Pavement System Research Articles

Efficacy of underground aggregate infiltration beds under a permeable pavement system

Pervious concrete (PC) pavement systems with their underlying aggregate storage beds provide potential strategies to mitigate stormwater runoff and associated flooding issues. Of particular interest is the efficacy of the infiltration capabilities into the soil of the underground storage bed, especially in areas with slow draining soils. This study uses real-time data on the water levels in the storage beds to determine soil infiltration rates at two test sites in Southeast Texas. One of the sites is connected to a rain garden system, with the potential for taking advantage of the low impact development (LID) processes of both systems. It was found that the combined systems allowed water to flow between them possibly adding additional storage during a storm event with the PC system’s void spaces and allowing for additional evaporative processes from the rain garden post the event. The other site has the PC raised above the surrounding grade, which represents potential applications where structures are raised above base flood elevations with connecting paved areas also raised. It was found that this raised system provided both infiltration into the soils from the underground bed and some detention capabilities in the raised portion of the system. These results aid in designing these LID systems in flat areas subject to flooding with slow draining soils such as in much of the coastal Gulf region.

Utilizing Machine Learning for Predictive Maintenance of Climate-Resilient Highways through Integration of Advanced Asphalt Binders and Permeable Pavement Systems with IoT Technology

This review provides a comprehensive examination of the application of advanced technologies in enhancing the climate resilience of highway infrastructure. The focus is on the integration of machine learning for predictive maintenance, the use of Internet of Things (IoT) devices for real-time monitoring, and the deployment of advanced materials, specifically permeable pavements and modified asphalt binders. The review explores how these technologies work synergistically to create durable, low-maintenance highway systems capable of withstanding extreme environmental conditions. Advanced asphalt binders, such as those modified with styrene-butadiene-styrene (SBS) and nanomaterials, are discussed for their enhanced flexibility, thermal stability, and load-bearing capacity. Additionally, the environmental and structural benefits of permeable pavements are highlighted, particularly their role in stormwater management and reduction of urban heat island effects. Several case studies highlight the successful implementation of these technologies in diverse geographical and climatic conditions, including North Carolina, Australia, and Raipur, India. These cases illustrate the adaptability and environmental benefits of permeable pavements in managing stormwater, recharging groundwater, and mitigating the urban heat island effect. By synthesizing recent advancements and practical implementations, this review emphasizes the importance of integrating predictive technologies and resilient materials to develop sustainable, climate- adaptive highway systems. These insights offer a foundation for future infrastructure policies and technological innovations aimed at enhancing the durability and environmental sustainability of highway networks.

Boundary Layer Modelling of Flow Acceleration and Energy Transfer Effects in Smart Pavement Design

The integration of remote technology into the construction industry has advanced the emergence of smart, self-learning infrastructure across all levels of land transportation design and development. However, energy harvesting capabilities for smart road ventilation purposes have not yet been fully researched as the world gravitates towards energy sustainability. This study thus presents a mathematical investigation of the self-draining design potentials of road pavements, leveraging on energy absorption and conversion to accelerate conductive and convective ventilation of these pavements during wet conditions, as a means of ensuring the skid resistance and safety of such pavements are not compromised. Applying numerical methods with the aid of commercial software code MATLAB®, the classical physics of fluid-surface interaction was used to model accelerated drainage of microflood off paved surfaces through methodical modification of the thickness of the boundary layer associated with the flow regime over the flat road surface. Results indicate significant influences of energy exchange, thermal, and viscous diffusivity on flow acceleration. Alterations in parameters such as the slip factor, thermally induced Brownian motion or phoretic characteristics of the fluid particles at the boundary induce accelerated flow at the surface of the pavement. The findings contribute to the advancement of smart infrastructure by offering practical insights into the potential benefits of integrating energy-harvesting technologies into road pavement systems. By optimizing flow acceleration mechanisms, smart pavements can play a crucial role in improving road safety and sustainability in urban environments.

Mechanical Response of Hot-Mixed Epoxy Asphalt Concrete Steel Deck Pavement Under Thermal and Load

In recent years, fatigue cracking in orthotropic steel bridge deck pavements has become a significant concern, so the investigation of the mechanical response of the pavement layer has become a central focus in pavement structure design. This experiment subjected a full-scale specimen to a constant amplitude dynamic load of 60 kN to 300 kN over 2 million cycles. Throughout the testing, a circulating water bath elevated the temperature of the pavement layer from 15 °C to 50 °C. Key locations were monitored for strain and deflection data, facilitating an investigation into the mechanical response of the epoxy asphalt pavement system under the effects of temperature and load. The results indicate that the maximum transverse strain at the bottom of the steel deck occurs at the U-rib weld aligned with the load center, reaching 190% of the initial loading strain. Meanwhile, the maximum transverse strain on the pavement surface is observed at the U-rib weld adjacent to the loaded area, measuring 167% of the initial strain. The maximum longitudinal strain is lower than the maximum transverse strain. In the load zone, the longitudinal strain between the U-ribs exceeds that at the U-rib weld. Both transverse strain and relative deflection increase as the load intensifies. The relationship between transverse strain and applied load is characterized by an exponential function, while deflection exhibits a cubic relationship with the applied load. Elevated temperatures also contribute to increased transverse strains at both the bottom of the steel deck and the pavement surface, following an exponential trend. Relative deflection is primarily influenced by the applied load and remains relatively unaffected by temperature variations. When accounting for the coupling of load and temperature, the maximum transverse strains at both the bottom of the steel deck and the pavement surface can be modeled as an exponential function of the independent variables: load and temperature.

In Situ Experimental Analysis and Performance Evaluation of Airport Precast Concrete Pavement System Subjected to Environmental and Moving Airplane Loads.

The behavior of airport precast concrete pavement (APCP) involving new design and construction concepts was experimentally analyzed under environmental and moving airplane loads, and the long-term performance of the APCP was evaluated using fatigue failure analysis. The strain characteristics and curling behavior of the APCP under environmental loads were comprehensively analyzed. The APCP slabs exhibited a pronounced curling phenomenon similar to conventional concrete pavement slabs. The dynamic response of the APCP subjected to impact loads was analyzed by performing heavy weight deflectometer tests. The test results confirmed that the vertical deformation of the APCP was small and within the typical range of vertical deformation of conventional concrete pavement. The dynamic strain response of the APCP under moving airplane loads was then analyzed and the strain variation during day and night times was compared. The strains during the day were found to be significantly larger than those at night under airplane loads because of the curling phenomenon of the APCP slabs. Finally, the long-term performance of the APCP was evaluated using fatigue failure analysis based on the obtained behavior. Even using the most conservative fatigue failure prediction model, the service life of the APCP was ascertained to be more than 30 years. Based on the overall results of this study, it is concluded that the APCP, which is designed to reduce slab thickness by placing reinforcing bars in the slabs via reinforced concrete structural design, exhibits typical behavior of concrete pavements and can be successfully applied to airport pavement rehabilitation.

Effect of nonlinear stress-dependency and cross-anisotropy on the backcalculation outputs from the TSD deflection slopes and the effect on estimated pavement performance

ABSTRACT The Traffic Speed Deflectometer (TSD) is a mobile vehicle that measures deflection slopes. Deflection slopes have been utilised in previous studies to backcalculate pavement layers’ moduli. However, the nonlinear stress-dependency and cross-anisotropy of unbound granular materials and fine-grained soils were overlooked in those studies. Utilising the Finite Element Method (FEM) based on static analysis in this study to evaluate a three-layered flexible pavement system with specific material properties and layer thicknesses revealed that neglecting the nonlinear stress-dependency of base and subgrade layers underestimated the permanent deformation life of the backcalculated pavement by more than 45%. Neglecting the cross-anisotropy of the base layer with the design anisotropy ratio of 0.5 increased the backcalculated Asphalt Concrete (AC) modulus by more than 21%, increased the estimated permanent deformation life of the pavement by more than 160%, and decreased the backcalculated base modulus by around 28%. Neglecting the cross-anisotropy of the subgrade with the design anisotropy ratio of 0.5 almost increased the estimated permanent deformation life of the pavement by 15%. The results underscore the necessity to consider the nonlinear stress-dependency and cross-anisotropy of unbound granular materials and fine-grained soils in backcalculating pavement layers’ moduli from TSD deflection slopes.

Effects of Mixture Proportions and Molding Method on the Performance of Pervious Recycled Aggregate Concrete.

The use of pervious concrete pavement systems with recycled aggregates is a sustainable and innovative solution to major urbanization challenges such as repurposing construction waste, alleviating urban waterlogging, and reducing heat-island effects. This study aims to investigate the effects of mixture proportions and molding methods on the performance of pervious recycled aggregate concrete (PRAC). To this end, the coarse aggregate size (4.75~9.5 mm, 9.5~16 mm, and 16~19 mm), the molding method (layered insertion-tamping and vibration molding with vibration times of 5 s, 10 s, or 15 s, respectively), and the replacement rate of recycled coarse aggregate (RCA) (0%, 30%, 50%, and 100%, respectively) are considered. The results reveal that the addition of RCA to permeable concrete weakens its permeability. However, the compressive strength of PRAC reaches its maximum value when the RCA replacement rate is 50%. A larger aggregate particle size (16~19 mm) enhances the compressive strength of PRAC, yet decreases the permeability of PRAC. By using vibration molding to fabricate PRAC, an extension to the vibration duration increases the compressive strength, yet concurrently decreases the permeability. Based on the compressive strength and permeability coefficient of PRAC, the optimal mixture proportions and molding method are suggested.

Development and optimization of ultimate performance self-levelling epoxy asphalt concrete (USEA) for prefabricated deck pavement

Developing prefabricated bridge deck pavement systems can fundamentally avoid the drawbacks caused by traditional on-site construction such as poor continuity of the construction process and significant differences in pavement layer performance. Prefabricated bridge deck pavement requires that the bridge pavement is completed with the steel deck together in the factory indoors thus it needs the concrete has better self-leveling characteristics which is rare for existing pavement materials. This article develops the ultimate performance self-levelling epoxy asphalt concrete (USEA) which both have excellent self-leveling properties and comprehensive performance. The study initially established the self-leveling asphalt concrete rolling model based on asphalt film thickness theory and Hertz contact theory and the critical rolling mechanical calculation results states that the aggregate particles and the skeleton amounts are the key elements for fluidity of concrete. Comprehensive the analysis of rolling model and research on the curing reaction process of epoxy resin, the study raises the material configuration of USEA and determine the gradation by the Bailey’s method. To guarantee the better pavement performance of USEA, the study designs the orthogonal test and establish the Entropy-TOPSIS model to optimize the details mortar schemes of concrete. Through the model calculation, the epoxy resin content and the SBS modified asphalt oil-stone ratio are determined as 20 % and 8.0 % respectively. In this scheme, the high temperature stability is 2805 times·mm-1, the low temperature bending strain is 3662με and the fatigue life can reach 1.200 million times. The study provides a material selection for the development of prefabricated bridge pavement system and the technical reference for the development of other self-leveling materials which has constructive practical significance.

Temperature Effects on Traffic Load-Induced Accumulating Strains in Flexible Pavement Structures

AbstractRutting is a major distress mode in flexible pavements, results from the repetitive loading caused by traffic movement. Pavement deformation consists of both recoverable (elastic) and unrecoverable (plastic) components. The continuous movement of vehicles contributes to the overall deformation in the flexible pavement system, involving all pavement components. In regions with hot climates or in the hot summer season, rutting tends to be more prominent due to the substantial reduction in the viscosity of the asphalt binder. This decrease in viscosity, which is inversely linked to rutting, occurs as temperatures rise, leading to a heightened susceptibility of the Hot Mix Asphalt (HMA) blend to rut formation. However, according to studies, a significant amount of permanent deformation takes place in the unbound layers beneath the asphalt course, it is therefore essential to prioritize attention on these layers. Temperature exerts besides viscosity a substantial impact on asphalt stiffness, leading to the transfer of higher vertical deviatoric stresses to the unbound layers beneath the asphalt course (base, subbase, subgrade). This research presents a study integrating the High Cycle Accumulation (HCA) model into a laminar model to determine permanent deformations in the unbound granular layer of flexible pavements and taking into account the temperature dependent stiffness of asphalt. Rutting depths at the end of the design lifetime were computed, accounting for seasonal stiffness variations. It was shown that the softer asphalt behavior significantly increases the development of ruts in the underlaying soil layers. The findings were compared with results obtained from mean annual temperature and the typical equivalent asphalt stiffness utilized in fatigue tests. Additionally, an analysis was conducted to assess whether the timing of road implementation influences settlements throughout the design lifetime. The results suggest that the sequence of seasons is most relevant during the first year of service, showing a distinct effect at that time. However, with a higher number of axle passes, the initial differences fade away, and the curves start to merge.

Modeling the influences of rainfall conditions on nitrogen removal effects of a permeable pavement system

Permeable pavement systems are important facilities for alleviating runoff pollution. However, few studies have focused on outflow nitrogen processes under varying rainfall conditions. In this study, we proposed a permeable pavement system model that can simulate the nitrogen transformation processes. The model was verified with data on outflow rates and outflow nitrogen concentrations (ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), and total nitrogen (TN)) processes for six consecutive rainfall events of a permeable pavement system in Shenzhen, China. Based on the validated model, scenarios were designed to simulate the influences of rainfall conditions on nitrogen removal effects at the single rainfall event and annual scale, respectively. The results indicate that: (i) the model can explicitly describe hydrological and nitrogen processes of the system, and the Nash Sutcliffe Efficiency and percent bias of outflow rates and outflow nitrogen concentrations range from 0.5 to 0.9 and from −22.4% to 24.9%, respectively; (ii) the sensitivity analysis reveals that the empirical coefficient (C) related to evaporation in the gravel layer is sensitive at the annual scale but not at rainfall events scale. Parameters related to mineralization and microbial assimilation (i.e., mineralization rate constant in the gravel layer (k3N,mine), microbial assimilation rate constant of NH4-N, NO3-N, and ON (k1N,bio, k2N,bio, k3N,bio)) are sensitive annually but not at rainfall events scale; (iii) in the single rainfall event simulations, nitrogen removal efficiencies decrease with increasing rainfall return periods but is not significantly affected by rainfall peak coefficients. Nitrogen removal efficiencies improve notably with antecedent dry period extension for single rainfall event with antecedent dry period less than 2 days. In addition, we found that the outflow process significantly affects the outflow nitrogen concentrations process. In annual-scale simulations, extending the antecedent dry period only significantly boosts runoff reduction efficiency and nitrogen removal efficiencies for rainfall events with less than 10 mm.

Exploration of Carbon Fiber Concrete with Silica Fume Admixture for Potential Application in Rigid Pavement Systems

Concrete is a crucial material in civil engineering construction, but its traditional components, coarse and fine aggregate, are becoming increasingly expensive and scarce. This study aimed to investigate the potential of replacing cement with silica fumes and fine aggregate with carbon fibers to improve concrete's hardened properties. Different concrete mixes were created with varying percentages of these replacements. The resulting concrete specimens were tested for compression, split, and flexural strength after curing periods of 7 and 28 days.

Prediction of resilient modulus with pre-post experimental data of undisturbed subgrade soils using machine learning algorithms

The resilient modulus (MR) of subgrade, which shows relationship between stress and unit deformation of a pavement systems under traffic loads, is a design parameter of the pavement structure. Although a cyclic triaxial test apparatus can be used to directly determine the MR of the subgrade in the laboratory, utilizing prediction models based on easily obtainable soil parameters, is a more efficient method when taking time and cost considerations into account. A comprehensive laboratory testing program is designed to create MR prediction models using machine learning (ML) algorithms. 70 undisturbed soil samples are subjected to MR tests, as well as physical and engineering soil properties tests (water content, field density, specific gravity, gradation, consistency limits, unconfined compressive strength, swell pressure, swell percentage). Soil samples are drilled from a highway that has been in operation for over five years.First, a linear model like MLR is used in the study. Next, nonlinear regression models like RF, GBM, LightGBM, CatBoost, and XGBoost algorithms are used. Research findings showed that nonlinear regression models outperformed linear regression models in predicting the MR (R2 > 0.85), with the XGBoost algorithm yielding the best accuracy (R2 = 0.90). Apart from the primary effects such as confining pressure (σ3) and deviatoric stress (σd), it was found that unconfined compressive strength (qu), natural water content (wn), and swelling percentage (SR) are significant parameters in the prediction of MR among all parameters.

Toward Sustainability: A New Construction Method for Electrically Heated Rigid Pavement Systems

Toward Sustainability: A New Construction Method for Electrically Heated Rigid Pavement Systems

Evaluating the effectiveness of innovative pervious concrete pavement system for mitigating urban heat island effects, de-icing, and de-clogging

The Urban Heat Island (UHI) effect and water runoff pose significant challenges in urban environments. This research aimed to mitigate the UHI effect through the implementation of an innovative pervious concrete pavement (IPCP) system. Furthermore, the study investigated the main obstacles associated with conventional permeable pavements, such as clogging and vulnerability to freeze-thaw damage. To achieve this goal, a physical model apparatus was developed to simulate temperature variations, de-icing processes, and clogging incidents in the IPCP system. This apparatus was specifically designed with distinct sections for both the pervious concrete layer and the subbase layer. The findings revealed that careful selection of an appropriate subbase material significantly contributed to reducing the temperature rise (up to 37 %) under varying environmental conditions by enhancing evaporative cooling capacity. Additionally, the pavement simulation apparatus featured a dual-function drainage system. This system not only regulated the water level within the pavement by draining excess water but also addressed clogging (with up to 97 % permeability recovery) and de-icing concerns through the application of air pressure and hot water injection, respectively.

Verification and Analysis of the Pavement System Transfer Function Based on Falling Weight Deflectometer Testing

Verification and Analysis of the Pavement System Transfer Function Based on Falling Weight Deflectometer Testing

A Condition Assessment Tool for Steel Bridge Deck Pavement Systems Based on Data Balancing Methods and Machine Learning Algorithms

The primary challenge in the operation of steel deck pavement systems lies in the inspection and assessment of their condition. Traditionally, manual inspection methods are employed. However, these approaches are not only time-consuming and labor-intensive but also prone to human error. As a result, integrating data-driven machine learning technologies into the evaluation of pavement systems presents a significant advantage in addressing these issues. This study proposes a decision-making tool for estimating the condition levels of steel bridge deck pavement systems by employing classification techniques. To address the issue of class imbalance in the dataset, the SMOTE algorithm is utilized. Additionally, seven different machine learning methods—Light Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Adaptive Boosting, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression—are applied for training. Comparative analysis reveals that the Light Gradient Boosting performs optimally, achieving classification accuracies of 0.841 and 0.929 on the original and synthetic datasets, respectively.

Laboratory and Field Performance Evaluation of NMAS 9.5, 8.0, and 5.6 mm SMA Mixtures for Sustainable Pavement

This study evaluates the laboratory and field performance of stone mastic asphalt (SMA) mixtures with nominal maximum aggregate sizes (NMAS) of 9.5, 8.0, and 5.6 mm. Aggregates and fine aggregates of these sizes were produced using an impact crusher and a polyurethane screen. Mix designs for SMA overlays on aged concrete pavement were developed. Laboratory tests assessed rutting performance using full-scale accelerated pavement testing (APT) equipment and reflective cracking resistance using an asphalt mixture performance tester (AMPT). Field evaluations included noise reduction using CPX equipment, skid resistance using SN equipment, and bond strength using field cores. Results showed that for 8.0 mm SMA mixtures to achieve the same rutting performance as 9.5 mm SMA, PG76-22 grade binder was required, whereas 5.6 mm SMA required PG82-22. The 8.0 and 5.6 mm SMA mixtures showed 22.2% and 25% reduced crack progression, respectively, compared with the 9.5 mm SMA mixtures. Field evaluations indicated that 8.0 mm and 5.6 mm SMA pavements reduced tire–pavement noise by 1.7 and 0.8 dB, increased skid resistance by 8.5% and 2.0%, and enhanced shear bond strength by 150%, compared with 9.5 mm SMA. Overall, the 8.0 mm SMA mixture on aged concrete pavement demonstrated superior durability and functionality toward sustainable pavement systems.

Digitalization of Analysis of a Concrete Block Layer Using Machine Learning as a Sustainable Approach

The concrete block pavement (CBP) system has a surface layer consisting of concrete block pavers and joint sand over a bedding sand layer. The non-homogeneous nature of the surface course of CBP, along with different laying patterns and shapes of block pavers, makes the analysis of CBP cumbersome. In this study, the surface course of CBP was modeled based on the slab action of the block pavers and joint sand, which are connected together in full contact. Four different laying patterns, including herringbone, stretcher, parquet, and square, were modeled using a finite element model. The elastic moduli of the block pavers varied from 2500 MPa to 45,000 MPa, with thicknesses ranging from 60 mm to 120 mm. As a result, modeling of CBP based on slab action can be considered a realistic strategy. In addition, a dataset was created based on quantitative inputs, e.g., elastic modulus and thickness of the block pavers, and qualitative input, i.e., block laying patterns. The approaches of machine learning adopted were support vector regression, Gaussian process regression, single-layer and deep artificial neural networks, and least squares boosting to implement prediction approach based on input and output. The analyses of statistical accuracy of all five machine learning methods showed high accuracy; however, the Gaussian process and deep artificial neural network methods resulted in the most accurate outputs and are recommended for further studies. Based on the machine learning models, digitalization is achieved through the development of simple, user-friendly software for electronic devices in order to perform a preliminary analysis of different laying patterns of CBP. Such a platform may result in less laboratory work and boosts the level of sustainability in concrete block pavement technology.

A Robust Statistical Analysis of Factors Affecting Interface Bonding between Asphalt Pavement Layers

Interlayer bonding within a multilayered pavement system plays an essential role in the overall performance of the pavement structure. Therefore, studying the interlayer shear strength (ISS) as a major index of the bonding strength and its accurate evaluation is of great importance. The main objective of this research is to assess the ISS of pavement using an experimental and statistically rigorous approach. The results showed that the ISS is highly temperature-dependent, experienced a rapid decline with increasing temperature. As tack coat rate increased, the ISS initially increased to reach a pick at 0.8kg/m2 rate and then started to decline. The ISS demonstrated an almost linear correlation with vertical pressure at all temperature levels. Two-way factorial analysis of variance (FAV) underscored the significant impact of any two, namely temperature (T), tack coat rate (TC), and vertical pressure (VP), on ISS results. However, three-way FAV results indicated that the combined effect of T, TC and VP did not hold statistically significant influence on ISS. Moreover, all ISS models developed in this study were statistically significant at a 0.05 significance level, with a good coefficient of determination (R2 =0.73) for multiple linear regression (MLR) and an excellent R2 of 0.976 for polynomial regression (PR).

Study on physical properties and snow-melting performance of multilayer composite conductive-pervious concrete for improving the snow-melting efficiency and energy consumption of ECON

One of the most significant challenges facing transport networks in cold regions is snow accumulation on road surfaces. This can lead to traffic congestion and safety issues. While electrically conductive concrete (ECON) heated pavement systems (HPS) offer several advantages, including stability, and low environmental impact, they also present certain limitations, such as low snow-melting efficiency and high snow-melting energy consumption. In this study, a SiO2 aerogel mortar thermal-insulation layer and a self-compacting concrete structural layer were set underneath the reduced graphene oxide (RGO) composite conductive concrete, and the straight pervious channels were prefabricated, to reduce the snow-melting efficiency and energy consumption through the preparation of multilayer composite conductive-pervious concrete integrated specimen. The initial objective was to investigate the influence of SiO2 aerogel substitution rate on the mechanical properties and thermal conductivity of thermal-insulation mortar. Secondly, the impact of the thickness of the conductive layer and thermal-insulation layer on the mechanical properties of the integrated specimen was analyzed. Finally, the impact of SiO2 aerogel thermal-insulation mortar and straight pervious channels on the integrated specimen's snow-melting performance was evaluated through a snow-melting test, which assessed the efficiency and energy consumption of the snow-melting process. The results indicate that when the SiO2 aerogel substitution rate was 40 %, the thermal-insulation mortar exhibited favorable mechanical and thermal-insulation properties. The 5 cm conductive layer and the 1 cm thermal-insulation layer achieved good mechanical properties of the integrated specimen. In comparison to single-layer composite conductive concrete, the snow-melting time and energy consumption of multilayer composite conductive-pervious concrete integrated specimen were reduced by 23.05 % and 21.30 %, respectively. The straight pervious channels permit the immediate discharge of water produced by snow melting, thereby reducing the heat transfer from the snow-melting board to the water film. The setting of SiO2 aerogel thermal-insulation mortar enables the retention of heat for the heating of the snow-melting board and snow melting, which improves the snow-melting performance.