Accurate prediction of rolling resistance (RR) is essential for improving vehicle fuel efficiency and supporting policymakers in making sustainable environmental decisions. This study introduces a novel framework that integrates both data-driven and physics-based approaches to enhance RR prediction by incorporating tire-penetration level indicator, the Delta (δ) parameter. The research investigates the relationships between RR, the δ parameter, and texture properties to refine predictive modelling. A portable device was built to measure the in-field δ parameter using tire-pavement interaction. Machine learning (ML) techniques, including multiple linear regression (MLR), random forest regressor (RFR), artificial neural networks (ANN) and finite element method-based (FEM) tire-pavement interaction models were employed to develop and validate the framework. Findings from the FEM tire-pavement interaction model confirmed the reliability of the δ parameter. Exploratory data analysis (EDA) highlighted the strong correlation between texture metrices such as MPD, ETD, and RMS, reinforcing the δ parameter's role in tire-pavement interactions. Comparative analysis of different pavement surfaces revealed that worn surfaces contribute to higher δ parameter values and increased RR. The improvement resulting from the inclusion of the δ parameter is particularly evident in the ANN and RF models, confirming nonlinear interaction effects between tire penetration and surface texture. It was also observed that the obtained RR data follow a non-normal distribution, which most of the previous studies did not consider. A deeper statistical insight showed that the δ parameter has a significant impact on RRC prediction. The primary contribution of this study lies in demonstrating the feasibility of integrating a physics-based tire-pavement interaction parameter into ML models for rolling resistance prediction, thereby bridging mechanistic modelling and machine learning within pavement engineering.

Accurate replication of pavement textures is crucial for tire-pavement interaction studies in laboratory conditions, particularly at the macro-texture level. Existing replication methods often rely on costly equipment and lack standardized procedures for validating surface replicas. This study presents a novel methodology to replicate pavement surfaces and validation. The methodology integrates Mean Texture Depth (MTD) as a 3D texture indicator, silicone rubber casting technology, Finite Element (FE)-based 3D geometric calculations, and smartphone-video-based digital modelling using computer vision. Different asphalt mixtures with distinct surface textures were used for the development of the physical and digital replicas. Physical replicas were produced through silicone casting and epoxy molding, while digital replicas were generated using the Structure-from-Motion technique. Validation was conducted using the standard sand patch test for physical samples and FE-based MTD calculations for digital models. The results showed strong agreement between the replicas and the original pavement samples. Statistical analyses confirmed normal distribution, variance homogeneity, and no significant differences between digital and physical samples. The findings of this study are expected to benefit road authorities in the design and laboratory testing of pavement surfaces, with the aim of evaluating pavement performance.

This research addresses the critical issue of load transfer efficiency (LTE) in jointed plain concrete pavements (JPCP), with a specific focus on the role of dowel bars in ensuring optimal load transfer and providing a comfortable ride for vehicles. While experimental studies have investigated factors like joint width, slab thickness, concrete strength, and dowel bar size that influence LTE, they are limited in their ability to accurately replicate real-world conditions and can be time-consuming. To overcome these limitations, finite element modelling (FEM) is employed as a powerful tool for simulating complex loading conditions and analyzing stress and strain distributions in pavements. The primary objective of this research is to develop an advanced FE model that incorporates the forklift tire-pavement interaction, enabling precise analysis of complex loading conditions in industrial pavements and the impact of various rigid pavement parameters on load transfer. By explicitly considering the interaction between the tire and pavement, the proposed model will provide an extensive and robust numerical tool for designers and engineers. Additionally, this study represents a novel framework to integrate concrete pavement dowel bars and complex tire modelling using FEM. The developed methodology holds significant promise in optimizing the design of dowel bar systems and back-calculating the pavement parameters for rolling weight deflectometers.

A major contributor to GHG emissions is the transportation sector, particularly pavement transport. The limited understanding of tire-pavement interactions leads to inaccurate predictions of these emissions, particularly from rolling resistance (RR). Traditional methods for predicting RR are constrained by their limited applicability and inability to account for the complex dynamics of tire-pavement interactions, resulting in poor prediction accuracy. These limitations make it challenging for policymakers to make proper decisions, as existing methods are manual and labour-intensive. This study aims to develop an automated system to capture tire-pavement interaction data using the Laser Crack Measurement System (LCMS). To the best of the authors' knowledge, no robust technique currently exists for automatically calculating tire penetration-related information from LCMS data to predict RR. Therefore, this research explores machine learning (ML)-based models to reduce uncertainties in existing approaches and enhance RR predictions using automated LCMS data. It examines the relationships between RR, tire penetration volume, and the characteristics of the Dutch pavement network, comparing the results with those of commonly used RR prediction models. The study introduces an automatic tire penetration calculation approach using LCMS data to assess the impact of tire penetration volume and depth on RR in relation to surface properties. The findings reveal that traditional empirical models show poor correlations between RR and texture indicators, whereas ML-based models significantly improve the accuracy of RR predictions. These results could inform the development of strategies to reduce GHG emissions from pavement transport, supporting global efforts to combat climate change and achieve the goals of the Paris Agreement.

Pavement materials that could enhance the mechanical properties of open-graded porous asphalt mixtures in long-term service periods could offer a solution to produce long-life pavements, causing a reduction of interventions' needs, as well as the associated disruptions to road users and user costs. One option to improve the longevity of open-graded porous mixtures is with the use of epoxy asphalt that, despite its high initial cost, offers enhanced longevity that might offset any future user and intervention costs. This study aimed to evaluate the durability of plant-produced epoxy-modified open-graded porous asphalt mixtures. A batch production plant was employed to produce loose mixtures, which were used to pave a test road in the Province of Gelderland, the Netherlands, and compact specimens in the laboratory. Control mixtures with a non-epoxy-modified asphalt binder were also produced in the same plant. The durability of laboratory- and field-compacted mixtures was evaluated by conducting indirect tensile tests before and after oven conditioning. Results illustrated that the epoxy-modified asphalt demonstrated the highest strength and stiffness values, while the strength was reduced after conditioning in a water bath with the retained strength within the allowable specification limits. This attribute was confirmed from drill cores obtained from the test road after one year in service. Also, the materials compacted in the field had slightly higher strength and stiffness values than the laboratory-produced mixtures. Although the results provided have illustrated the improvement of durability of open-graded porous asphalt with implementing epoxy modification, further evidence from the test road over the years is needed for validation.

This study aims to correlate the chemical and rheological properties of bitumen at different ageing states and understand the chemical mechanisms of bitumen degradation due to ageing. The relationship between Fourier transform infrared (FTIR) spectral data and rheological results is investigated using partial least squares (PLS) regression integrated with two variable selection methods. The spectral region of 1800 – 800 cm1 is identified as the most informative for accurate estimation of the rheological properties of bitumen. Variable selection methods, particularly moving windows (MW), improve the prediction accuracy of the regression models.

This paper presents a United Atom (UA) force field for simulating hydrocarbon molecules in bituminous materials, integrating explicit hydrogens into beads with their parent atom. This method simplifies all-atom molecular models, significantly accelerating Molecular Dynamics (MD) simulations of bitumen by 10 to 100 times. Key advantages include halving the particle count, eliminating complex hydrogen interactions, and decreasing the degrees of freedom of the molecules. Developed by mapping forces from an all-atom model to the centers of mass of UA model beads, the force field ensures accurate replication of energies, forces, and molecular conformations, mirroring properties like pressure and density. It features 17 bead types and 287 interaction types, encompassing various hydrocarbon molecules. The UA force field's stability, surpassing all-atom models, is a notable achievement. This stability, stemming from smoother potential energy surfaces, leads to consistent property measurements and improved stress tensor accuracy. It enables the extension of MD simulations to larger spatiotemporal scales, crucial for understanding complex phenomena such as phase separation in bituminous materials. This foundational work sets the stage for future developments, including refining parameters and introducing new bead types, to enhance the modeling capabilities of the force field, thereby advancing the application and understanding of bituminous materials.

Bituminous binders naturally age, affecting the properties and performance of asphalt pavements. The physical and chemical characteristics of binders are influenced by environmental factors, leading to a decline in their performance and durability. Therefore, it is essential to understand the mechanisms of binder environmental aging to design more resilient and long-lasting asphalt pavements. This study examines the effects of temperature, liquid, and vapor water on binder aging to develop more durable pavements. Aging was induced at three temperatures (60°C, 70°C, 85°C) under dry air, 90% relative humidity, and water immersion conditions. Field-aged samples were also analyzed to compare with laboratory-aged samples. Fourier-transform infrared spectroscopy (FTIR) and dynamic shear rheometer (DSR) were used to assess chemical and rheological changes. To assess the similarity between samples and identify the lab aging protocol closest to field aging, Hierarchical Cluster Analysis (HCA) was employed for data analysis.

The linear viscoelastic behavior of materials is represented using mechanical models of choice, which are further utilized in different numerical investigations, such as finite element simulations and discrete element simulations. Burger's model is one of the widely adopted mechanical models and remains highly favored in contemporary research due to its multiple advantages. Specifically, it excels in representing long-term creep and stress relaxation behavior in a relatively simplified manner. Accurate identification of the long-term behavior for the viscoelastic material, particularly asphalt concrete, is crucial, as it serves as a key indicator of asphalt pavement performance over its service life. However, past research studies show that the parameters of Burger's model should be back-calculated from experimental data only within a limited range of frequency, otherwise, the parameters fail to represent the true material behavior. To the best of the authors’ knowledge, there is no approach for researchers to obtain the critical frequency range in which the experiments should be performed. Therefore, this study proposes a novel framework to find the critical frequency range to obtain appropriate model parameters of Burger's model, to better characterize the viscoelastic behavior of the materials. To examine the framework, asphalt concrete mixtures are used as examples in this study. Necessary laboratory tests including complex modulus tests and stress relaxation tests, are performed on two distinctive types of asphalt concrete mixtures. The generalized Maxwell model with different number of Maxwell chains are used to evaluate the performance of Burger's model. Furthermore, since commercially available finite element packages generally do not have a direct built-in Burger's model, the article shows a way of implementing Burger's model in finite element simulation. The simulations corresponding to the laboratory tests are carried out in both frequency domain and time domain to thoroughly evaluate the performance of Burger's model. The optimal frequency range of 0.1–20 Hz for the examined mixtures is found to significantly improve the accuracy of the descriptive master curve. The results also suggest that the generalized Maxwell model requires a minimum of four Maxwell chains to maintain good performance in accurately characterizing the behavior of asphalt mixtures. However, adding more Maxwell chains beyond a critical limit may not provide significant benefits. Finite element simulations demonstrate that the stress relaxation behavior predicted by the obtained Burger's model parameters aligns more closely with experimental data over longer time intervals. This makes Burger's model a strong choice for aiding in the design of simulations for studies focused on the long-term behavior of materials.

Developed by Delft University of Technology, the tri-component polyurethane modified cold binder (PMCB) displays impressive durability and strength in asphalt mixtures, showing promise as a reliable binder for cold in-place recycling. However, when applying PMCB for rapid, in-situ recycling, the presence of moisture in reclaimed asphalt pavement (RAP) poses a significant challenge. To address this, an innovative approach involving treatment of the wet RAP with Calcium dioxide (CaO) prior to the integration of PMCB was tested. Evaluation methods used included the Indirect Tensile Test (ITT), followed by the calculation of the Indirect Tensile Strength Ratio (ITSR) to assess moisture susceptibility. Furthermore, Cantabro tests were performed to determine the material loss under abrasion and weathering conditions. These assessments underscored the feasibility of this approach. The treatment of wet RAP with CaO has proven a viable strategy for rapid in-situ recycling with PMCB, contributing to sustainable pavement construction. In addition, the research identified that a 5.5% concentration of the PMCB binder maximizes structural integrity and performance in the considered RAP.

The ever-growing need to build roads to meet the necessary transportation demands is challenging, especially for developing countries. Low-volume roads (LVRs) are usually the backbone of catalyzing economic growth in these countries. With impediments surrounding Petroleum bitumen (price fluctuations) and environmental concerns, scientists are putting their effort into finding an alternative. The presented research is an attempt to check if Natural asphalt can be used as a full or partial replacement of the Petroleum bitumen. To the best of the authors' knowledge, only limited studies have focused on characterizing and understanding the engineering properties of Natural asphalt. The available techniques do not provide reliable information to the road authorities and hence they are discouraged from using it in practice. Particularly for countries, where the Natural asphalt source is available, the overall dependence on importing the Bitumen could be substantially reduced. Empirical and experience-based design criteria may not be sufficient as the standards were never developed for such materials, hence, a scientific approach is required before bringing it into practice. In this research, Natural asphalt sourced from different locations in Nigeria was assessed. Before performing the mixture level tests using Marshall and Cantabro design methods, the rheological and fatigue properties of the extracted Natural bitumen were examined in the laboratory. In the design of the experiment, various percentages of Natural asphalt were added between 0 % and 20 % by total mix weight; implying that the remaining required fraction of binder was fulfilled by the addition of petroleum bitumen. By using a ranking system (supported by statistics), an optimal design of mixture was obtained which was used in the field (exposed to normal traffic) at 30 different sections.