blue limestone, and crushed andesite particles, using the aggregate imaging system (AIMS). Results showed that recycled aggregate (RAP, RCA) particles are highly angular, with RAP having a rough texture. This was in comparison to the moderately angular and low-moderate texture obtained for the two quarried crushed aggregates. Additionally, while all materials’ fine aggregate angularity index values were within the moderate range, the recycled materials were most angular. The study further examined the form properties of the aggregate particles, with RAP and RCA also being the most spherical of the investigated aggregates. The outcomes of the study further demonstrate the potential for the reuse of construction waste materials as pavement layer aggregates, in a sustainable method that can be beneficial in improving waste management, while protecting natural resources and the environment. The observed differences in geometric characteristics between these materials also highlight the significance of parent material characterization and will be the basis of further investigation into the influence on stabilized material behavior.

This study focuses on the properties and performance of hot-mix asphalt (HMA) mixes, where the aggregates are coated with waste plastics commonly found in Bangladesh. Different percentages of waste plastics have been used to prepare the HMA test samples. The waste plastic-modified aggregates were found to be performing better in terms of their absorption, aggregate crushing value, aggregate impact value, and Marshall Stability value, when compared with the unmodified aggregates. However, the behavior of the HMA mixes made with waste plastic-modified aggregates tends to behave more like gap-graded asphalt concrete mixtures than dense-graded mixes. While more research is needed to determine the optimum amount of waste plastics to prepare the waste plastic-modified aggregate for using in the HMA of the flexible pavements of Bangladesh, the research suggests that such pavements are at least very much suitable for low traffic zone and parking areas. The findings open up the possibility of introducing recycled waste plastics in flexible pavement construction.

Future climate conditions are not going to resemble the past. Temperatures will be hotter, and storms will be stronger. However, pavements are still being designed assuming that past conditions will resemble the future. This is a bad assumption. When climate change and pavement resilience are discussed, the focus is often on the immediate impacts of the natural disaster. While this is important, pavement damage also occurs after the natural disaster when rescue, emergency response, recovery, and rebuilding activities are taking place, and the pavement, often in a weakened state, is subjected to increased volume of heavier traffic. This paper will show how current resiliency concepts and framework can be applied to pavements and how two cement-based solutions (concrete overlays and full depth reclamation with cement) can be used to improve pavement resilience and mitigate damage both during and after a natural disaster, using flooding as an example.

To date, there is no agreed-upon accurate standard method for collecting automated pavement faulting measurements at a network level. This issue arises because detecting joints automatically and at high speeds has been proven to be complex. There is a high demand for an automated joint detection system that is accurate, quick, easy to implement, and consistent across different rigid pavement surface finishings. This study collected a sample of 573 transverse joints from jointed concrete pavements with two different surface finishings using a custom-built high-speed laser profiler. These data were used to develop joint detection algorithms using machine learning models. The results indicate that joint detection rates vary drastically depending on the surface finishing type and the input data provided to the models. The deep neural networks and random forest models developed in this study had accuracies, measured in terms of the F1-score, ranging from 70% to 80% and from 50% to 80%, respectively.

As more agencies move toward implementing the balanced mix design (BMD) of asphalt mixtures, laboratory performances, including resistance to cracking, will be required to meet established criteria during mix design. There may be cases when several candidate mixes with different components need to be evaluated for fatigue cracking resistance prior to final mix design selection, which can be time-consuming and sometimes non-practical. To reduce the number of laboratory tests, a quick prediction method to identify the mixtures with better cracking resistance without need to complete the test is required. Artificial neural network has shown potential to predict certain properties of asphalt mixtures in recent research. This study investigated implementing one type of artificial neural network—convolutional neural network (CNN), to predict the cracking resistance of asphalt mixtures, represented by the overlay test based on the material properties and test setup parameters. The architecture of the CNN was established and optimized, with predicting capability tested and evaluated. The predicting quality of the CNN was slightly better than the linear regression model, demonstrating potential to preliminarily predict the cracking resistance so the laboratory testing effort can be reduced.

Machine learning algorithms are powerful AI tools that have demonstrated strong robustness and reliability in the performance prediction of road infrastructure. In this study, the authors have attempted to develop a prediction model to estimate the transverse cracking in jointed plain cement concrete pavements using three machine learning approaches, namely, decision tree regression (DTR), random forest regression (RFR), and deep neural network (DNN). The results show that the DNN model outperformed DTR and RFR with coefficients of determination (R²) greater than 0.95 in both training and testing data sets. Performance metrics are summarised and presented for all three methods used in this study.

The Federal Aviation Administration (FAA) is developing new design procedures to extend airport pavement life beyond 20 years. One element of this research is a new distress “mega-index” whose components are intended to represent independent aspects of airport pavement serviceability: low foreign object damage (FOD) potential, low skid potential, and smoothness. The proposed components of the mega-index appear intuitively correct but require validation. This paper is part of the research to validate the assumption that these three components completely describe airport pavement serviceability. Machine learning methods were used to review maintenance and rehabilitation records to determine the types of issues that the airport owners and operators were willing to expend funds and effort to resolve, which is a strong indicator of factors that cause a pavement to fail to meet expectations. Researchers developed topic models using Latent Dirichlet Allocation (LDA) on a corpus of funding request documents and then examined the topics for issues related to pavements. The records review included the detailed maintenance records collected for the FAA’s Extended Airfield Pavement Life project and Department of Defense Standard Form 1391 requests for funding. Researchers expected the LDA result topics to contain FOD, friction, or roughness. Early results indicated that using LDA to identify the topics in funding request and work order documents was viable. Topics identified using work-order sources only included items seemingly related to cracks and roughness. The results did not scale well once the Forms SF1391 were integrated into the data set. The Forms SF1391 included many non-pavement projects, so while the analysis was able to identify that pavements needed repair, it did not identify specific pavement issues. The analysis was able to identify specific issues for other facilities, including asbestos and lead paint abatement for buildings and leak repairs for roofs. This indicates the validity of the approach given an appropriate corpus of funding documents.

The prevention of road collapse accidents caused by hidden structural defects becomes an urgent problem for road safety. Three-dimensional ground penetrating radar (3D GPR) is an advanced non-destructive detection approach to effectively detecting road hidden defects. However, the GPR images are difficult to interpret, and the manual interpretation speed is slow. To realize the automatic location and recognition technology of road internal diseases and improve detection efficiency, a large number of measured 3D GPR road data are used to establish a B-scan image disease database in this study. Data preprocessing, image capturing, defects marking, and data cleaning are performed in this database. Deep learning convolution neural network models were built based on one-stage methods (YOLOv3 and YOLOv4) and a two-stage method (Faster R-CNN). Through comparing and analyzing their recognition effect and performance differences. The frames per second (FPS) of YOLOv3 and YOLOv4 are much larger than that of Faster R-CNN. Generally, the YOLOv4 has the best performance among all the models, and the prediction accuracy of four features from high to low is well, crack, concave, cavity.

Assessing the levels of sulfate concentration and the extent of zones prone to sulfate-induced heaving is essential in designing appropriate stabilization and modification methods. However, due to spatial and temporal variations of sulfate minerals, conventional methods of measuring sulfate that provides point-specific data lead to misrepresentation of sulfate concentration. This study proposes an approach for identifying sulfate concentration levels and distributions based on electrical resistivity imaging (ERI) and random forest (RF) algorithm. Laboratory experiments were carried out on several soil samples based on a factorial design to assess the effects of sulfate concentration and water content on the electrical resistivity of soils. The sulfate concentrations of soil samples were modified by adding calcium sulfate in 1,000 ppm increments to represent sulfate concentrations ranging from 0 to 12,000 ppm. A random forest model was then trained using collected data from the laboratory (382 observations) to classify the sulfate concentrations into three levels: low (below 3,000 ppm), medium (between 3,000 ppm and 8,000 ppm), and high (above 8,000 ppm). Facing an imbalanced classification problem (i.e., one class has more observations than the others), a synthetic minority over-sampling technique (SMOTE) was used to remedy the imbalanced class distribution and increase the predictive performance of the trained model. This study showed that the random forest classifier could identify the levels of sulfate concentration using electrical resistivity and moisture content with an accuracy of 68.8%. The results showed that the random forest classifier performs better in determining the true observations from all observations in moderate sulfate concentration levels with a recall of 79% than low and high sulfate concentration levels. The proposed approach could help pavement engineers in construction and treatment decisions by determining the spatial variability of sulfate concentration levels and identifying areas with a potentially high risk of sulfate-induced heaving. Making well-informed decisions can prolong pavement service life and lower maintenance and rehabilitation costs.

This study compares the environmental impact of the different methods of determining the asphalt concrete (AC) modulus through life-cycle analysis (LCA). Dynamic modulus testing is one of the most widely used methods for determining AC modulus. On the other hand, the in situ modulus of AC can be determined using falling weight deflectometer (FWD) test. Like any other laboratory test, several steps are involved in determining AC modulus through dynamic modulus testing. At a minimum, these are the production/extraction of the appropriate and required amount of binder and aggregates, preparation of the sample, and testing and disposal of the samples. All these steps have an environmental impact. This study used process-based LCA to compare the environmental impacts. The result shows that compared to the FWD test, the laboratory dynamic modulus test emits two times higher green house gas (GHG) to the environment.

Jointed plain concrete pavement (JPCP) is the most used rigid pavement system in the United States providing a service life of 30 years or more and is proven to be cost effective against other pavement systems. The state-of-the-art Pavement ME design (PMED) procedure evaluates performance through the international roughness index (IRI), joint faulting, and transverse cracking. Primary factors that affect performance of JPCP are climate, traffic, concrete properties, and pavement structure. This study focuses on the impact of climatic factors on performance of JPCP. Simulations were performed using the PMED software version 2.6.2.1 for various climates in continental United States encompassing mild, moderate, and severe climatic conditions. This study is a comparison of simulation results with regard to freezing indices, temperature, precipitation, and freeze/thaw cycles between Southwest and Northeast regions of United States. Simulations produced show an increase of 41% in roughness index, 89% increase in joint faulting, and an 82% decrease in cracking. Variation in JPCP performance indicators can be attributed to the severe changes in climatic factors.

With the passing of the Bipartisan Infrastructure Law in 2021, the US is poised to spend billions of dollars on its highways and roadways. Despite this enormous outlay of funds, the US highway infrastructure needs are still at an all-time high. There is about a $435 billion backlog of highway road repair projects, and over 40% of US roadways are in a poor/deficient condition, which is costing the country an additional $130 billion in extra vehicle repairs and operating costs, or over $1,000 per motorist per year. Improvement of the system is needed. The primary approach to address this challenge has typically been to increase funding. While more funding helps, agencies also need to be more efficient within their constrained budgets to get more out of their roadway and pavement investments. This paper will show how competition across paving industries can be used to lower pavement unit costs.

Silent Falcon has developed a process for efficiently collecting pavement condition inspections at over 80 airports in the United States. The project planning process ensures that 100% coverage of the airfield pavements to be acquired at less than 2 mm ground pixel resolution tied into the National Geodetic Survey grid. The operations team has developed an excellent coordination process with the airport and air traffic control tower to ensure the minimum time on the runways and safe airport operations. Depending on the size of the airport, the data collection phase is accomplished using multiple UAS with FAA licensed pilots. Lessons were learned about the best overlap, best altitudes, and best processes for orthorectification of the stitched images. The distresses are quality control reviewed with a professional pavement engineer for final calculation of the Pavement Condition Index. The pavement inspection is delivered to the client in ArcGIS Pro with individual distresses in separate layers geolocating the distress and severities. Utah DOT compared the previously collected manual pavement inspections with the UAS calculated PCI and have been incredibly happy with the correlations. The ArcGIS deliverable provides the ability to compare the PCI and individual distresses with the detailed imagery, which allows for further field inspection of areas of interest. This detailed imagery is a great benefit to the airport or airport consultant. The total quantities of cracking and other distresses that are calculated supply data for preliminary engineering of rehabilitation options. Silent Falcon has gone beyond the research phase and is progressing with full-scale implementation of the system that has been developed. Feedback provided by other states’ aviation officials and airports has been incredibly positive.

A digital elevation model (DEM) is a topographic representation of a bare surface, usually referring to the Earth’s surface. DEMs have been used for flood assessment, landside hazard detection, soil properties’ quantification, road and highway planning, etc. Recently, DEMs generated using small unmanned aircraft systems (sUAS) have shown potential in transportation infrastructure inspection. In this study, we evaluated the performance of sUAS-collected DEM data to detect airfield pavement distresses. Natural-color red-green-blue (RGB) data were collected from both Portland cement concrete (PCC) and asphalt concrete (AC) pavements at three airports with UAS. The data were processed to generate an RGB optical orthophoto using close-range photogrammetry, and one product of that process was a DEM representing the airfield pavement in three-dimensional space. The resolution of the generated DEMs varied from 2.8 mm/pix to 84 mm/pix based on sUAS sensors, flying altitude, and parameters selected for RGB data processing. The DEMs were studied for their usefulness in visually identifying 14 PCC pavement distresses and nine AC pavement distresses. Analysis showed that DEM with 6 mm/pix resolution or better can be useful for detecting one or more severity levels of 13 PCC pavement distresses and 6 AC pavement distresses. High-resolution DEMs were also useful in identifying distresses related to elevation differences, such as shoving, depression, and faulting. This study illustrates the potential of DEM data in airfield pavement distress detection.

The continuous demand for virgin aggregates in construction of new roads and maintenance and rehabilitation of in-service pavements, and the ever-increasing demand to reduce the disposal of road construction waste, is making agencies to look for ways to recycle construction materials. Foamed asphalt base course (FA) mix, which is typically produced by stabilizing reclaimed asphalt pavement (RAP) with foamed (expanded) asphalt, is a material that can help in reducing the use of virgin aggregate in base layers in construction projects. FA mix behaves like unbound granular materials (UGM). A fundamental property used to characterize UGM is the material’s resilient modulus (M_r). Resilient modulus is also one of the primary input parameters for base course materials in mechanistic-empirical (M-E) pavement design. The purpose of this study was to determine the M_r (stiffness) of FA mix and compare that to the M_r of virgin aggregate (VA) used in base course of pavements using the repeated load triaxial resilient modulus test. Using the laboratory test results, the parameters of a resilient modulus model used in M-E pavement design were also estimated. In order to compare the M_r of both materials, they were compacted at three different density levels (98%, 100%, and 102% of maximum dry density). All tests were performed in accordance with the AASHTO T307 procedure, except that the number of conditioning and testing cycles were reduced to prevent potential damage of the FA mix specimens, which appeared to have more brittle behavior after curing in the oven for two days at 40°C. To maintain consistency, the same conditioning and testing protocol was followed to test the virgin aggregate. From the results, it was observed that (1) M_r of both materials increased with increases in density. The coefficient K₁, which is proportional to Young’s modulus, is positive and increased with increase in density for both materials; (2) the coefficient K₂, the exponent for the bulk stress term, was found to be positive for both materials, indicating that an increase in the bulk stress results in an increase in the material stiffness; and (3) the coefficient K₃, the exponent for the octahedral shear stress term, was negative for the foamed asphalt mix but positive for the virgin aggregate, suggesting that an increase in shear stress decreased the stiffness of the foamed asphalt but increased stiffness of virgin aggregate. The resilient modulus of the foamed asphalt mix was found to be between 1.9 and 5.4 times that of the virgin aggregate depending on density level and bulk stress, indicating suitability of foamed asphalt mix as a base course material in lieu of virgin aggregate, at least in the as-compacted state and with the used testing protocol.

Recycled concrete aggregate (RCA) in pavement construction is getting increased attention due to cost savings, conservation of natural resources, and environmental benefits. However, the lack of information on durability and strength of RCA limits its use in the construction of pavements in Oklahoma. The purpose of this study was to generate durability and strength data of commonly used RCA in Oklahoma through laboratory testing. For this purpose, an RCA and a virgin limestone aggregate (VLA) were collected from local sources. Laboratory tests were conducted using upper and lower limits of Type A gradation specified by the Oklahoma Department of Transportation (ODOT). Laboratory testing for this study included the following: particle size distribution, wash loss, optimum moisture content and maximum dry density, Los Angeles (LA) abrasion, durability index, and resilient modulus. Both aggregates were conditioned using different LA abrasion cycles (0, 100, 300, and 500 cycles) to simulate different levels of durability loss that a pavement would experience during construction. The changes in the strength of aggregates due to loss of durability were assessed by conducting resilient modulus tests. Laboratory test results indicated that the resilient modulus values were influenced by the loss of durability of aggregates and gradation due to conditioning. The durability and strength data of the RCA were compared with those of the virgin aggregates, and recommendations were made for the use of RCA in the construction of pavement bases in Oklahoma.

Reliable estimates of soil water diffusivity are critical for describing and predicting water movement in unsaturated soils. Moisture diffusion through unsaturated soils is governed by the total suction gradient within the soil profile, with moisture moving from regions of low total suctions to regions of high total suctions. As a result, soil water diffusivity is regarded as one of the most fundamental soil hydraulic parameters, and precise acquisition and parameterization of diffusivity are critical in understanding soil moisture dynamics and soil hydrology. The study’s objective is to establish a well-documented foundation for assessing soil suction variations within the so-called moisture active zone or vadose zone. The study utilized a supervised method for estimating soil diffusivity at 5 cm below the ground surface using Oklahoma Mesonet matric suction measurements. The study presents a set of equations for estimating van Genuchten diffusivity in Oklahoma soils. The study’s findings show that the prediction model produced results comparable to those obtained by the one-step pressure outflow procedure.

Proof rolling is a critical element of constructing airport pavements. This is particularly important in Australia, where flexible airport pavements generally comprise thick fine crushed rock layers with a relatively thin asphalt surfacing, and rigid airport pavements still make use of fine crushed rock sub-base materials. Traditionally, purpose-built proof rollers were capable of inducing stresses in the crush rock layers that exceeded those induced by commercial aircraft trafficking the surface of the pavement. However, as proof rolling tyre pressures have been decreased and aircraft wheel loads and tyre pressures have increased, a gap between the proof rolling capacity and the aircraft-induced stress has developed, mainly affecting the upper base layers of flexible airport pavements. This research developed and compared approaches for closing the gap, including the design of a replacement proof rolling fleet, the incorporation of more expensive bound base course layers, and retrofitting second-grade aircraft tyres to the existing proof rollers. The conclusions allow the Australian airport pavement construction industry to close the fine crushed rock proof rolling gap for more reliable airport pavement construction.

Application of gyratory compaction as an alternative compaction method for granular materials is investigated in this study. Impact hammer compaction is the prevalent compaction technique of granular materials. The effects of using Superpave gyratory compactor on the behavior of granular materials are investigated in this study. An experimental program is performed on both specimens compacted with the gyratory compactor and impact hammer. Engineering properties and mechanical response in terms of resilient modulus are studied. Resilient modulus is the primary property in mechanistic-empirical design for responses to cyclic loading. Filter paper test is conducted to measure the soil suction as the significant variable in unsaturated soils behavior. Then, the soil water characteristic curve is generated. A resilient modulus model incorporating matric suction is studied. Testing and modeling of the resilient modulus demonstrate the combined effects of soil suction, compaction method, and aggregate imaging system (AIMS) parameters. Results also show that the gyratory compactor produces different compacted materials from impact hammer compaction.