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.

The design of asphalt pavement in many developing nations still relies on an empirical approach, often leading to either premature failure of the pavement or overdesign. The transition from an empirical approach to semi-mechanistic or mechanistic was felt by past researchers, and many advanced tools based on these approaches have been developed. Computational tools, like finite element (FE) analysis, are capable of handling complex material properties of pavement materials under nonuniform loading conditions. Asphalt mixes are widely known to exhibit viscoelastic behaviour based on temperature and loading conditions, while the response of unbound materials under cyclic loading is stress dependent. Due to the complexity of the entire process, numerous pavement design tools treat them as purely elastic materials. This study aims to develop a finite element based, simple, and practical framework to assess the structural response of asphalt pavement under overloading and varying temperature conditions in a tropical climate. The framework offers a straightforward method for the determination of time dependent viscoelastic parameters of the asphalt mixture using creep compliance test. The nonlinear stress-dependent behaviour of unbound granular materials (UGMs) in different layers has also been presented based on repeated load triaxial compression testing. It was concluded that overloading and increasing mix temperature severely affect pavement performance. A 25 % overloading resulted in a reduction of subgrade rutting life by 62.33 %, whereas an increase in mix temperature by 10° C at intermediate temperature reduced asphalt fatigue life by 29.34 % and subgrade rutting life by 42.03 %.

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.

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.

Emulsion-treated aggregate base layer structure is one of the popular choices to form a more stabilized layer, in which aggregates are treated with slow-setting bitumen emulsion. The aim of the study is to propose a three-dimensional finite element model that is capable of showing the potential benefits of using an emulsion-treated aggregate layer. The damaging effect of overloading and high temperature in a tropical climatic condition on the pavement response have been highlighted in this study. The analyses showed that by using an emulsion-treated aggregate layer, the rut resistance and fatigue life considerably improve.

In the context of climate change and global warming, the attention on the environmental cost of pavements is increasing. To scientifically quantify the environmental cost of pavements, accurate prediction of rolling resistance and fuel consumption is important. In this paper, a comprehensive review on rolling resistance of asphalt pavements and its environmental impact was presented. At first, the commonly used definitions of rolling resistance and texture characterisation methods of pavement surface were introduced. Then, the influence of different factors on rolling resistance was discussed. Next, the measuring and modelling approaches of rolling resistance were reviewed. At last, methods which can be used to predict fuel consumption and environmental impact were presented. It was found that an ideal approach for texture characterisation of pavement surface is to make use of the entire wavelength spectrum of road profiles and consider the enveloping curve of tire treads. Furthermore, the fact that rolling resistance can be influenced by different factors introduces difficulties in accurate measurement and modelling of rolling resistance. Moreover, testing methods and conditions have a significant effect on the empirical modelling of rolling resistance, while it is difficult and time-consuming to consider all the energy loss in the computational modelling of rolling resistance. In addition, the prediction of fuel consumption and environmental impact highly depends on the formulating methods and measuring conditions. The work presented in this paper will help to gain more insight into rolling resistance and its environmental impact, which ultimately promotes the construction of environmentally friendly pavements.