International Conference on Transportation and Development 2024: Pavements and Infrastructure Systems

This research describes changes made to the FAA’s airport pavement roughness evaluation software ProFAA and to FAA AC 150/5380-9 Guidelines and Procedures for Measuring Airfield Pavement Roughness. Phase 1 developed a method to locate and quantify airport pavement roughness using profile data only. It is referred to as the multiple event roughness (MER) procedure. This presentation discusses phase 2 of this research and includes a demonstration of the updated ProFAA software and how it will work synergistically with related FAA advisory circulars to proactively maintain smooth airport pavements. The demonstration will include assessing a runway or taxiway in 500-ft sections, determine if corrective action is needed, and optimize the corrective action. The limits of acceptability for the MER process were developed empirically in phase 1 and have been embedded in ProFAA. The proactive pavement management approach will extend the life of the pavements and the aircraft that use them.

Maintenance and repair activities are critical for sustaining airfield operations on existing concrete pavement infrastructure. Repairing spalls on Portland cement concrete (PCC) pavements is a common work activity used to reduce the costs associated with aircraft damage and prolong the service life of the pavement further reducing the life-cycle costs for the pavement structure. This paper will focus on laboratory testing evaluations on cementitious rapid-setting product performed at the Engineering Research and Development Center (ERDC) in Vicksburg, MS. The laboratory evaluations were performed under the Pavement Repair Material Certification Program on seven commercially manufactured proprietary products. The laboratory testing evaluated how well each product performed under ASTM test methods for compressive strength, flexural strength, bond strength, modulus of elasticity, time of set, slump, and slump flow. This paper provided the material data sheet results from laboratory testing, and what products passed or failed the criteria for use on DoD airfield concrete repair applications.

The 1982 Consultant Board review of the US Army Corps of Engineers (USACE) overlay procedure is re-examined in the light of more recent analytical developments that provide a pathway to implementing the recommendations formulated at that time. The history of the existing USACE equations is traced, and new equations proposed in the aftermath of the 1982 Review are re-evaluated. These equations were not recommended at the time as substitutes to the prevailing USACE approach, since these could not be verified by the limited field data available. This study shows that selection of the thickness of a notional slab, h_N, is tantamount to fixing a threshold stress level for the calculation of the required overlay slab thickness, h_O. Once verification becomes reduced to an exercise of matching stresses, excellent agreement may be restored between the assumed values of h_N and the backcalculated h_O-values, using mechanistic analytical tools.

The United States government has committed to improve the nation’s preparedness and resilience to the effects of climate change by targeting net-zero greenhouse gas (GHG) emissions by 2050 and limiting global warming to 1.5°C. The Department of Defense (DOD) is supporting this initiative by developing strategic processes to ensure net-zero emissions from federal procurement. DOD’s climate adaption plan outlines five lines of efforts (LOEs); LOEs 1 and 4 are of particular interest to the pavement’s community. LOE 1 includes consideration of the social cost of GHG emissions; LOE 4 deploys GHG emissions reduction technologies. This paper explores GHG emissions reduction strategies for DOD and includes estimates of baseline GHG emissions from documented sources associated with construction and maintenance and rehabilitation of flexible pavements. Documented GHG emissions data are used to establish expected emissions reductions for each strategy. Optimized GHG reduction strategies are highlighted using an airfield pavement case study.

Airport authorities constantly collect pavement condition data and utilize life-cycle cost analysis to select construction and maintenance alternatives. The current Federal Aviation Administration (FAA) Advisory Circular 150/5380-7B recommends using pavement condition index (PCI) to assess airfield pavement condition for planning of maintenance and rehabilitation (M&R) treatments. However, structural and functional distresses initiate different pavement deterioration behavior that might not be fairly represented by one indicator, the PCI. The latter might mask the root cause of the airfield pavement deterioration by limiting the ability to focus on one failure type over the other. This results in inadequate M&R recommendations. Furthermore, the regression nature of the existing airfield pavement assessment models is not adequate for pavement performance prediction as a function of multitude features. Hence, the objective of this study was to evaluate the airfield pavement condition of key airport components (runway, taxiway, and apron) using three indices; PCI, structural condition index (SCI), and foreign object debris/damage (FOD). To achieve this objective, three machine learning algorithms, namely, ensemble-learning method, CatBoost, and LightGBM, were used to predict the SCI and FOD based on the corresponding PCI value and key project conditions such as pavement age, branch use, pavement surface type, inspection year, aircraft average operation per day, air traffic composition (%General, %Transient, %Military, and %Air Taxi aviation), mean annual temperature, annual cumulative rainfall, annual cumulative rainfall days, and annual cumulative snowfall. A total of 2,505 pavement sections obtained from 89 airport networks in seven states were included in the analysis. Based on the collected data, two models were trained (for each algorithm) and validated to predict the SCI and FOD of airport sections from the existing PCI database and other key inputs. Results indicated that the CatBoost algorithm yielded the highest accuracy; therefore, the CatBoost-based models were considered in the proposed decision-making framework. This framework is expected to advance the existing FAA PCI-based approach and expand the current airfield performance models beyond pavement age using robust techniques.

Input values used in existing airfield pavement design methods are all assumed to be consistent for 20 years of their whole pavement design life without allowing for any potential changes during the pavement life. This paper presents research efforts the Naval Facilities Engineering and Expeditionary Warfare Center (NAVFAC EXWC) made to characterize pavement damages caused by traffic condition changes such as emerging new aircraft types with excessive stresses that were not considered in the original pavement design. Selected military aircraft operations under the expeditionary environments were implemented by the California Bearing Ratio (CBR) design method in the US Department of Defense (DoD) Pavement Computer Assisted Structural Engineering (PCASE) program to characterize pavement damages which has been adopted for civilian airport pavement design as well. A procedure was developed to estimate in-service airfield pavement damages under given aircraft operations in expeditionary (military) and overload (civilian) environments and adopted for remaining pavement life computations.

Historically, the layered elastic method has been the preferred response model used in flexible pavement mechanistic-empirical methods in the pavements industry. Since this analytical procedure is restricted to linear elastic material behavior, important behavioral mechanisms driving performance such as temperature-load frequency dependency in asphalt or moisture sensitivity in unbound and subgrade layers are addressed either through modulus adjustments outside the response model or empirically in a permanent deformation model (or both). This paper provides the analytical solution to the Biot poroelastic boundary value problem for an axisymmetric multilayered system under surface loads. The Laplace-Hankel transform method is used to resolve the time and spatial partial differential equations. The inverse Laplace transform is performed using Schapery’s direct method, while the inverse Hankel transform is conducted using Gauss-Legendre quadrature. In addition to conventional formulations solving the transient consolidation problem using fully-bonded interface conditions, an interface frictional model capable of simulating partial and unbonded conditions is also presented. Interface fluid flow conditions for both continuous flow and no flow boundaries are presented. Comparisons are made to published results to verify computed responses. Results show that the model matches well with published data. Findings suggest that the proposed solution could provide improved mechanistic responses for estimating the effects of moisture sensitivity on the performance of flexible pavement structures.

The main objective of this study is to accommodate the increasing vehicular traffic through rural Simpson County, Kentucky. The study involved thoroughly designing a new interchange along Interstate 65 (I-65) and creating a connector road between the interchange and the major road arterial, US-31W. All engineering specifications were followed per the American Association of State Highway and Transportation Officials (AASHTO) “Green Book” and the Kentucky Transportation Cabinet (KYTC) design manual. A series of comprehensive analyses were performed using numerous software, including AutoCAD, OpenRoads Designer (ORD), Highway Capacity Plus (HCS+), and crash information website. Each software provided unique capabilities to aid in the full roadway design. Examples of the analyses performed included earthwork quantity estimation (either cut or fill), vertical and horizontal alignment design, level-of-service (LOS) analysis, crash and traffic safety investigation, and material cost calculations (for pavement design and construction). The benefit-to-cost (B/C) ratio selection method was then applied to assess the different design alternatives. The design revealed that the diamond interchange was the optimal interchange type in the rural study area of Simpson County, Kentucky. The total study cost was estimated to be roughly $9.5 million. From the B/C ratio analysis, it was found that it would take 3.5 years to benefit from the proposed interchange construction (right after opening the interchange to the public) in reducing fatal-related crashes and 3.16 years to benefit in reducing total crashes. This study provides useful insights and guidance for appropriately selecting and designing new interchanges and connector roads in rural areas, which can be easily transferred to other rural states in the US.

Bangladesh has made significant progress in development since gaining independence. Aggregates are essential construction materials used extensively in Bangladesh, but the reserves of natural stone aggregates in the northern region are depleting rapidly. Additionally, brick aggregates have detrimental environmental impacts. To address this scarcity of aggregates and reduce dependency on imports, this research has been conducted to explore using steel slag as an alternative construction material for pavements. The research project consisted of three phases: laboratory testing, trial pavement construction using steel slag as a sub-base material, and monitoring the performance of the road segment after one year. The laboratory testing phase demonstrated satisfactory results when comparing steel slag with regular sub-base materials typically used for pavements. Subsequently, a trial pavement was constructed using steel slag as the sub-base material following standard procedures. Field tests were carried out before and after completing different layers of pavement. After one year of service condition monitoring, visual inspections were conducted to assess distress and severity compared to regular pavements. The trial pavement performed similarly to those constructed with conventional subbase materials. This positive performance of the steel slag subbase layer suggests the potential for using slags in other layers of pavements according to specific regulations governing physical and mechanical properties. This study provides promising evidence that utilizing steel slag can help meet Bangladesh’s aggregate demand while promoting it as an alternative sustainable pavement material.

The study aimed to identify the key elements that influenced building activities during the COVID-19 pandemic and to explore strategies for addressing these issues in subsequent emergencies. A comprehensive review of the literature was conducted to pinpoint the causes of delays in construction timelines amid the pandemic. Subsequently, a survey was created using these insights and disseminated to industry experts to gather their views on the identified contributing factors. A total of 103 completed responses were received to the questionnaire, and a principal component analysis (PCA) was performed of the responses, followed by the development of a multinomial logistic regression model, using a statistical tool in SPSS. The results of the principal component analysis extracted two components, explaining approximately 68% of the variation data. Factors such as scarcity of labor force, shortage of competent workers, challenges in obtaining required materials, and limited access to heavy machinery were found to be highly influential. It was also found that two new synthesis variables, resource availability and management and coordination and operational challenges, played a critical role in explaining the variations in schedule overruns, and the scale of construction entities was found to be statistically significant in analyzing the factors contributing to schedule overruns. This study provides key findings for decision-makers and project management professionals in the construction sector, by enhancing their understanding of how to effectively manage projects during a pandemic or any similar crisis.

Despite a global emphasis on safety, construction personnel experience a sizable number of occupational injuries. The dynamic nature of construction sites makes it extremely challenging to completely eliminate the hazards associated with these injuries, but an awareness of them and acknowledgment of their potential dangers are crucial first steps in improving the safety of the workers. This paper’s goal is to add to the construction safety body of knowledge by conducting a thorough literature review and identifying, defining, and categorizing the safety hazards that affect the transportation sector of the construction industry. Through a literature review, fourteen (14) construction highway safety hazards were identified and categorized as primary, physical, chemical, and ergonomic or other, and based upon how often they were mentioned in the literature, working at heights, material and equipment handling, and heavy machinery appear to be the most hazardous to workers’ safety. Recognizing the hazards, taking preventative measures, and implementing best practices for managing them are vital to improving worker safety and reducing the severity of injuries when they do occur. The results of this study will help employers in the construction industry detect potential safety hazards and lower the likelihood that highway workers will sustain occupational accidents and injuries from them.

This paper describes status of a significant modular roundabout technology developed using USDOT and DOD SBIR funding. The resulting vortex roundabout system has many advantages over conventional road construction materials and methodology: installation time and cost each approximately half of conventional road construction methods; installs directly onto road surfaces with no modifications to existing conditions; design using Google Map images—reducing or eliminating survey costs; Lego-like instructions with individually serialized components tied to intersection map; local DOT or community maintenance crews easily install; reconfiguration possible during or after installation using carpentry tools; damaged modules replace in minutes using a socket wrench; VERY GREEN—average installation consumes about 500,000 plastic bottles; and some users reporting cost and installation time reductions of 75% compared to their initial project estimates for conventional construction. The paper will describe unique aspects of several Vortex installations, choosing interesting examples from commercial installations in VA, NE, FL, CA, and NC.

The recurring challenge of soil stiffness and strength reduction, arising from fluctuations in soil moisture, finds a promising solution in engineered water repellency (EWR). Engineered water repellency involves the transformation of in situ soil into a hydrophobic state, using organosilane (OS), which modifies the soil surface without creating cementitious bonding. This study delves into the mechanical strength of two distinct EWR-treated soil samples, subjected to wet-dry cycles simulating moisture variations. Strength determination was carried out using unconfined compressive strength (UCS) testing at a 1% per minute strain rate. A comparative evaluation was also conducted on untreated soil samples with varying moisture content. The results showed that EWR-treated samples exhibited minimal water absorption, averaging less than 4 g per cycle, and endured only a 38% decline in unconfined compressive strength after 12 cycles. In contrast, untreated samples experienced substantial strength fluctuations, reaching 73%, owing to the inherent variability in moisture levels.

Engineered water repellency (EWR) is a soil improvement method that involves intentionally modifying natural soil to become hydrophobic. Potential applications of EWR include landfill covers, frost mitigation, and moisture control in pavement systems. Designing with EWR requires knowledge of water entry pressure (WEP) and its relationship to pore size and contact angle for a given soil. In this study, a commercial grade organosilane (OS) was examined using a new methodology to establish the WEP. Soil specimens were placed in a triaxial-type cell and tested with a step-wise pressure profile from 0.7 to 117 kPa. The results indicate that for a given dosage, a relatively higher density yields the least water infiltration and a larger WEP. The maximum dry density (e.g., 95% of standard proctor effort) should be specified for field application.

Archirodon NV constructed under an EPC contract a unique, multi-purpose, 1.1 km-long low-lying sea bridge in the Red Sea coast of KSA, connecting the mainland to an isolated treasure island (Shura). The project was demanding due to the marine environment, the sensitive ecosystem, the architectural features, and the strict time schedule. Archirodon managed to deliver the bridge in 1.5 years, and the project was recognized as the MENA winner for the category of “Road Project of the Year” during the prestigious MEED Projects Award 2023. The aim of this paper is to present how project challenges were addressed in both the design and construction stages with due consideration to employer requirements and various site and environmental restrictions.

A longitudinal joint is created during a flexible pavement construction when one lane is paved while traffic flows in the adjacent one. The service life of flexible pavement depends critically on the joint quality. A poorly constructed joint can prematurely deteriorate an otherwise sound pavement. Improved joint compaction during construction can improve pavement performance with less maintenance. Current quality control and assurance plans are based on destructive testing (i.e., coring), providing limited coverage. The non-destructive density profiling system (DPS) measures the asphalt surface dielectric and offers a continuous real-time assessment of the compaction levels achieved during construction. The DPS requires calibrating and validating air voids vs. dielectric relationship for an asphalt mix, a labor-, cost-, and time-intensive process involving core extraction from pavements. This paper presents a dielectric-based longitudinal joint quality index (LJQI) that can evaluate the field compaction quality at the joint without the need for a relationship. When limited coring data are available, the index can be used as a pass/fail criterion. A minimum LJQI value of 60% is suggested for accepting a joint without penalty.

Oklahoma struggles with the widespread presence of expansive soils, incurring millions of dollars in damages to roads and constructed facilities annually. In recent years, stabilization using chemical injection has emerged as a highly efficient technique for reducing soil volume changes. Potassium-based chemical injection stabilization is a widely practiced technique to effectively reduce soil volume changes. Potassium-based chemical has a unique ability to balance the negative charge on the surface of soil without causing water retention. Introducing this innovative soil stabilization method to Oklahoma can provide practical solutions for mitigating volume change-related challenges. Therefore, the objective of this study was to assess the efficacy of potassium-based solution in Oklahoma soils. This was achieved through the collection of undisturbed soil samples from various locations throughout Oklahoma. Laboratory tests were conducted to determine the basic soil properties, such as Atterberg limits, moisture content, particle size distribution, void ratio, and density. The undisturbed samples were then subjected to one-dimensional swell tests utilizing the potassium-based chemical and water, and the corresponding swell percentages were analyzed and compared. The study yielded promising results, indicating that the chemical employed in this technique was highly effective in reducing soil swell percentage. The findings of this study are expected to encourage the construction industry to consider adopting chemical injection techniques for soil stabilization.

Raw materials depletion such as the sand used in concrete production has been a concern for the built environment. This research aimed to explore the effects of optimum coal bottom ash (CBA) and coal bottom slag (CBS) in concrete on fresh, mechanical, and durability properties. Experimental investigations were conducted to evaluate the characteristics of hardened concrete by substituting the optimum 50% fine aggregate determined from the previous study with CBA and CBS. Fresh, mechanical, and durability tests were carried out, and results were compared to control concrete. Outcomes of this research revealed that CBA-based concrete increased compressive strength at an early curing age by 74.2%; however, splitting tensile strength and flexural strength were significantly developed at later curing ages. A higher modulus of elasticity and lower chloride ion penetration severity were also established. These findings indicate that CBA and CBS can be used as a fine aggregate replacement.

This paper presents the analysis of pavement mechanistic-empirical design (PMED) climate input data for the state of Tennessee. The climatic data source considered for the analysis is Modern-Era Retrospective Analysis for Research and Application (MERRA). First, the sensitivity analysis using 2^k factorial design method, considering lower and higher extremes of each climatic input and water table, was conducted to determine the sensitivity of climatic inputs to pavement distress predictions. Then, virtual weather stations (VWSs) were created, and their predicted performance was analyzed in comparison to the existing stations. On sensitivity analysis of the enhanced integrated climatic model (EICM), temperature was the most sensitive climatic input in PMED distress predictions, while humidity had no effect on pavement distress predictions. Performance evaluation of PMED VWSs indicated a significant difference in some of the predicted distresses when comparing PMED VWSs and MERRA stations at identical locations.

The dynamic modulus of asphalt mixtures depends on temperature and the time rate of loadings. This behavior directly affects the mechanistic-empirical (M-E) of flexible pavement design procedures. The main purpose of this study was to determine the dynamic modulus, develop a master curve, and assess the impact of polymer-modified binder on asphalt mixes of flexible pavement that are used in Western Australia. Eight asphalt concrete mixtures containing various types of polymer-modified binders (PMB) were produced in a laboratory. The PMBs were set to alter the performance of regular unmodified asphalt mixtures. The effects of selecting polymers on the asphalt mixture dynamic modulus, |E*|, were investigated. The effects of temperature, loading rate, and confining pressure on the measured dynamic modulus were also analyzed. Results revealed that some of the asphalt mixtures which contain a large percentage of butadiene, elvalory, and EVA exhibit improved low-temperature properties and dynamic modulus as compared to other asphalt mixtures. However, the results of rheological tests with the other asphalt mixture performed poorly.

Hot mix asphalt (HMA) materials engineering is the process of identifying mix characteristics that reasonably reflect expected field performance. Air voids is a fundamental mix property used for the acceptance of HMA mixes. Two test methods, AASHTO T 166 and AASHTO T 275, are currently used by Caltrans to determine the bulk specific gravity of the compacted HMA mix to determine air voids in accordance with AASHTO T 269. The AASHTO T 166 procedure is required to determine bulk specific gravity for the determination of mix air voids for California Test 389 and AASHTO T 283. The current study aims to evaluate the practicality of using AASHTO T 166 (using saturated surface-dry) in lieu of AASHTO T 275 (using paraffin) for the determination of laboratory air voids for dense-graded HMA mixes as part of AASHTO T 269. This effort used data generated from a previous interlaboratory study conducted by the Caltrans Reference Sample Program. The study was done using a 3/4″ Type A HMA with 15% reclaimed asphalt pavement meeting Caltrans specifications. Seventy-six Caltrans-accredited laboratories participated in this study. Results showed that the average bulk specific gravity (Gmb) using AASHTO T 166 and T 275 were 2.475 and 2.484, respectively. The resulting air voids calculated based on theoretical maximum specific gravity (Gmm) from AASHTO T 209 were slightly higher when using AASHTO T 166 (5.21%) as compared to AASHTO T 275 (4.86%). The practical implication of this change would be that, to achieve a target 4% air voids when using AASHTO T 166 in place of AASHTO T 275, a marginal increase in binder content of 0.1% would be required for this dense-graded HMA mix. Three additional mixtures were also evaluated. The increases in binder content were: 0.05%, 0.2%, and 0.3%, respectively. It is anticipated that this trend would be valid for all HMA mixtures; however, the actual increase in binder content will be mix-specific.

In 2019, the Pavement and Materials Partnering Committee (PMPC) work product group, made up of Caltrans and industry representatives, revised California Test (CT) 125 to include provisions for random sampling and minimum sample size. The mechanical quartering device, which is one of the methods used in CT 125 Appendix B for reducing the size of hot mix asphalt (HMA) samples, may introduce variability in the testing process. Minimizing testing variability is a crucial component of quality and contract compliance. It has been observed in many cases that only a single quadrant sample of the material may be secured and used for testing while it is required to use opposite quadrants of the mechanical quartering device. There is limited data available to justify if this practice is advisable. To address this issue, the California Department of Transportation (Caltrans) Independent Assurance and Reference Sample Program conducted an interlaboratory study on the 3/4″ HMA Type A mix sample reduction using four mechanical quartering devices. Eight laboratories were selected to study the variability in the reduction process and were requested to perform a single test on four samples obtained from each mechanical quartering device. This study showed that the combined samples from opposite quadrants (either A-C or B-D) are statistically similar for AASHTO T 209, T 308, and T 30 using the t-test analysis. In the case of AASHTO T 308, all the individual quadrants were statistically similar, indicating that a single sample can be made up of any single quadrant box. In contrast, using a single quadrant box for AASHTO T 209 or T 30 proved to have statistical difference in results. This validates CT 125 requirement for mixing and reducing samples to use a combination of opposite quadrants or corners of the mechanical quartering device.

This paper investigated the use of high percentage recycled asphalt pavement (RAP) in combination with crumb rubber (CR) and ethylene-glycidyl-acrylate terpolymer for asphalt pavement maintenance applications. Hot asphalt mixtures were prepared according to the Superpave hot mix asphalt protocol. Combined mixtures were prepared with 40% RAP and single mesh size #20 CR modifier using a wet process (hereinafter CRW) and 40% RAP and ethylene-glycidyl-acrylate terpolymer (hereinafter EGA). Additionally, full RAP (100% RAP) mixtures prepared by petroleum-based rejuvenator and a conventional cold patching mixture (CPM) were studied to compare the relative mixture performances since pavement maintenance activities like pothole patching have historically been carried out with CPMs. All binders were modified using the same PG58-28 base binder. Dynamic modulus testing for linear viscoelastic characterization, flow number (FN) testing for rutting potential, and indirect tensile strength testing (ITS) for thermal cracking were conducted to assess the relative performance of the mixtures. The results revealed that the environmentally friendly and sustainable 100% hot RAP mixture and CRW and EGA combination mixtures outperformed the typical CPM in laboratory performance tests.

Thermal cracking is a vital pavement distress in cold regions such as North Dakota. Pavement ME uses creep compliance [D(t)] to predict asphalt mixtures’ long-term thermal cracking performance. Measuring D(t) in the laboratory is costly and time-intensive. This study used the interconversion procedure to generate D(t) inputs from the measured dynamic modulus (|E*|) of 10 sampled asphalt mixtures. The interconverted D(t) inputs were used to compare thermal cracking performance for levels 1, 2, and 3 of the pavement ME. Level 3 crack length predictions were underpredicted compared to interconverted levels 1 and 2, indicating that the model predicts higher D(t) values and could lead to the design of inadequate mixtures in terms of thermal cracking performance. Further evaluation using measured D(t) was recommended before adopting the interconverted D(t) values.

Sustainability is of critical consideration in road construction, maintenance, and repairs. Reclaimed asphalt binder has been proving to be a potential and sustainable solution towards road construction. During the construction and service life of a pavement, it undergoes ageing phenomenon due to exposure to environmental and traffic conditions. Hence, there are changes in its chemical as well as physical properties. Also, the use of plastic has been exponentially increasing leading to large amounts of plastic wastes being produced and issues associated with its disposal. Hence, in this study, an effort has been made to incorporate pyro-oil extracted from plastic wastes in asphalt binders. Neat bitumen (VG30) and neat bitumen modified with 50% reclaimed asphalt binder and pyro-oil (1% and 2%) have been used in this study. Fourier transform infrared spectroscopy (FTIR) has been used to characterise the changes in the chemical composition of binders after modification at unaged or fresh condition and after short term ageing. Two indices, sulfoxide and carbonyl indices, have been studied and compared as they are responsible for the ageing of bitumen.

This study explores the viability of recycled steel fiber-reinforced concrete (RSFRC) as a sustainable alternative to traditional pavement materials. Obtaining recycled steel fibers (RSF) from post-consumer tires and using them in concrete pavement reduces the use of costly asphalt material and the environmental impact of waste tires as well. The research involves experimental and numerical investigations, assessing the mechanical properties of RSFRC through compressive, split tensile, and flexural tests. Results reveal that specimens with 1.5% RSF demonstrate notable improvements, exhibiting a 10.7% increase in compressive strength, 39.3% in split tensile strength, and 10.4% in flexural strength compared to other specimens. Numerical models further highlight the load-carrying capacity advantages of RSFRC pavement sections, presenting a 17% increase over plain cement concrete sections. Additionally, cost and environmental analyses reinforce the economic and ecological benefits of incorporating RSFRC into practical infrastructure, offering valuable insights for sustainable pavement design and construction.

Thermal cracks are considered one of the most prevalent and critical forms of pavement distress. While recent studies have proven that the regression method to explain thermal cracks is not an accurate representation to quantify distress, linear models are still commonly used in engineering practices. Using long-term pavement performance (LTPP) data from 15 different road sections located in the Midwest region of the US, an artificial neural network (ANN) model was developed to predict the count of thermal cracks given the extracted input parameters: average annual temperature, annual average freeze index, 18 Kip ESAL, thermal conductivity, heat capacity, surface shortwave absorption, and coefficient of thermal contraction. The results for 7-9-9-1 ANN structure with TANSIG-LOGSIG transfer functions generated the closest thermal cracking estimate with root mean square error (RMSE) of 0.089, mean absolute percentage error (MAPE) of 0.10, and a regression coefficient (R) of 0.94, which confirmed that the model was adequate to predict thermal cracks in the pavement.

Pavement rutting negatively impacts vehicular travel by posing a safety hazard while water fills the rut and hydroplaning occurs. The ability to predict pavement rutting through an artificial neural network (ANN) can ensure rutting is fixed before severe damage to vehicles can occur and save lives. Climate, traffic, and rutting data for 15 roads in the US Midwest region were collected from the Long-Term Pavement Performance (LTPP) database to model an ANN. The LTPP data includes annual average precipitation, average temperature, average freezing index, average humidity, annual average daily truck traffic, 18-Kip ESAL, and time. The ANN model predicted growth of rut between 2012 and 2020 with R² 0.9817. For all 15 tested pavement sections, the ANN model’s R² accuracy is 0.9516. This ANN model could predict ruts in pavement sections with reasonable accuracy.

The traffic speed deflectometer (TSD) is a mobile vehicle that utilizes Doppler laser sensors to capture pavement surface deflection velocities. These velocities are then divided by the travel speed to obtain deflection slopes, which are used in pavement management tasks. Previous studies on pavement layer moduli backcalculation using TSD have often overlooked the nonlinear stress-dependency and anisotropy of pavement layers materials. This research, employing ABAQUS finite element software, explores the impact of stress-dependency and anisotropy of unbound granular materials and fine-grained soil layers on TSD deflection slopes. Simulations of a 2D-axisymmetric pavement system, considering specific material properties and layer thicknesses under uniform tire loading, reveal a substantial difference of about 40% variation in deflection slopes when nonlinear stress-dependency is considered. Comparatively, considering an anisotropy ratio of 0.5 or higher maintains deflection slope differences around 10%, while an anisotropy ratio below 0.5 leads to a 40% variation. This study emphasizes that overlooking stress-dependency and anisotropy of pavement layers materials in analyzing TSD data in pavement layers’ backcalculation is inadequate.

This study explores the viability of integrating waste plastic into road construction. Various proportions of waste plastic were incorporated into asphalt binders, followed by a comprehensive battery of traditional and advanced characterization tests on both the plastic-modified and unmodified asphalt binders, along with hot mix asphalt (HMA) samples. The addition of waste plastic notably enhanced the viscosity-temperature susceptibility (VTS), rheological properties of asphalt binders, and the stability of HMA. HMA utilizing waste plastic-modified asphalt binders exhibited characteristics akin to gap-graded hot mix asphalts, suitable for low-traffic flexible pavements and noise-reducing friction courses in rigid pavements. While further research is necessary to ascertain optimal waste plastic quantities for diverse road types, the findings illuminate the potential for integrating waste plastics into pavement construction, offering prospects for sustainable road infrastructure.

Aging alters asphalt binders’ chemical and mechanical characteristics, contributing to pavement distress such as cracking, raveling, and moisture-induced damage. Aging is primarily driven by a reduction in the maltene phase due to the oxidation of specific functional groups within the asphalt binder. Oxygen diffusion, which elevates the concentration of polar components within the binder, leads to an overall embrittlement. To this end, the oxidative aging indices of two polymer-modified (PG 64-28 and PG 58-34) and a non-modified (PG 58-28) asphalt binder were determined through a chemical analysis. In addition, the mechanical properties of the asphalt binders subjected to aging were evaluated. To accomplish this goal, asphalt binders were subjected to aging in a rolling thin-film oven and a pressure aging vessel to simulate construction and service aging conditions, respectively. The evolution of oxidative aging was analyzed based on the chemical aging indices from Fourier-transform infrared spectroscopy and the rheological aging index using a dynamic shear rheometer. The outcomes of this study are expected to help with understanding different mechanisms involved in the aging of polymer-modified and non-modified asphalt binders and the methods used to quantify them.

Pavement mechanistic-empirical design (PMED) is a state-of-the-art analysis and design tool for flexible and rigid pavements. The transfer functions in the PMED are nationally calibrated and may provide unrealistic predictions for the local conditions, leading to under-design or over-design of pavement layer thicknesses. Therefore, the PMED models need recalibration for local conditions. Several studies have recalibrated the transfer functions for local conditions using the least square (LS) approach. Although LS is a widely used approach, it requires certain assumptions that may not be valid for non-normal distributions. This study uses the maximum likelihood estimate (MLE) approach to calibrate the transfer functions for PMED v2.6. The MLE approach optimizes a given function by matching predictions with a known distribution. MLE used four probability distributions: exponential, gamma, log-normal, and negative binomial. The paper presents the calibration of local transverse and bottom-up cracking for rigid and flexible, respectively. A total of 54 and 78 pavement sections for rigid and flexible pavements, respectively, are selected from the Michigan Department of Transportation (MDOT) Pavement Management System (PMS) database. The selection is based on the observed performance and availability of the PMED inputs. The results show that MLE outperformed the LS approach for both cracking models. MLE provides more robust calibration coefficients, especially for non-normal data distributions.

This research aims to investigate calcium oxychloride (CAOXY) formation in concrete pavement joints due to the interaction between calcium hydroxide [Ca(OH)₂] and chloride ions (Cl²⁻) from deicing salts, notably calcium chloride (CaCl₂). Common in the US Midwest, CAOXY induces joint deterioration in heavily deiced concrete pavements. Unlike previous studies, our focus is on understanding the combined effects of key parameters, including water-to-cementitious ratio (W/CM), salt solution concentration, age, and temperature, on CAOXY formation. The primary goal is to develop an energy-based model, emphasizing temperature and salt concentration. Visual observation, analysis, and thermogravimetric analysis (TGA) reveal temperature, along with salt concentration, as critical factors causing harmful CAOXY formation and joint deterioration in concrete pavement. The findings of this study are expected to be useful for developing strategies to reduce the CAOXY formation and prevent the deterioration of concrete pavement joints, especially in regions where deicers are frequently used.

Recently, link slab was proposed to replace the conventional dowelled-joints in jointed plain concrete pavements (JPCP) after it was previously employed in bridge expansion joints to avoid the problems associated with dowel bar joints. This study introduces numerical modeling of the proposed link slab expansion joints that simulates the joint behavior and provides the basis for optimal joint design using Abaqus software. The generated model and its boundary conditions are based on recent experiments conducted on the proposed joint. The model consists of two conventional concrete blocks which represent two slabs of JPCP and ultra-high performance concrete (UHPC) link slab that is connected to JPCP using double headed shear studs. The materials involved in the model are concrete damage plasticity to model conventional concrete and UHPC, and elastic-plastic material to model Grade 40 steel shear studs that connect the UHPC link slab to the conventional concrete slabs. The numerical model simulated the experiments very well where it showed accuracy that ranged from 87% to 100% and from 82% to 92% for load-displacement behavior, and load transfer efficiency (LTE), respectively. Also, the failure mode was captured very well in the numerical model.

Moisture-induced damage is one of the major distresses which contribute to significant premature deterioration of asphalt pavements in Oklahoma. Oklahoma Department of Transportation (ODOT) is using tensile strength ratio (TSR) from the AASHTO T 283 test for screening of mixes for moisture-induced damage. Also, recently ODOT has started using cracking tolerance index (CTindex) from the indirect tensile asphalt cracking test (IDEAL-CT) for screening of asphalt mixes for fatigue cracking. The testing mechanism of the IDEAL-CT is similar to the AASHTO T 283 test method. However, the IDEAL-CT test requires specimens with a height of 62-mm, whereas the specimen height of the AASHTO T 283 is 95-mm. By using specimens of similar dimension, the data obtained from the IDEAL-CT test can be readily used for the calculation of TSR. In this study, the effect of changing the specimen height from 95-mm to 62-mm on the current AASHTO T 283 method was explored. For this purpose, the cracking and moisture-induced damage potential of three asphalt mixes with 62- and 95-mm specimen heights was evaluated in the laboratory. The test results indicated that the indirect tensile strength (ITS) of specimens vary with specimen height. Also, the type of mix (coarser vs. finer) was observed to affect the changes in the ITS value with specimen height. It is expected that the findings of this study will help modify the current AASHTO T 283 test method and allow an integrated screening of fatigue cracking and moisture-induced damage for asphalt mixes.

The application of reclaimed asphalt pavement (RAP) materials in the pavement construction industry has been on an upward trajectory due to cost considerations and the need for sustainability. The scarcity of road construction materials may be effectively addressed by integrating RAP in developing countries like Bangladesh. This paper provides a comprehensive and insightful evaluation of RAP utilization alongside natural aggregate for road construction purposes. Various recycling processes have been applied across the world, and this study emphasizes the hot mix recycling method and its potential use for the construction of new pavement layers. In this study, recycled aggregates from six different locations show slightly downgraded properties but still fall within specified limits. Consequently, recovered bitumen from the rotary evaporator performs satisfactorily in rheological testing, although oxidation and aging have caused variations in some of its properties. This paper determines the influence of using different RAP percentages (0%, 30%, 50%, 70%, and 100%) and bitumen content (0%−6.5%) in hot mix asphalt (HMA) on the Marshall test results. Incorporating 70% reclaimed asphalt pavement (RAP) yields the greatest strength, while concurrently requiring an addition of bitumen content of 0.8%−1.5%. The research concludes that recycled pavement materials can produce stable and durable bituminous mixes, contributing to sustainable infrastructure development.

Proficiency testing is used for assessing, ensuring, and improving the quality of laboratory test results using consistent samples with known characteristics. The results submitted by participating laboratories verify the accuracy of laboratory equipment, testing practices, technician competence, and accuracy of test results. As defined by 23 CFR 637 B, proficiency samples are homogeneous samples that are distributed and tested by two or more laboratories; the test results are compared to ensure that the laboratories are obtaining comparable results within an expected range. Laboratory and tester’s ability to test materials and provide accurate test results are evaluated through proficiency testing with the California Department of Transportation (Caltrans) Reference Sample Program (RSP). Participation in Caltrans RSP is mandatory to maintain laboratory accreditation under the Caltrans Independent Assurance program. In recent years, Caltrans RSP has been used to conduct interlaboratory studies (ILS) whenever there is a concern regarding variability in test results both from Caltrans and the construction industry. Caltrans ILS is an applied research program that supports special studies requested by the Caltrans-Industry Pavement and Materials Partnering Committee (PMPC) and/or Caltrans Materials Engineering and Testing Services (METS) technical committees. The ILS plays an important role in quality improvements to materials testing by assisting with the development of new test methods and developing precision statements for various existing test methods. These precision statements are developed based on the averages and standard deviations of data collected from both ILS and RSP. Tests having standard deviations not meeting the expected precisions are considered for re-examination. Overall, the goal of ILS is to make data-driven decisions on test methods and potential changes to the standard specifications. This paper discusses an overview of proficiency testing with Caltrans RSP, recent RSPs with a scale of laboratory participation, and the role of RSP in conducting ILS. In addition, the paper also discusses how other state agencies can benefit from the lessons learned from Caltrans.

The laboratory-determined resilient moduli (M_R) of various unbound pavement layers, obtained from multiple sections of six different projects, were compared with moduli determined using in situ field tests, including falling weight deflectometer (FWD), lightweight deflectometer (LWD), and direct cone penetrometer (DCP). While the FWD deflections were measured over the finished pavement surfaces, the LWD and DCP tests were conducted on the surface of the compacted unbound layers. The laboratory subgrade M_R values were comparable to the median FWD- and DCP-determined moduli. Similarly, the subbase and base layer M_R values were in close agreement with the FWD backcalculated moduli. Plots are presented to estimate potential correlations between the lab- and field test-based layer moduli. When the exact sampling locations were unavailable for these correlations, the median moduli estimated from the different field tests were compared to their laboratory values. Correction factors were obtained without accurate field vs. laboratory correlation to estimate different layer moduli from the field-testing data. The comparison showed that among the three field tests evaluated, the FWD backcalculation moduli results with negligible bias and standard error compared to their lab-determined values, especially for the base layers that require little to no correction. This finding suggests FWD testing over the finished pavement surface reasonably estimates the unbound layer moduli for use in design. The LWD and DCP testing may quantify individual layers’ density to identify the relatively weaker areas during construction.

There is growing interest in adding some reclaimed asphalt pavement (RAP) to rubberized hot mix asphalt (RHMA). However, the binder from RAP is expected to reduce the need for recycled tire rubber. Therefore, the coarse RAP can be utilized as an aggregate replacement with minimal binder replacement. Based on industry feedback, it was estimated that the additional binder mobilized and blended from coarse RAP in plant-produced mixes is between 0.2% and 0.5% by the total mass of mix. According to that, binder blends were prepared in the laboratory by adding aged binder from coarse RAP to rubberized binder to evaluate the effect of RAP binder on the performance of RHMA. It was found from this study that the addition of RAP to the binder somewhat negatively affects the low-temperature grade and appears to make it less able to relax thermal contraction stresses at cold temperatures. The addition of RAP increases the amount of elastic recovery of the mix, which is expected to increase rutting resistance. The intermediate-temperature grade does not appear as sensitive to increases in a small percentage of RAP binder as the high- and low-temperature grades.

This study investigates the impact of heat capacity of paving concrete on the performance and design slab thickness of Jointed Plain Concrete Pavement (JPCP). The thermal properties of paving concrete play a critical role in determining the response of pavements to temperature variations, affecting their durability and structural integrity. The methodology involved simulating varying levels of heat capacity in concrete mixtures to observe corresponding changes in pavement performance predictions. The critical performance parameters were evaluated for varying heat capacity values including transverse cracking, joint faulting, and pavement roughness. It was found that heat capacity of concrete significantly impacts cracking and pavement roughness with a higher heat capacity resulting in better performance. In phase 2 of this study, a comparison was made between the JPCP performance of laboratory measured heat capacity values and the default values, and it was found that the laboratory measured heat capacity values consistently lead to higher cracking and higher pavement roughness for all of the simulated mixtures. The results indicate that using the baseline/default values of heat capacity in the JPCP design process may result in underdesigned JPCP system which might fail prematurely without completing the design service life. In phase 3 of this study, analysis was conducted to quantify the impact of baseline heat capacity vs. laboratory measured heat capacity on the slab thickness of JPCP system, and it was found that the difference between using the laboratory determined heat capacity and the PMED default/baseline heat capacity is 1 in. of PCC slab thickness which has numerous financial, sustainability, and environmental implications. The thermal properties of concrete may play an increasingly important role in pavement performance as the effects of climate change become more pronounced. It is highly recommended that JPCP design should be conducted according to the laboratory measured concrete properties including heat capacity values.

To provide sufficient bonding between two layers of a pavement, a tack coat material is applied on top of one layer at an optimal application rate, and next, the tack coat material is subjected to curing prior to the placement of the next layer. Though different researchers have prescribed different application rates and curing times, neither a concise value of optimal application rate nor curing time can be found in asphalt literature. To that end, this study undertook a comprehensive laboratory investigation to determine the optimum application rate and curing time for a slow-setting anionic emulsified tack coat (SS-1h) using a direct shear test. The test results demonstrate that the shear strength initially increases as the tack application rate increases until it reaches a peak value. Subsequently, the shear strength decreases with the application rate. The decrease in shear strength can be attributed to the presence of an excessive amount of tack coat, resulting in the formation of a slip plane. A second-order polynomial model was employed to fit the shear strength vs. % track coat. An optimum application rate is determined by setting zero value to the first derivative of the polynomial. The recommended application rate for the tested tack coat, in terms of residual asphalt content, is found to be 0.045 gallons per square yard. Regarding the curing time, it was observed that the shear strength exhibits a rapid initial increase as the curing time progresses, eventually reaching a plateau. This phenomenon can be attributed to the existence of a direct correlation between the quantity of water evaporation and shear strength. It was observed that complete evaporation to reach plateau value requires at least 2 h after the initial application of the tack coat. Therefore, it is recommended that the curing process should be carried out for a minimum duration of 2 h for this emulsion.

This study analyzes the effect of compaction temperature of full depth reclamation materials using Hamburg Wheel Tracking Test (HWTT) and semi-circular bending test (SCB). Hamburg Wheel Tracking Test (HWTT) is a test method related to the permanent deformation of FDR material. Semi-circular bending test (SCB) is related to fracture energy of FDR. Results show that increase in compaction temperature led to a smaller permanent deformation. Higher compaction temperatures lead to higher fracture energy needed to an initiate crack.

Pavement foundation stiffness is a critical parameter for pavement design and improving pavement durability. When the subgrade soil is subjected to repeated traffic loading, the initial nonlinearity behavior and permanent strain of subgrade per load repetition are high. As the number of load repetitions increases, the subgrade strain decreases, and it begins to behave more elastically. This condition allows for a more accurate measurement of the resilient modulus (M_R) value of subgrade soils. The stress-strain behavior of the soil depends on several factors such as the index properties, stress history, and confining pressure. In this study, the stress-strain responses of sand and fine soils are thoroughly investigated via laboratory-resilient modulus tests, and the shakedown theory was adopted to evaluate the stress-strain response of four subgrade soils. A total of four different subgrade soils were tested. The results showed that the sandy soil exhibited a quasi-brittle behavior characterized by sudden failure when the applied stress exceeded the recoverable strain. The resilient behavior of this material was highly dependent on the confining pressure. The fine materials showed stress-softening behavior and were less dependent on confining pressure. The fine soil exhibited the behavior of over-consolidated soils when the material was subjected to cyclic loading at low confining pressure. The analysis of permanent strain revealed that the summary resilient modulus (SM_R) measured for the soils with the lowest fines contents did not accurately reflect the SM_R of the material. This material continued to exhibit high permanent strain when the SM_R was measured. This suggests that the initially calculated SM_R may not be indicative of the material’s stiffness.

Accurate cost estimation has been a notable challenge for developing cost-effective highway maintenance plans. While multiple variables, including market indices, have been used to attain higher accuracy of cost estimation models, the difficulty escalates for long-term forecasting. Especially in the prediction of early-stage projects, the limited amount of information and the unpredictability of market conditions make it intricate to achieve the desired accuracy. This paper applies machine learning (ML) to develop a highway maintenance cost forecasting model using only the early-stage project details. Georgia Department of Transportation (GDOT) pavement data from 2017 to 2021, consisting of “bidding-related,” “asset-related,” and “project-related” variables, is input to ML methods including linear regression, random forest, extra trees, gradient descent, extreme gradient descent, and k-nearest neighbors. Extra trees regression showed the lowest mean absolute percentage error of 18.51% after fivefold cross-validation. This study will work as a reference for DOTs to implement ML in the long-term maintenance cost prediction and ultimately be utilized in the tradeoff analysis of optimal management strategies.

The Oklahoma Department of Transportation (ODOT) embarked on a phased implementation of balanced mix design (BMD) starting in 2018. This involved initial shadow projects (Phase I) and a proof of concept stage (Phase 2) with pilot projects, followed by long-term evaluation and implementation in Phase 3 and Phase 4, respectively. The goal of this study is to evaluate the field performance of BMD mixes used in Oklahoma. Cutting-edge 3D laser imaging technology was used to collect pavement condition data from various BMD sites, including metrics such as percentage cracking, international roughness index (IRI), rut depth, and mean profile depth. Firstly, a comparative analysis directly compared traditional Superpave mixes to BMD mixes using hypothesis testing. Secondly, among BMD mixes, the study analyzed performance variations resulting from different mix constituents. Lastly, statistical regression analysis and machine learning techniques (gradient boosting algorithm and random forest models) were used to pinpoint the most influential factors affecting field performance. The findings are expected to offer insights into the successful application of BMD while contributing to a deeper understanding of pavement design and performance on a broader scale.

There is a national effort, including the Oklahoma Department of Transportation (ODOT), to move toward a balanced mix design (BMD) approach for the design of asphalt mixtures. This paper presents the findings of ODOT’s Phase I implementation of BMD in Oklahoma, whose objective was to evaluate selected ODOT mixtures to assist with determining test procedures, specifications, and special provisions for evaluation and eventual adaptation of a BMD procedure for ODOT. The test plan consisted of evaluating I-FIT test results from 31 existing mixes and I-FIT and Ideal CT_Index tests from nine mixes from three ODOT projects, consisting of three control mixes and six BMD mixes. Inconsistent results were found from the testing within and between the data sets. BMD mixes differed from control mixes by the addition of RAP, increased VMA, and increased asphalt content. Many of these high VMA BMD mixes collapsed in the field resulting in void properties below specification limits. It is recommended that ODOT consider adopting the Ideal CT test due to ease of testing and perform this testing on all mix designs submitted for use on ODOT projects.

One of the challenges for local/regional governments is acquiring federal/state funding for pavement repair and/or replacement projects. Each project requires pavement distress data collection to justify the need. Current methods of data collection are either expensive or time-consuming. This research presents a unique approach for automatic pothole and crack recognition utilizing a deep learning framework to optimize cost and time. In this research, we developed a masked autoencoder (MAE) model with vision transformer (ViT) based architecture as the encoder backbone. We first pre-trained the MAE on the ImageNet-1K (IN1K) training set. Then, we further fine-tuned this pre-trained MAE to capture the road features using a custom image dataset from the Center for Research and Education in Advanced Transportation Systems (CREATES), Rowan University, New Jersey. This dataset represents the roadway network in southern New Jersey. The primary purpose of fine-tuning the MAE model on our custom dataset was to generate relatable low-dimensional embeddings from road images representing potholes, cracks, or normal roads. Following the completion of this training, we integrated the obtained encoder into a linear probing scheme to perform a classification task. We added the classification layers to the end of the ViT-based encoder. The classification layers included two cases: (1) potholes vs. no pothole and (2) no cracks vs. moderate cracks vs. severe cracks. The ViT architecture supported the extraction of complicated spatial data, while the classification layers allowed for exact categorization. The combined model was adjusted to better match the encoder’s attributes to the purpose of anomaly classification. We compared our proposed model with pre-trained and finetuned version of ResNet18 and MobileNet V3 Small and Googlenet. The proposed model outperformed convolutional neural networks (CNNs) based classification approaches in classifying imperfections in the road surface. The model’s performance was rigorously evaluated using a variety of real-world road images, with promising results in terms of efficiency and accuracy.

Improper skid resistance can lead to road accidents and driving discomfort. The study examined the effectiveness of 0.75-in. OGFC in ensuring skid resistance on New Mexico roads, categorizing them into interstate, US national, and state roads. Field friction/skid testing was conducted monthly on 1-mi sections of each road. Data were analyzed for skid resistance or skid number (SN) values. Overall, a poor correlation between the peak SN and SN value at 100% slip was observed. The peak SN values for I-10, I-25, and I-40 are found to be 77.44, 77.725, and 56.04, respectively. The peak SN values for US-60, US-70, and US-180 are 89.95, 31.32, and 87.93, respectively. The peak SN values for NM-04, NM-38, and NM-80 are 56.04, 16.17, and 84.77, respectively. No significant pattern in SN value can be observed among these three groups. In general, an SN value of more than 35 is considered adequate. The SN values at 100% slip are 82.95, 69.52, and 42.5 for I-10, I-25, and I-40; 39.92, 32.99, and 47.78 for US-60, US-70, and US-180; and 29.3, 56.9, and 59.42 for NM-04, NM-38, and NM-80, respectively. Average SN values of interstate roads are slightly higher than the US and NM roads; however, the difference is statistically insignificant. The study suggests that current road designs, surfaced with OGFCs, may not account for the different traffic speeds and volumes, indicating a need for tailored SN values for improved safety and comfort.

One of the key factors in developing an understanding of compaction mechanics is the interfacial friction between the binder and aggregates, and tribological investigations are very useful in this regard. In this study, tribological tests are performed for a range of angular velocities and normal forces such as 5 N, 10 N, and 15 N. Stribeck curves are developed for one unmodified bitumen and two modified bitumen (elastomer and plastomer) at three temperatures of 120°C, 140°C, and 160°C. It is observed that the unmodified binder shows more coefficient of friction than modified binders irrespective of the temperature, whereas the modified bitumen exhibits responses that are dependent on temperature. By studying the different regimes of the Stribeck curve for the different binders, temperatures, and normal forces, it is possible to understand the response of asphalt binders during mixing and compaction.

The use of waste materials in asphalt mixtures has become a popular way of promoting sustainability and reducing environmental impact. Waste plastic is one such material, a major component of solid waste which is abundantly available and disposed of without proper treatment. Waste plastic was found to be an effective modifier for bitumen mixes to be used in flexible pavements. Plastic roads can prove to be a boon for India’s extremely hot and humid climate, where ambient temperatures frequently cross 50°C, and heavy rainfall create major moisture damage in pavements. Low-density polyethylene (LDPE) plastic waste is one such material that has been investigated as a potential additive in asphalt mixtures. In this research, LDPE waste plastic of size less than 2.36 mm is used to modify the bituminous mixture using a dry process. The objective of the study is to compare volumetric parameters of waste plastic asphalt mixture with control asphalt mixtures and to evaluate the effect of LDPE waste plastic on moisture susceptibility of asphalt mixtures. The waste plastic was mixed with the aggregates at 155°C−160°C for proper coating of waste plastic on the aggregates, and the coating was quantified using MATLAB software. In this study, an attempt is made to quantify the reduction in optimum binder content due to the coating of waste plastics on the aggregate surface. Waste plastic-based asphalt mixes showed improved resistance to moisture susceptibility than the control mixture.

This research paper focuses on addressing the environmental impact of the cement industry and road construction waste. It explores the use of chemical additives alongside recycled asphalt and cement components in the base layer. The study examines various aspects, including strength, moisture compaction interaction, and microstructural characteristics. The results indicate that incorporating silica-based chemical additives has positively influenced the mechanical properties of the mixture. The addition of this additive led to an enhancement in the intensity of the calcium silicate hydrate gel, as revealed by microstructural analysis, which is accountable for the improved strength of the mixture. Moreover, the design displayed durability, with minimal ettringite formation in later stages. Overall, the application of recycled asphalt pavement and additives offers a robust, eco-friendly, and long-lasting base layer solution.

This study is to evaluate pedestrian bridges on important arterial routes in Karachi, such as University Road, MA Jinnah Road, Rashid Minhas Road, and Sharah e Faisal. The primary goal of the study is to gather and evaluate information about Karachi’s pedestrian bridges and their safety, as well as suggest preventative strategies to decrease pedestrian accidents. The study’s purview encompasses a range of qualitative aspects, including location, condition, and safety levels, that affect how pedestrian bridges are used. Additionally, it investigates the reasons why people don’t use pedestrian bridges and investigates possible fixes to encourage more people to use them. A questionnaire survey was conducted as part of the data collection process, which focused on impressions of the safety and location of the bridge, pedestrian behavior, and demographics. The survey provided information about pedestrian behaviors, including the respondents’ age, gender, level of education, and the existence of any illnesses that would limit their mobility. Later, the gathered data was analyzed using the Statistical Package for Social Sciences (SPSS) software.

The constant wear and tear experienced by rail surfaces due to continuous loading necessitates repairs for maintaining optimal performance within railway networks. Traditional repair methods are time-consuming and costly, motivating the exploration of innovative alternatives. This study focuses on the development of a finite element (FE) model to simulate the submerged arc welding (SAW) process, which serves as an additive manufacturing technique for restoring 136-lb/yd (136RE) rails, commonly utilized in heavy freight and passenger rail systems across the United States. A series of experimental laboratory investigations were conducted to validate the developed FE model. A worn section of the 136RE rail was carefully chosen for this study. After the rail’s surface underwent milling and flattening, the submerged arc welding process was employed to rebuild the rail, utilizing a 1/8-in. Lincore 40-S depositing wire. The reconstructed rail sample was then subjected to experimental tests, including tensile testing, which provided the essential mechanical properties required to validate the simulation process. The FE model encompasses all conceivable interactions, including thermal, mechanical, and phase transformations. This simulation employs an element-birth-and-kill method, examining the thermal distribution within the sample across different sections. By considering the thermal history and phase change relations, the model predicts the mechanical properties of the repaired rail. The validated model showcases substantial potential in exploring and predicting mechanical properties and thermal distribution during the SAW process for heavy rail repair.

This research investigates the quality and accuracy of the 2022 contributions to a geolocated database of culverts created by New Mexico Tech for the New Mexico Department of Transportation (NMDOT). The database was collected by trained field inspectors and reviewed by a quality control team. The database includes physical descriptions and condition appraisals for 11,499 culverts in fields, such as GPS location, material, span, end section damage, and erosion control. A random sample of 1,059 culverts, 9.2% of those collected, were analyzed, and 53 errors were clearly identified among 38 different culverts. 3.6% of culverts contain at least one error. GPS locations were required to be within 1.5 m (5 ft) horizontal accuracy; 7.0% of the total population (11,499) of culverts had a horizontal accuracy greater than 1.5 m (5 ft). Errors in the database affect culvert prioritization for maintenance and assessment by NMDOT. The analysis provides insights into how to improve the appraisal process and minimize future errors.

Bridges are considered one of the most critical infrastructure types in transportation networks requiring a significant amount of money to maintain. To better maintain bridges, it is essential to be able to predict their conditions to ensure that maintenance activities are scheduled at the correct time. Prior research was conducted to predict the condition of individual bridge components, such as decks. However, a more holistic approach that can predict the condition of multiple bridge components can provide richer insights for informed decision-making. Such an approach has been relatively underexplored by the research community as predicting the conditions of multiple components of bridges simultaneously has not been investigated thoroughly. This research addresses the gap by harnessing the power of multi-target machine learning (MTML) algorithms to predict the condition of two core elements of the bridge, namely the deck and substructure, simultaneously. Using a dataset provided by the Ohio Department of Transportation (ODOT), ensemble ML algorithms, including XGboost, and random forest (RF) were implemented to predict the deterioration of the deck and substructure simultaneously. This study is particularly unique because it showcases the effectiveness of MTML algorithms for bridge condition prediction, a distinction from conventional single-target ML models. MTML algorithms, unlike their single-target counterparts, have the intrinsic capability to model and predict multiple interdependent variables concurrently. Moreover, MTML algorithms allow for capturing intricate relationships and dependencies among various components, and thus enhance predictive accuracy. ML algorithms were used for (1) selecting features and (2) predicting condition. For feature selection, the RF algorithm performed best on the training set, while the XGboost performed best on the test set. For condition prediction, the XGboost achieved the highest accuracy on both training and test sets with an average accuracy of 90.343% and 83.891%, respectively. Findings from this research can provide more insight to bridge owners to simultaneously predict the condition of multiple bridge components, thereby allowing them to allocate the maintenance budget more effectively.

Design, construction, and maintenance of off-system bridges differ among local governments (i.e., cities and counties). Bridge inspection reports reveal that off-system bridges, owned and maintained by local governments, exhibit inferior performance compared to those owned and maintained by state highway agencies. However, the lack of knowledge about the design, construction, and maintenance procedures related to off-system bridges in local governments hinders understanding the reasons behind this deficiency. This lack of knowledge poses challenges for state highway agencies to provide necessary guidance regarding the off-system bridges in their continued partnerships with local governments. This research aims to synthesize the current procedures related to the design, construction, and maintenance of Texas off-system bridges managed by local governments—including the utilized bridge-related design manuals and standards. Findings from fact-finding surveys and semi-structured follow-up interviews revealed deficiencies in certain existing practices. For instance, several local governments highlighted the absence of any mandatory design criteria (i.e., specific design manual or standard) for developers when designing or constructing bridges. The findings of this research are expected to enhance the overall performance of off-system bridges in local governments by providing insights into best practices for design, construction, and maintenance, as well as identifying possible areas for improvement.

Physics-based predictive models for identifying rainfall-induced slope susceptibilities typically consider slope geometry, soil type and properties, and precipitation. However, these models do not often account for the impact of land cover (e.g., retaining structures, vegetation, wetlands, and water bodies) on slope susceptibilities. The presence of retaining structures and vegetation can improve slope stability, while exposure of slope to water bodies can adversely degrade slope stability. Existing data sources, such as the US Department of Agriculture (USDA) and Texas Natural Resources Information System (TNRIS) databases, lack sufficient land cover information, making it challenging to derive the necessary data. The objectives of this research are to develop a satellite and street imagery-informed calibration method and integrate it with physics-based models to incorporate the effects of land cover on slope stability. The methodology comprises creating a formal workflow for collecting images along highway corridors through high-resolution satellite and street images, classifying the condition of land cover through assessment of these images, and developing the polygon feature layer representing the extent of different land covers. This method is integrated with an existing physics-based model by transforming the polygon feature layer into a raster file and conducting a raster overlay analysis of the slope susceptibility maps to update the susceptibility levels of slopes. This integration method was implemented along the highway corridors in Texas. Over 10,000 slope assessments were calibrated utilizing satellite and street imagery to identify instances of water-eroded and mechanically stabilized slopes. Consequently, the susceptibility levels in these locations were adjusted to account for the impact of the observed land cover conditions. This integration method enhances the accuracy of slope failure susceptibility maps, enabling transportation agencies to identify critical slope segments and plan proactive slope maintenance initiatives more effectively.

The reliability of electric vehicle (EV) charging infrastructure during natural disasters is paramount for ensuring effective evacuation and response strategies, especially in regions vulnerable to such events. As EVs increasingly dominate the transportation landscape, disruptions to their essential infrastructure, such as charging stations, can profoundly hinder evacuation efficiency and safety. This research delves into the vulnerabilities of the EV charging network during a county-wide evacuation in Florida, a state regularly confronted by hurricanes and tropical storms. Using a multilayered network analysis, the study focuses on the electric grid robustness, shedding light on intricate interdependencies within the network. Central to this investigation are resiliency metrics that quantify the electric grid and charging stations’ functionality during and after natural disasters. Real-world data on EV ownership, supporting infrastructure, and historical disaster impacts further refine our realistic simulation scenarios. Our findings spotlight potential critical vulnerabilities across the electric infrastructure that may impact EV evacuation efforts during a natural disaster. These insights are poised to guide policymakers, urban planners, and emergency response teams in crafting strategies that fortify Florida’s EV charging infrastructure’s resilience during expansive evacuations.