Pavement Sections Research Articles

Laboratory and Field Evaluation of Asphalt Concrete Modulus &Critical Strain Inputs for M-E Flexible Pavement Design in the State of Illinois

This study aims to update the current algorithm for modulus prediction of hot mix asphalt in the state of Illinois-USA. Numerically, laboratory, and field-obtained modulus and strain levels were connected to advances in asphalt materials and current design criteria. Four mix designs were selected and combined with seven different binders for modulus measurements. The reference experimental results were compared to: the current Illinois Department of Transportation (IDOT) modulus algorithm, traditional regression models, a Bayesian Neural Network (BNN) model, and field measurements obtained using the Traffic Speed Deflectometer. Results showed that the current IDOT algorithm under-predicts modulus and BNN (840 data points, R^2 = 0.986) outperformed the Witczak and Hirsch models. Field modulus and critical strains were sensitive to temperature and showed considerable variability, but confirmed that HMA in perpetual pavement sections performs under low strain levels. These findings guided considerations for an updated design method: realistic modulus prediction can be achieved using data-driven methods and less conservative strain criterion might be considered.

Mechanical and economical feasibility of LDPE Waste-modified asphalt mixtures: pathway to sustainable road construction

This research study evaluates the impact of low-density polyethylene (LDPE) modified asphalt binder on the mechanical performance and economical feasibility of asphalt concrete (AC) mixtures. LDPE-modified mixtures were compared with conventional mixes prepared with various binders commonly used in India, i.e., VG 30, VG 40, Polymer-Modified Binder (PMB), and Crumb Rubber Modified Binder (CRMB). LDPE-modified mixtures exhibit superior mechanical performance, increasing the stiffness of AC mixtures by 171% and 125% at 25 °C and 35 °C, respectively, compared to VG 30 base binder. Additionally, the indirect tensile strength (ITS) improved by 51% over VG 30. LDPE-modified mixtures also showed improved resistance to permanent deformation, with RTIndex values up to 133.46% higher than VG 30, and higher fatigue resistance, as indicated by increased CTIndex values compared to VG 30 and CRMB. However, the CTIndex values for LDPE-modified mixtures were 26.32% and 56% lower than those for VG 40 and PMB, respectively. Pavement analysis using 3D-Move showed lesser deflections at pavement layer interfaces, resulting in higher rutting and fatigue life for LDPE-modified pavements. Furthermore, LDPE-modified pavements showed up to 57% higher fatigue life (Nf) and up to 42.33% higher rutting life (Nr) than other pavements. The economic analysis showed that the cost of LDPE-modified pavements is comparable to VG 40 and around 10% more economical than pavements containing PMB. Using LDPE also offers environmental benefits by repurposing up to 750 kg for every kilometer of single-lane pavement section having 50mm thick surface course. Overall, LDPE-modified asphalt mixtures present a sustainable and high-performance solution for asphalt pavement construction.

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

Asphalt pavement structural competency during staged airfield runway construction strategy

ABSTRACT This paper reports the results from a full-scale evaluation of asphalt pavement structural competency during a multi-stage construction strategy. The staged pavement construction included three stages: (1) new conventional asphalt pavement, (2) unpaved full depth reclaimed (FDR) pavement, and (3) thin surface asphalt paved FDR layer. The full-scale pavement sections consisted of a standard and substandard pavement structural design. The accelerated pavement testing (APT) experiments were executed by simulating the loading conditions of an aircraft with a critical combination of relatively high wheel load and high tire inflation pressure. The structural capacity of the pavement test items was monitored using a falling weight deflectometer. From this study, the staged pavement construction concept was found to be a potential approach to extend the service life of asphalt pavements. Unpaved FDR layers are structurally competent to support a low volume of aircraft operations. These findings support the transition to the use of asphalt pavements containing an FDR layer from an experimental to routine rehabilitation practice for airfield asphalt pavements.

Machine learning-based models for thermal cracking prediction of flexible pavements

Prediction of transverse cracking is crucial for pavement practitioners and decision-makers to prognosticate the cracking development and allocate maintenance budgets. This research investigates thermal crack predictions, using four various machine learning techniques: the support vector regression (SVR), the regression artificial neural network (RNN), the Gaussian process regression (GPR) and the least-square boost ensemble method (LSBoost). Predictions were based on 214 pavement sections with 1262 data points from the Long-Term Pavement Performance (LTPP) database. The models are developed using 20 variables representing age, binder, mix, aggregate and climate properties. The LSBoost ML algorithm exhibited the best performance, with a coefficient of determination (R2) of 0.926 for training and 0.727 for testing. Sensitivity analysis revealed that pavement age is the predominant factor in transverse crack prediction followed by the freezing index (FI). Overall, climatic parameters played an important role in transverse crack predictions compared to the mix and binder properties.

Verification of On-Site Applicability of Rainwater Road Surface Spraying for Promoting Rainwater Utilization and Analyzing the Fine Dust Reduction Effect

This study conducted a pilot test and field demonstration to analyze the feasibility of using rainwater for road surface sprinkling and its effectiveness in reducing fine dust to create sustainable cities. The pilot test results verified the effectiveness of rainwater sprinkling on road surfaces in reducing fine dust. Subsequently, to ensure field applicability, a test bed with different pavement sections was established along Ojeong-ro in Bucheon, Gyeonggi-do, and a circulatory system for reusing the runoff rainwater was installed and tested for its acceptable dust reduction effectiveness. The results showed that the section with porous pavement exhibited a significant reduction in acceptable dust levels, and a trend of decreasing pollutant levels due to the initial washing effect of rainwater was observed. However, the water quality and quantity analysis revealed that additional purification processes are necessary to reuse the water for road surface sprinkling. Based on these findings, this study suggests that expanding porous pavement and introducing purification systems are essential for installing rainwater utilization facilities to reduce fine dust in urban areas. This research provides critical foundational data that can contribute to urban environmental improvement and establish a sustainable water circulation system.

Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.

Buckling prediction based on joint opening and safe temperature estimation in jointed plain concrete pavements

Concrete pavement buckling is a significant distress that disrupts traffic flow and results in costly repairs, issues that are increasingly exacerbated by global warming. Addressing these issues, this study develops a systematic framework for assessing jointed plain concrete pavement (JPCP) by integrating temperature and moisture gradients, pavement structures, and material properties into mechanical-based models for predicting buckling potential. Specifically, a two-stage analysis is developed to analyze buckling development based on the slab deformation and the slab-base interface, including joint closure assessment and upheaval buckling prediction. Through the established model, parametric studies are conducted to quantitatively identify key factors influencing buckling. Moreover, the developed models are applied to pavement sections in various climate regions and validated through comparative analysis with field data from the Long-Term Pavement Performance (LTPP) database to confirm model accuracy. Additionally, a case study using pavement buckling data from Wisconsin between 2014 and 2020 demonstrates the model's practical application. As the results, it is found that temperature increase, the percentage of incompressible materials, and the coefficient of thermal expansion are critical for joint opening changes, while the coefficient of thermal expansion, elastic modulus, and slab thickness are crucial to post-buckling performance. Regression equations are provided to estimate safe temperature increases as a recommended buckling temperature limit. Furthermore, the predicted joint opening shows reasonable alignment with joint gauge measurement in the field recordings from the LTPP database. Finally, the predicted buckling events are reasonably compared with field recordings for a Wisconsin pavement section from 2014 to 2020. In summary, by considering pavement structural parameters, material properties, and weather conditions, this comprehensive model provides a reasonable tool for predicting and mitigating JPCP buckling in different climate conditions.

UPDAPS-Flood: a mechanistic-empirical flexible pavement analysis tool to evaluate the effect of flooding events on flexible pavement performance

ABSTRACT Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels.

Semi-analytical modelling of realistic tire-pavement contact: an analysis of pavement surface cracking damage under critical driving conditions

This paper aims to evaluate the surface cracking phenomenon on a flexible pavement subjected to diverse vehicle running conditions. Top-down cracking initiation is addressed through a continuum damage framework coupled with a semi-analytical model of tire-pavement rolling contact. The acceleration, braking and cornering scenarios were selected to represent the effect of combining normal and tangential loads. The differences between these loading cases are accurately computed by simulating the realistic contact stresses on the pavement surface through semi-analytical modelling. The computational cost of this modelling technique is reduced, allowing it to be used in industrial applications. It is assumed that tires and pavement are linear elastic multilayered half-spaces. The critical locations of the cracks are identified for each loading scenario and the cracking directions are discussed. This study emphasises the influence of driving conditions on premature pavement cracking which is not typically considered in the current pavement design guides. It is shown that a standard axle load may cause about 9 times more surface damage on a circular intersection compared to a straight-line pavement section.

Effects of 3D Geocells on Flexible Pavement Foundations

Abstract: The current below study examines the effects of adding a Cubic Three-dimensional Geo-synthetics material known as Geo-cells to the foundation layer of flexible Bitumen pavements. In this study, the test of California bearing ratio (CBR) of 5% is used to compare the steel reinforced and unreinforced pavement made sections built on that subgrade. To comprehend the impact of Geo-cell reinforcement upon the pavement section's load-carryings mechanism under static and repetitive loading circumstances, a number of model tests were conducted. The following characteristics were examined: surface deformation profile the test sections, real rut subgrade level, load-settlement response of the pavement sections, and pressure is transmitted into the subgrade soil beneath into the Geo-cell reinforced foundation layer.

Holistic and Practical Approach for Assessing Flood Risks of Low Volume Asphalt Roads

Flood-induced damage to roadways is well recognized and documented in local and national media as well as in the literature. However, the damage to pavement structures that remain visually intact after a flood event are far from well understood, nor is there a guideline for selecting an appropriate engineering tool or procedure to evaluate such structural damage, based on flood characteristics and roadway conditions. While low volume roads are the most vulnerable to flood-induced damage, many municipalities, such as smaller cities, towns, or local government districts, are the least prepared to address such risks and hazards to their transportation infrastructure because of the lack of financial or technical resources. A holistic framework for assessing flood damage after a flood event or for future flood scenarios is proposed, which considers destructiveness of hazard (i.e., flood), vulnerability, and consequence of the flood. A quantitative, composite indicator, risk factor ( RF), as a multiplication of hazard destructiveness factor ( HF), vulnerability factor ( VF), and consequence factor ( CF), can readily be approximated with flood characteristics, pavement characteristics, and functional class of pavement and traffic volume. A flooding risk map is then developed, on the basis of the RF in a space of physical damage factor ( PF)–consequence factor ( CF). The map is divided into three different risk zones: high risk zone ( RF>64), medium risk zone (27< RF≤64), and low risk zone ( RF≤27). The proposed holistic, quantitative approach has been applied to six flooded pavement sections in Louisiana to evaluate its feasibility and application potential.

Airfield pavement performance prediction using Clustered Markov Chain Models

This study pairs unsupervised learning methods of clustering and classification with discrete-time Markov Chain models for airport pavement performance prediction, with the goal of allowing the clustering algorithm to take the place of typical pre-analysis grouping of pavement sections. The influences of cluster method, number of clusters, input years, interval time span, and training data on prediction results are analysed. Results indicate that clustering using the deterioration profiles of Pavement Condition Index (PCI) before applying the Markov Chain is sufficient to reduce the error by half in predicted pavement conditions for most cases. The initial pavement degradation is a reliable source for clustering and future condition prediction using the appropriately matched Markov Chain. This approach minimises the data required to predict pavement condition and proves the early deterioration rate of a pavement section can be used to predict its overall lifespan.

Repeatability and Reproducibility of Pavement Density Profiling Systems

The work conducted in this study was designed to establish achievable testing tolerances for non-destructive pavement density measurements using Density Profiling Systems (DPSs). Nine and six sensors were used to determine the precision of repeatability and reproducibility in the laboratory and the field, respectively. A minimum of six sensors (considered in this study as independent laboratories) were needed to comply with the minimum number of participants required in the current ASTM standard practice (ASTM E691). The methodology included the development of laboratory precision evaluation with a total of nine sensors and two different mixtures (9.5 mm fine-graded mix, 19.0 mm coarse-graded mix) compacted at four density levels (97%, 94%, 91%, and 88% of Gmm). For the field portion of this study, pavement sections built at the National Center for Asphalt Technology (NCAT) Test Track in 2021 served as experimental variables. These sections were built with fine-graded asphalt mixtures and open-graded mixes as wearing courses. Additionally, the pavement sections included three underlying materials: new asphalt (binder layer), milled asphalt surface, and granular base, with thicknesses ranging from 3.8 to 13.9 cm. Density profile testing was conducted at two locations: within the mat (center of the lane) and along the joint. Computed precision statements regarding dielectric values within and between laboratories were about double for field results compared to laboratory results. However, when converted to density, the statements were significantly below the reported statements for Bulk Specific Gravity and Vacuum Sealing in the laboratory and Nuclear and Electromagnetic density gauges in the field.

Tracking particle displacements in unbound aggregate layers of roadways

ABSTRACT Accelerated pavement tests were conducted on reduced-scale pavement sections under controlled environmental conditions using the model Mobile Load Simulator (MLS11). A unique monitoring system was developed as part of this study to evaluate the response of the pavement sections under rolling wheel loads. The performance of the sections with an increasing number of wheel passes was evaluated by measuring the surface rutting as well as the subsurface displacement of particles within the base layer. The sections were built in modular frames constructed above grade to facilitate access to the particles within the base layer from the sides of the frame. Surface rutting was measured intermittently using an in-house developed laser profilometer. A unique, cost-effective assembly of 30 linear position transducers was designed to continuously track the displacements of artificial particles within the base layer. The displacements, measured with the new system, were found to be particularly suitable to generate horizontal permanent displacement fields and strain fields. Overall, the new developments allowed comprehensive monitoring of the internal response of pavements subjected to wheel loading.

Studying the Structure of the Road Pavement and Specifying the Repair Method

Studying the structural condition of road pavement is essential for making effective decisions about the type of maintenance or rehabilitation that will be carried out on a pavement section. Reliable quantitative assessment of the volume of necessary repairs will ensure the performance characteristics and durability throughout the entire service life at minimal cost. This article presents detailed results from Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) studies. In addition, an analysis of these results and recommendations for strengthening the road pavement for the Sh-176 “Sighnaghi-Saint Nino Monastery” road to develop rehabilitation and maintenance scenarios. FWD results combined with road pavement layer thickness and traffic load information to determine residual service life and necessary treatment type. Thus, using the nonlinear theory of layered halfspace elasticity, pavement structure analyzed in the same way as other civil engineering structures.

Evaluating the influence of volumetric properties on back-calculated asphalt layer moduli using falling weight deflectometer data

The back-calculation process performed in pavement systems is the numerical analysis of captured deflections to estimate layer stiffness parameters. Prediction of fatigue performance in terms of back-calculated asphalt layer moduli by using a Falling Weight Deflectometer (FWD) has specific challenges in terms of testing protocol, skillset, and complex back-calculation analysis. The performance of the asphalt layer is primarily governed by extrinsic parameters such as temperature, vehicular transient loading characteristics, moisture content, and intrinsic parameters such as binder properties and aggregate mix properties. The role of volumetric properties of asphalt mixes contributes significantly to the back-calculated asphalt layer moduli in terms of the overall life of the structure. The asphalt layer moduli value is dependent on the traditional volumetric properties of asphalt mixes such as air voids in the mix, voids in mineral aggregate, and the percentage of bitumen mix. In this study, a total of 60 in-service pavement sections are identified from three different categories of roads to perform FWD tests and collection of asphalt layer core samples. A detailed laboratory investigation has been carried out to estimate the volumetric properties of different core samples. This study uses field investigations to determine the degree of interdependency between the volumetric characteristics of asphalt mixtures and temperature on the back-calculated layer moduli. Furthermore, the findings from this study are utilized to establish several correlations at the aggregate level, demonstrating strong relationships with R2 values ranging from 0.84 to 0.875. The developed model is validated and depicted in good agreement with the actual values.

Influence of Cementitious Stabilization of Subgrade Soil on the Distresses of Flexible Pavement Sections

Influence of Cementitious Stabilization of Subgrade Soil on the Distresses of Flexible Pavement Sections

Erosion Potential of Stabilized Support Layers for Concrete Pavements and Overlays

The performance of concrete pavements and overlays is highly dependent on the uniformity, durability, and stability of the underlying support layers. Erosion of support layers can lead to pavement distresses and a reduction in pavement life. A review of existing erodibility performance tests that assessed stabilized support layers was first conducted to identify and evaluate their suitability for adaptation. The Hamburg wheel tracking device (HWTD) test was selected to assess the erosion potential of asphalt and cement stabilized support layers. Field testing with distress surveys, falling weight deflectometer, and coring was completed to obtain HWTD specimens and link laboratory results to pavement performance. The HWTD test was performed on cores obtained from in-service cement and asphalt stabilized support layers, a cold in-place recycling (CIR) mixture, and cement stabilized laboratory mixtures. As expected, an increase in cement content within cement stabilized mixtures decreases the likelihood of erosion with the HWTD. Additionally, conventional asphalt stabilized base layers were highly erosion resistant. Erosion resistant cement stabilized bases (including full-depth reclamation) should target an average HWTD erosion depth ≤2 to 4 mm (0.08 to 0.16 in.) after 10,000 load cycles based on the functional classification and expected traffic volume of the pavement section. Likewise, asphalt stabilized bases (including CIR and support layers for concrete overlays) should target an average HWTD erosion depth ≤12.5 mm (0.5 in.) after 7,500 load cycles with performance grade 64 binder.

Evaluation of commercial and scrap tyre cellular reinforcement infilled with demolition waste for granular sub-base of flexible pavements: A sustainable approach

Nowadays, use of geocell is gaining more importance in pavement industry as it offers improved performance through confinement and tensioned membrane actions. In the recent years, solid waste management seeks greater attention as it poses serious issues related to their disposal, health and environmental hazards. Effective recycling and reuse of these wastes could be a viable and economical approach towards sustainable construction. In this regard, two such wastes (scrap tyres and construction & demolition (C&D) waste) are considered for investigation. The recycled concrete aggregates (RCA) obtained from the C&D waste is used as granular sub-base material and its performance is compared with conventional crushed aggregates (CCA). Scrap tyre is used as cellular reinforcement (recycled tyre cell (RTC)) and its effectiveness as a reinforcing material is compared with the commercial cellular reinforcement (commercially available geocell (CGC)). The study involves construction of laboratory pavement sections under unreinforced and reinforced conditions and testing under repeated loading. The pavement sections supplemented with CGC and RTC reinforcement shows reduced permanent deformation by 27 & 33% and 30 & 35% respectively for CCA and RCA infill materials. The comparable performance of RCA with CCA is attributed to the self-cementation effect offered by the adhered residual cement mortar. With CGC and RTC reinforcement, residual pressure at the subgrade level reduces by 30 and 37% respectively. Provision of cellular reinforcement improves the stress distribution angle than the respective unreinforced pavements and highest improvement is observed in case RTC. Improved behaviour with RTC is due to the increased strip thickness and tensile strength of recycled tyre strip than the ones used in the preparation of CGC. Irrespective of the infill material, higher strains are experienced under the loading zone than the neighbouring cells. Overall, the study promotes use of scrap tyres and RCA in bulk volume for pavement construction offering an eco-friendly, cost-effective, sustainable and long-lasting pavements.