Showing papers in "Journal of transportation engineering in 2022"

Assessment of Intelligent Compaction Quality Evaluation Index and Uniformity

Abstract: Compaction Measurement Value (CMV) is the most widely used quality evaluation index for intelligent compaction. However, with the growth of compaction degree, CMV becomes less accurate due to increased reaction force from densified geomaterials. To solve this problem, this paper proposes a more accurate intelligent compaction quality evaluation index, acceleration intelligent compaction value (AICV). Based on field measurement data analysis, AICV turned out to be effective to elevate quality evaluation accuracy. Additionally, the singularities were detected by 3σ normal distribution in order to assess the uniformity of subgrade compaction quality. Based on the semivariogram analysis method of spatial statistics, CMV and AICV were analyzed for spatial correlation, and the influence range of intelligent compaction technology employed for subgrade was evaluated. The results from this study provide a basis for further research on precision control and uniformity analysis of intelligent compaction technology.

Correlating the Asphalt-Binder MSCR Test Results to the HMA HWTT and Field Rutting Performance

Abstract: Asphalt binder is one of the key constitutive components of hot-mix asphalt (HMA) that considerably affects its rutting performance. In particular, the high-temperature rheological properties measured from the multiple stress creep and recovery (MSCR) test are critical for correlating to the HMA rutting resistance. In this study, the Texas flexible pavements and overlays database was used as the data source to investigate the effect of asphalt-binder high-temperature rheological properties on the HMA rutting resistance. The study methodology was based on correlating the results of the MSCR test and the Hamburg wheel-tracking test (HWTT) to HMA field rutting performance. The data matrix for the study included asphalt binder (PG 64-22) from three different sources, three widely used Texas HMA mixes (fine gradation to coarse gradation), and five in-service highway test sections constructed using the same asphalt binders and HMA mixes. In general, the MSCR nonrecoverable creep compliance parameter, Jnrdiff, showed fairly strong correlations with the HMA rutting performance in the laboratory and field. The percent recovery parameter (R), on the other hand, exhibited the potential to ascertain and quantify the presence of modifiers in the asphalt binders. Furthermore, the test results indicated that material source/supplier has an impact on the rheological properties of the asphalt binders with the same performance grade (PG). Overall, the use of the MSCR test to quantify the asphalt-binder high-temperature rheological properties indicated the potential to compliment the laboratory HWTT test for correlating with the field HMA rutting performance in terms of the effects of asphalt binder.

Mechanical Response of Asphalt Surfaces under Moving Traffic Loads Using 3D Discrete Element Method

Abstract: This paper investigates the mechanical response of asphalt surfaces under moving traffic loads using the three-dimensional (3D) discrete element method (DEM). As an example of a semirigid base asphalt pavement, a discrete element model for asphalt surface was established based on the random generation algorithm of irregular particles in Python language and DEM. The model considered the temperature gradient and fatigue damage to simulate the permanent deformations, shear stresses, and strains in asphalt surfaces under different working conditions (e.g., different temperatures and numbers of repeated loads). Part of the simulation results was verified by performing a full-scale accelerated loading test (ALT). Results show that the 3D discrete element model embedded with temperature gradient and fatigue damage could be used to predict the mechanical response of asphalt surfaces under repeated loads. As the temperature increased, the mechanical response of asphalt surfaces increased. The middle surface was the main area of shear stresses in semirigid base asphalt pavements. Due to fatigue damage, the stresses and strains in asphalt surfaces increased with the number of repeated loads.

Performance Evaluation of Fly Ash–Based Inverted Pavement System

Abstract: The inverted pavement system is an alternate type of pavement system compared to rigid and flexible pavement systems. The base layer of inverted pavements is generally a cement-treated layer with varied cement content, depending on the unconfined compressive strength criteria and durability. In the present study, fly ash was used as a replacement for aggregate in the cemented base layer; in the cemented subbase layer, only fly ash and cement were used. An optimized combination of fly ash (22%), aggregate (78%), and cement (3%) was used for the cemented base layer. For the cemented subbase layer, 7% cement and 93% fly ash were used. Therefore, 22% aggregate in cemented base and 100% aggregate in cemented subbase layer can be saved. For the field investigation, a test track was constructed for 0.5 million standard axles (MSA), and performance was monitored with both nondestructive testing (NDT), that is, falling weight deflectometer (FWD), Benkelman beam deflection (BBD), and ultrasonic pulse velocity (UPV), and destructive testing (actual loading, plate load test and dynamic cone penetration test) on the test track. The NDT testing showed that the cemented layers performed well. However, it was found that the pavement failed prematurely under actual loading. The plate load test showed that crack relief failed because of compaction issues. Last, finite-element modeling of the test section using PLAXIS 3D version 2013 showed the vertical stress distribution in the inverted pavement.

Implementation of Deep Neural Networks for Pavement Condition Index Prediction

Abstract: AbstractPavement condition index (PCI) is commonly used in pavement management systems (PMS) for indicating the extent of the distresses on the pavement surface. PCI values are a function of distre...

Introducing a New Test to Examine Moisture Susceptibility at the Interface of Bitumen and Stones

Abstract: This paper introduces a novel test method, the moisture-induced shear thinning index (MISTI), to evaluate the susceptibility to moisture damage at the interface of bitumen and siliceous stone aggregates. In the MISTI test, glass beads are used as surrogates for stones. The test quantifies shear thinning as a fundamental material property to examine the extent of desorption of bitumen from stone aggregates when exposed to water. Because desorption of bitumen from stones by water molecules is implicated in moisture damage, the MISTI can be used to examine susceptibility to moisture damage in bituminous composites. The theory behind the test method is based on the target bitumen’s profile of adsorption–desorption to siliceous stones, which are highly susceptible to moisture damage. As the surface of silica becomes coated by water displacing formerly adsorbed bitumen molecules, the surface chemistry of the silica changes; this in turn alters the extent of shear thinning of the bituminous composite. Shear thinning is measured by applying a shear rate sweep (0.1−100 s−1) to the bitumen–stone blend. The MISTI is defined as the ratio of the shear-thinning value measured in wet condition to the value measured in the dry condition. In an entirely moisture-resistant sample, the MISTI is 1, indicating no changes due to water conditioning. The value of MISTI indicates the degree of susceptibility to moisture damage; any deviation from 1 indicates change at the interface and susceptibility to moisture damage. The test was developed to detect bitumens with high amounts of acidic, water-soluble compounds. The outcomes of this study facilitate detecting nondurable combinations of bitumens and siliceous stones by providing a tool to characterize them based on their susceptibility to moisture damage.

Freeze–Thaw Durability of Conventional and Novel Permeable Pavement Replacement

Abstract: Permeable concrete pavements are becoming more common as a stormwater management system to mitigate urban flooding. However, they have several well-defined drawbacks including low permeability, high clogging potential, and low strength and durability, notably in cold climates exposed to freezing and thawing. A new generation of high-strength clogging-resistant permeable pavement replacement (CRP) has been developed, through extensive laboratory work, to address these shortcomings and advance the field of permeable pavements. This paper reports on new advances in permeable pavement systems and the performance of a range of conventional permeable concrete and the developed novel CRP (both prepared using Portland cement) of varying porosity exposed to freeze–thaw cycles. This will allow performance evaluations of both systems in a cold climate. The tests involved exposing samples to temperatures varying from −20°C to +20°C and measuring changes in mass, area, compressive strength, and ultrasonic pulse velocity after each cycle. These new results show that CRP is highly resistant to degradation caused by freeze–thaw cycles compared to conventional permeable concrete, reducing maintenance requirements and improving service life. This study presents the first high-strength clogging-resistant permeable pavement replacement that is durable under frost action, these findings will support and enable wider use of permeable pavements in cold regions.

Effects of Nonuniform Tire Contact Pressures on Near-Surface Pavement Response

Abstract: Most current mechanistic-empirical (ME) design methodologies leverage layered elastic (LE) computer codes to compute pavement response at locations indicative of distresses. In conventional pavements, the subgrade is the layer of interest and the exact distribution of the contact pressures is of less consequence. However, in thin pavements or base layers with marginal materials in contingency airfields, rutting failures can occur in the base. Thus, the nonuniformity of tire contact pressure becomes increasingly important in determining near-surface response. While most LE formulations assume a uniform contact pressure, difficulties arise when using this approach to match near-surface stresses in instrumented test sections for nontraditional pavement structures. This paper describes the formulation of a nonuniform loading function implemented into an LE program and its comparison to measured field data. Results show that the nonuniform loading function provides a closer approximation to field data than the standard uniform pressure assumption typical in current design methodologies. Findings suggest that the ME design of contingency airfields should incorporate a nonuniform loading function to improve base layer rutting failure predictions.

Dynamic Back-Calculation Approach of Deflections Obtained from the Rolling Dynamic Deflectometer: Application and Validation

Abstract: Pavement response depends not only on loading magnitude and pavement material properties but also on the pavement’s dynamic parameters such as inertia, resonance, and damping. In a previous study, it was found that by using rolling dynamic deflectometer (RDD) free vibration testing, the resonant natural frequency and the damping ratio of the pavement could be determined, which is essential in determining the pavement stiffness, k. In this study, a back-calculation approach using RDD-measured deflections considering the natural frequency and loading frequency was proposed. A three-dimensional (3D) finite-element (FE) model was established simulating RDD loading on a three-layered pavement system consisting of asphalt, subbase, and subgrade. Using the FE model, a synthetic database composed of different pavement conditions and deflection responses was developed. The synthetic database was trained to predict natural frequency and deflections using deep-learning neural networks (DLNN). A back-calculation algorithm was then established determining the pavement modulus and thickness using the pavement’s natural frequency, deflection response, and RDD loading frequency. The proposed approach was validated by comparing the RDD and falling-weight deflectometer (FWD) back-calculated modulus using static and dynamic analysis. The RDD back-calculated modulus at 25 Hz was found to have good correlation with the FWD back-calculated modulus with an assumed hitting frequency of 33 Hz. In addition, modulus values of field cored specimens were compared with the RDD back-calculated modulus and were found to have good correlation.

Review of Applications of Artificial Intelligence Algorithms in Pavement Management

Abstract: With the continuous advancement in data-acquisition devices, computer vision techniques, and machine-learning (ML) algorithms over the past decades, artificial intelligence (AI) technology has increasingly been applied to research and practice in pavement engineering and related fields. This paper is aimed at systematically synthesizing the state-of-the-art in applying AI algorithms and techniques to various areas of pavement management. To achieve this goal, the authors reviewed major work published in archival journals from 2015 to 2020. Key findings from the review are synthesized and presented based on three broad categories of pavement management activities: distress evaluation, performance modeling, and maintenance and rehabilitation (M&R) programming. Most of the reviewed studies have achieved positive and/or promising results, proving the effectiveness of leveraging AI algorithms for pavement management. Distress detection and classification are found to be the areas that attracted the most attention in terms of applying AI techniques and algorithms. In contrast, applying AI techniques to M&R programming represents a major research gap. Based on the review, it can be concluded that AI algorithms have made noticeable achievements in most activity areas of pavement management, although some major research gaps remain to be filled.

PCDNet: Seed Operation–Based Deep Learning Model for Pavement Crack Detection on 3D Asphalt Surface

Abstract: The detection of pavement crack plays a critical role in pavement maintenance and rehabilitation because pavement cracking is one of the most important indicators for the pavement condition evaluation, as well as an early manifestation of other pavement distresses. To detect cracks accurately, precisely, and completely based on three-dimensional (3D) pavement images, this paper proposes a deep learning framework based on a convolutional neural network (CNN) and pixel-level improved crack seed algorithm, called Pavement Crack Detection Net (PCDNet). Firstly, the CNN layer based on the convolution implementation of sliding windows is applied to each 3D pavement image to divide it into 8×8 pavement patches and classify each patch into two types: the background patch, and the pavement crack patch. Secondly, the seed layer, i.e., an automatic threshold pixel-level crack seed recognition algorithm is used to detect the crack distress further and depict the complete contour simultaneously. Finally, the region growing layer is utilized to ensure the continuity of the cracks. Due to the good combination of the CNN and the algorithm, PCDNet needs only a patch-level data set for training but can output pixel-level results, a great novelty in crack detection. In this paper, 5,000 3D pavement images were selected from an established image library. PCDNet was trained with 4,300 3D pavement images and further validated based on 500 3D pavement images. The test experiment based on the remaining 200 images showed that PCDNet can achieve high precision (0.885), recall (0.902), and F-1 score (0.893) simultaneously. It also was demonstrated that PCDNet can detect different types of pavement crack under various conditions and resist noncrack pixels with elevation variation features, such as pavement edge drop-offs, curbs, spalling, and bridge expansion joints. Compared with recently developed crack detection methods based on imaging algorithms, PCDNet is capable of not only eliminating more local noise and detecting more fine cracks, but also maintaining much faster processing speed.

Modulus Improvement Factor-Based Design Coefficients for Geogrid- and Geocell-Reinforced Bases

Abstract: A series of large-scale model experiments were carried out on different geogrid- and geocell-reinforced base courses to evaluate realistic base-layer coefficients to design flexible pavements. The placement depth of reinforcement was first determined under monotonic loading on designed unreinforced pavement sections over a very weak subgrade (resilient modulus of 10 MPa) prepared in a 2.25-m3 size test chamber. A structural support offered by the reinforcement alone in the base layer was quantified through the modulus improvement factor (MIF) for varying subgrade conditions. The MIF values ranged between 1.5 and 3.5 for geogrid-reinforced bases and 1.4 and 5.0 for geocell-reinforced base layers placed over different subgrade conditions. Further, a range of laboratory-produced MIF values and semiempirical mechanistic design principles were used to analyze the flexible pavements to get the base-layer coefficients for various geogrid- and geocell-reinforced pavements. In this analysis, the traffic was considered from 2 million to 150 million equivalent single-axle loads, subgrade resilient modulus (Mrs) from 10 to 85 MPa [corresponding a California bearing ratio (CBR) from 1% to 8%], and MIF from 1.2 to 3.5 for geogrids and 1.2 to 5.0 for geocells. A new set of apt base-layer coefficients for geogrid- and geocell-supported base layers was developed through a systematic analysis. The layer coefficients for geogrid-reinforced bases ranged from 0.15 to 0.35 and 0.175 to 0.425 for geocell-reinforced base layers. The proposed models were validated with an as-built pavement section from Montana state and the available design approaches. The proposed design approach has reduced the thickness of a geogrid-reinforced base layer by about 40%, and it is 50% for the geocell-reinforced base layer.

Development of Time–Depth–Damage Functions for Flooded Flexible Pavements

Abstract: The first step toward building pavement structures that are resilient to flooding is to have a proper understanding of the impact of inundation on the pavement. Depth-damage functions have been developed and are widely used to quantify flood-induced damage to buildings. However, such damage functions do not exist for roadway pavements. The objective of this study is to develop a methodological framework to model postflooding road damage by identifying the importance of several parameters including flood duration, flood depth, flood pattern (including real flood data), transfer functions, pavement materials, and analysis location. Pavement serviceability and costs are introduced into the evaluation as well. The long-term goal is a tool for decision makers to use in planning and management of flooding events for more resilient pavements and allocation of budgets. It is established that the most important parameters that should be accounted for by decision makers are the flood duration, combination of the materials, critical location on the roadway (both vertical and lateral), and use of appropriate transfer functions. Opening the roadway to traffic immediately after the floodwater recedes will lead to earlier and more significant deterioration of the pavement and more costly maintenance and reconstruction.

Analytical Calculations for Asphalt Pavement Considering Interlayer Performance

Abstract: Asphalt pavement structures are composed of layered materials and are assumed to be completely continuous elastic layered systems in structural design. However, a number of studies have indicated that the interface is not completely bonded. Hence, this paper presents the solution for an elastic multilayered system considering interface properties. First, a nonlinear shear model was adopted as the interface bonding model, a solution of the elastic multilayered system considering the interface bonding model was proposed, and the calculation program PADS was verified. Then a comparison between the product KΔu and shear stress τzr at the interface was used to verify the accuracy of the recursive coefficient calculation in the program, and the calculation results of PADS and BISAR were used to verify the accuracy of the numerical computations. Finally, the effect of the interface condition between the asphalt layer and semirigid base layer on the performance of asphalt pavement was analyzed. The research results can provide guidance for the design of asphalt pavement structures and the selection of adhesive layer materials.

Characteristics of Pervious Concrete Incorporating Cockleshell as Coarse Aggregate for Pavements

Abstract: Large amounts of marine by-product waste have emerged as a major environmental issue in many parts of the world. However, considering the limitations of natural materials, the application of this type of marine waste, such as seashells, in concrete can reduce its negative impacts on the global environment. Hence, this research study aimed to investigate the properties of pervious concrete for pavements by incorporating crushed cockleshells, a marine by-product waste, which is used as a natural coarse aggregate. The natural coarse aggregate fraction was partially (25%, 50%, and 75% by mass) replaced by cockleshells. Tests carried out were skid resistance, densities (fresh and hardened state), voids content ratio by volumetric method and image analysis, compressive and tensile strengths, permeability, and their relationships. It was observed that the density and both compressive strength and tensile strength of the lightweight pervious concrete incorporating cockleshell showed lower values compared with the control mixture, whereas voids content, water permeability, and skid resistance revealed higher performance. It is suggested that seashell waste could still be utilized as a partial aggregate at a replacement level of up to 50% for adequate compressive strength of pervious concrete for nonstructural purposes.

Line-Structured Light Rut Detection of Asphalt Pavement with Pavement Markings Interference under Strong Light

Abstract: The construction of highways has been well-developed worldwide. Meanwhile, the heavy traffic flow brings huge pressure on highway maintenance. Pavement rutting is one of the major pavement distresses and its detection has been a research hot spot in pavement engineering. Despite the fruitful research outcomes, most of them were based on ideal circumstances and focused on how to improve the processing procedure to reduce the detection error of usual rutting measurement. Whereas some particular interference, such as pavement markings under strong light, usually occurs during the detection, and remains undetected. Pavement markings affect the accurate extraction of pavement transverse profiles and increase the detection error of rut depth. To fill this gap, this study proposed a line-structured rut detection method to improve the detecting accuracy of rut depth. The global gray scale correction algorithm and feature-based fusion segmentation algorithm are mainly used to eliminate pavement markings of the background. The centerline-based midpoint thinning algorithm, least square based curve correction method, and envelope model are applied to calculate the rut depth, and are applicable for different forms of rutting distress. A total of 600 of images collected from urban roads were classified into four categories and used to verify the proposed rut detection method with pavement markings interference under strong light. The experimental results indicate that the average relative detection error is 10.07% and the average proportion of detection accuracy is 87.65%. Meanwhile, the evaluation accuracy of the pavement condition assessed by the rut depth index reaches 83.87%. This manifests that the proposed method can not only deal with the rutting detection with interference, but can also apply to the situation without interference. Thus, the method could be used to evaluate pavement condition and offer a reliable data source for pavement maintenance. The work in the paper offers a vital reference for pavement rut detection methods worldwide.

Integrating Skid Resistance and Safety Benefits into Life Cycle Cost Analysis for Pavement Surface Treatment Selection

Abstract: Even though 30% of the annual highway fatalities originate from inferior roadway conditions, crash costs under regular operations have seldomly been included in the life cycle cost analysis (LCCA). Among the various condition indicators, pavement roughness has been the most investigated on roadway safety, while skid resistance has been less studied. This paper presented an integral decision-making process to integrate pavement skid resistance and its corresponding safety benefits into the LCCA for pavement surface treatment selection. Built on the processing of an extensive amount of traffic crash, pavement skid, and surface condition data provided by the Oklahoma DOT (ODOT), friction demands at the investigation and intervention levels were recommended to trigger surface treatments. Later, friction deterioration models were established to evaluate the skid performance of treatments over time. Multivariate analysis results indicated that aggregate properties and treatment types were among the most important factors for pavement friction. Subsequently, an enhanced safety performance function (SPF) was developed to predict expected roadway crashes under different skid levels. Pavement friction was a statistically significant factor and had a negative effect on vehicle crashes. The predicted pavement friction variations and the corresponding safety benefits of surface treatments were calculated and included in a spreadsheet tool developed from this study. A case study was provided to demonstrate the LCCA results with and without considering the safety costs of different treatments. Neglecting crash costs in LCCA would result in agencies adopting alternatives with lower short-term agency costs but underestimating the long-term safety benefits.

Statistical Analysis of Hot-Mix Asphalt Pay for Performance versus Quality Control for Performance

Abstract: Statistical analysis of hot-mix asphalt (HMA) data for quality assurance programs used by the Illinois DOT was conducted. The aim was to quantify the total amount of incentives and disincentives, and distribution of the measured values, variability of HMA test results and identify significant variations between contractor and agency results. Quality control and quality assurance data for construction projects were collected for the 2015–2017 construction seasons and were statistically analyzed using the Mann-Whitney and Levene’s tests. The results indicated that during 2015 and 2016, approximately 44% to 55% of the produced HMA tonnage received disincentives, averaging $20,000 per project, based on 710 projects analyzed. More than 80% of the construction projects showed no significant difference between the quality assurance results reported by the district and contractor quality control results. HMA density was the most frequent pay parameter–caused contractor disincentive, and air void content was the second. The bulk specific gravity test results, which contribute to air void and voids in mineral aggregates, were found to be the most variable and, hence, the main cause of differences between IDOT and contractor laboratory data.

Mining Co-Occurrence Patterns among Deep Road Distresses Using Association Rule Analysis

Abstract: Co-occurrence patterns among different deep road distresses (distresses below the road surface) play a pivotal role in road maintenance. It is essential for the sustainable development of road performance and draws much attention from road maintenance departments. However, current work mainly focused on the rapid detection and development evaluation of pavement distress. Few studies shed light on the relationship among them. In this paper, over 200 km of field tests were conducted on 87 sections of the highways in China by ground-penetrating radar (GPR). Based on the distress detection results, the association rule mining algorithm Apriori was applied to explore the co-occurrence pattern among 13 types of deep road distress. Results indicate a significant correlation among light distresses (distresses with light degree), and between light distress and severe distress (distresses with moderate and heavy degree). Light distress has an average 53% probability of accompanying or inducing other distress, which is supposed to be maintained in time to prevent the road from further deterioration. Light and severe distress have a 36% probability of co-occurrence. However, the relationship among severe distresses is proved to be weak. Compared with the external environment, the interaction between different distresses is also a considerable inducement for pavement performance deterioration. The study provides a new perspective on the generation mechanism of deep road distress, which can further help the authorities optimize the maintenance schedule.

Mechanical Behavior of Doweled Joints in Concrete Pavements: A Review

Abstract: Dowel bars in concrete pavements can effectively improve the performance of joints as well as the overall performance of pavements. This paper reviewed the mechanical behavior, structural numerical simulation analysis, testing study of doweled joints in concrete pavements, and the research work on the alternative dowel bars. The influences of dowel bar configuration, diameter, spacing, misalignment, rust, and the bonding between the surrounding concrete on the performance of doweled joints were also discussed in this paper. Limitations of theoretical research and test methods were mentioned after reviewing these aspects of dowel bars, indicating the direction for future research on doweled joints in concrete pavements.

Designs of Abrasion Resistant and Durable Concrete Pavements Made with SCMs for Cold Climates

Abstract: Rutting from studded tire wear is a typical pavement distress in cold climates such as that of Alaska and other northern states. Current state-of-the-art advancements in material technology and concrete pavement design have allowed for implementation of improved materials and concrete pavement sections that are more resistant to rutting. The addition of supplementary cementitious materials (SCMs) has been identified as one effective way to produce concrete pavements with better abrasion resistance. The objective of this study was to identify and develop concrete pavement mix designs containing SCMs that can provide excellent abrasion resistance and durability to address rutting from studded tire wear and accommodate extreme climate conditions in cold regions. This study involved two phases of work. During Phase I, a series of ternary mixes containing silica fume with either slag or class F fly ash were produced and tested. The results were statistically analyzed using Minitab version 19.2.0 to identify mix designs with good performance in terms of workability, compressive strength, and flexural strength requirements for pavement applications. In Phase II, the mechanical properties and durability of concrete specimens with selected mix designs from Phase I were further evaluated to identify the optimum mix design with SCMs. This included tests for compressive strength, drying shrinkage, abrasion resistance, and other dualities such as scaling resistance to deicer salts, freeze-thaw resistance, and chloride ion penetration resistance. In terms of the properties evaluated within this study along with a cost analysis, five mixes, including four optimal mixes and the control, all provided good performance, but a quaternary mix design containing primarily silica fume and slag (SL12 SF4 FA1 mix) appeared to provide the overall best performance considering strength, durability, abrasion resistance, and cost.

Effects of Synthetic Fibers on the Mechanical Properties and Thickness Requirements of Roller-Compacted Concrete for Pavements

Abstract: Due to the construction method used, the use of conventional rebar is impractical for roller-compacted concrete (RCC) pavement applications, and the use of discrete fibers seems to be the best alternative to this problem. Among the available types, synthetic fibers are commonly employed in pavement applications, due to their advantages such as ease of handling, cost efficiency, and corrosion-free nature. However, studies that numerically examined the extent of synthetic fiber contribution to the mechanical properties and structural requirements of RCC pavements are very limited. To fill this gap in the literature, an experimental study was conducted to examine the effects of synthetic fibers on the fresh and hardened-state properties of RCC. Then, using the material parameters obtained in this study and retrieved (to cover the effect of different material compositions and synthetic fiber types) from the literature, thickness design for a sample pavement was conducted for plain and fiber-reinforced concrete mixtures, to determine the effect of different material compositions and fiber types on the thickness requirement of RCC pavements. Based on the results of the conducted experiments, the amount of change in the fresh- and hardened-state performance of RCC mixtures due to the addition of synthetic fibers were presented. Thickness design results showed that the contribution of fibers may vary in RCC mixtures depending on the type and amount of fibers used, and the properties of RCC mixture in which they are used. For the type and amount of fibers considered in this study, up to 25% reduction in required thickness was observed for synthetic fiber–reinforced RCC mixtures.

A Statistical Principal Component Regression-Based Approach to Modeling the Degradative Effects of Local Climate and Traffic on Airfield Pavement Performance

Abstract: Airfield pavement systems support the global economy, passenger travel, and national defense. Accurate pavement degradation predictions are critical inputs for maintenance and repair decisions, and when skillful, they may reduce the need for time-intensive, costly physical inspections that disrupt airfield operations. Existing airport pavement management systems (APMS) compute expected degradation as a function of pavement type and age, but they do not account for local climate and traffic conditions and they are not built to adapt to future changes in either mode of variability. This paper implements a bias-reduced statistical model that reveals the effects of local conditions using observed historical climatic and aircraft traffic data. Model performance is evaluated using a diverse data set from nine Air Force installations, encompassing three major Köppen-Geiger climate zones in the contiguous United States and representing a wide range of aircraft. Environmental factors are more impactful on pavement degradation than aircraft traffic; a climate-only model produces R2 values as high as 0.84, while traffic improves explained variance across installations (R2 = 0.86–0.97) for the most heavily trafficked pavement family. This work illustrates the impactful role of climate in pavement degradation and demands implementation into the current APMS. Airfield asset managers can use this adaptable framework to more accurately determine sources of local degradation and inform sustainable pavement design and management practices.

Deterioration Modeling of Flexible Pavements Based on As-Produced and As-Constructed Properties

Abstract: The goal of this study is to develop a framework for the life-cycle understanding of flexible pavements. New advancements in data analytics allow for the utilization of pavement life-cycle data (historical, environmental, and structural) to evaluate the effects of material, construction, and loading parameters on the in-service performance of the pavements. In this study, the data were georeferenced to establish a connection between pavement parameters such as construction and production quality factors, traffic loading, material properties, pavement structure, and climate conditions to the long-term performance of flexible pavements. The data used in this paper were sampled from the Wisconsin Department of Transportation (WisDOT). Data were filtered to include pavement sections of comparable traffic load and environmental conditions to avoid potential bias in the analysis. Information on 42 highways with a total length of 260.5 mi was collected and analyzed for this study. Pavement deterioration metamodels were developed on high-resolution data using three machine learning (ML) techniques. For the purpose of construction of the metamodels, ML techniques including decision tree regression (DTR), random forest (RF), and gene-expression programming (GEP) were utilized by using coded subroutines in Python. The outcomes of DTR, RF, and GEP approaches showed promising results in the modeling of pavement performance by considering the effects of mix production quality factors such as air voids of the mixture (VA), individual lots voids in mineral aggregates (VMA), in-place density of asphalt mixture (%Gmm), asphalt content (AC), surface thickness, and age of pavements. This approach provides a basis for comprehensive life-cycle evaluation of the highway network without disrupting the state of practice. It relies on connecting data already being collected by the transportation agencies. The relational connection of such data allows for a pavement management system that is capable of continuously reflecting the pavement network performance on design, control, and maintenance activities.

Simulation on Water Flow in Permeable Pavement Influenced by Voids Decrease

Abstract: The permeable pavement has good drainage capabilities, but the voids are easy to be clogged. Describing the internal flow and potential surface flow of permeable pavement is essential for pavement design and driving safe maintenance. In this paper, indexes including connected porosity, equivalent area of the voids channels, and the bending rate were selected to depict the permeable pavement internal flow when the voids decreased due to clogging. A simulation model was proposed based on Darcy’s law by using the images from computer tomography (CT) scanning; then, the seepage flow inside the voids was obtained to evaluate the drainage of porous asphalt mixture. The potential surface flow situation including waterlogging, critical runoff depth and drainage time was calculated. Surface hydrodynamic pressure between the tires and pavement was also compared. Results showed that the connectivity of internal voids was crucial to quantify the voids clogging. The internal flow nearly disappeared when the porosity decreased by less than 15% due to the blockage. The drainage function was influenced by the porosity, thickness, and length of the drainage path. The surface layer’s thickness should be determined according to the indexes such as the maximum runoff depth without water skiing, the drainage time, and the road facilities, among others. Surface hydrodynamic pressure distribution illustrated it might decrease by 33% for every 10 km/h deceleration. A short flow path length and a more considerable cross slope within a reasonable range could decline drainage time. It was suggested that all these factors might be considered when designing and maintaining the permeable pavement.

Application of Dynamic Stability Criterion in Evaluating Field Rutting of Asphalt Pavements Using the Wheel Tracking Test

Abstract: In this study, the application of the dynamic stability (DS) criterion to evaluate rutting of asphalt pavements using the wheel tracking test is presented considering field pavement conditions. A simplified model estimating the rut depth of asphalt pavements was first developed considering the DS, the number of load cycles (N), the maximum shear stress (τmax), and load duration (t). To develop the model, indirect tensile (IDT) and uniaxial compressive strength (UCS) tests were conducted to measure cohesion (c) and internal friction angle (ϕ) of three asphalt mixtures. In addition, seven types of asphalt mixtures were evaluated to determine their DS using the wheel tracking test. To determine the average maximum shear stress, a predictive regression equation was established through the KENLAYER program with various combinations of asphalt concrete (AC) modulus, subbase and subgrade resilient moduli, and layer thicknesses. Based on the rutting performance of six pavement sections from the WesTrack test, the rutting model was validated and applied to different AC layer scenarios. It was found that the proposed model is accurate in estimating the rut depth of AC layers under varying load and environmental field conditions. Application of the DS criterion in evaluating rut depth for asphalt concrete is proposed using the developed rutting model.

Estimation of Resilient Modulus Constitutive Model Parameters for Unbound Coarse Materials for MEPDG

Abstract: In this study, 18 different unbound aggregates were collected from various locations of Idaho and several characterization tests were conducted in the laboratory. The resilient modulus of these collected aggregates was measured with the help of repeated load triaxial (RLT) testing. The results of the laboratory characterization tests were used to develop a regression model for the Mechanistic Empirical Pavement Design Guide (MEPDG) model parameters (e.g., k1, k2, k3). The k1, k2, and k3 models were correlated to compaction and gradation characteristics of the test aggregates. The proposed models can be used to predict the MEPDG model regression coefficients for unbound layers in AASHTOWare Pavement ME Design software. The resilient modulus measured in the laboratory and the resilient modulus predicted with these ki models are found to be in good agreement.

Comparative Characterization of Field and Laboratory-Aged Binders Modified with Antioxidant Additives and Copolymers Using Fourier Transform Infrared Spectroscopy and Gel Permeation Chromatography

Abstract: This study investigates the behavior of antioxidant additives and copolymers to an asphalt binder by comparing laboratory-aged (up to 60 h) and antioxidant-modified binders with binders extracted from field cores. To evaluate the change in carbonyl, sulfoxide, aromatic, and aliphatic compounds of asphalt binders due to both lab and field-aging, spectral analysis was performed using Fourier transform infrared spectroscopy. A good correlation was found between carbonyl growth and viscosity in the field-aged binders. Chemical analysis with gel permeation chromatography showed that the quantity of large particle sizes increases with the increase in carbonyl growth due to aging. Both Redicote and Solprene were found to retard the growth of large molecular size particles in lab-aged binders when compared to field-aged binders. This study provides further validation of the use of Redicote and Solprene in retarding the aging of asphalt binders. The advanced chemical characterization conducted in this study can be used to evaluate the effectiveness of antioxidant additives and copolymers in retarding oxidative aging and selecting the proper products that work better with specified asphalt binders.