Traditional methods for predicting pavement performance rely on complex finite element modelling and empirical equations, which are computationally expensive and time-consuming. However, machine learning models offer a time-efficient solution for predicting pavement performance. This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. The input variables include axle load, truck load, traffic speed, lateral wander modes, asphalt layer thickness, traffic lane width and tire types, while the output variables consist of number of passes to fatigue damage, number of passes to rutting damage, fatigue life reduction in number of years and rut depth at 1.3 million passes. A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. In contrast, linear regression and KNN demonstrated the lowest performance, with MSE values up to 188% higher than CatBoost. This study concludes that integrating machine learning with finite element analysis provides further improvements in pavement performance predictions.

In situ monitoring of pavement health has been getting much attention due to the efficiency, reliability and accuracy of data. This review consists of various embedded as well as nondestructive sensing options that have been used to perform analysis on pavement health either by simply calculating horizontal and vertical strains under pavement layers or by crack detection models inside pavement structures by supplementing information from moisture, temperature and traffic related sensors. With optimum integration of such combination sensors, engineers can predict the optimum rehabilitation time of the pavements and reduce a huge amount of budget spent on infrastructure reconstruction.

The performance of SBS-modified asphalt mixtures can be enhanced by incorporating various types of fibers offering a cost-effective alternative to increasing the SBS content. In this study, three different fibers, Basalt, Polyester, and Lignin fibers were added to a 3% SBS-modified bitumen binder, and their performance was compared to a 7% SBS mixture without fibers. Laboratory tests, including indirect tensile strength and dynamic shear rheometer tests, were used to evaluate the mixtures. The indirect tensile strength of all samples was assessed at loading rates ranging from 10 to 70 MPa/s, while stiffness moduli were tested at frequencies of 5 Hz, 3.5 Hz, 1.9 Hz, and 1.2 Hz. Finite element simulations using the Burger’s Logit model have been performed and microstrain analysis has been carried out to assess rutting and fatigue damage, complementing the experimental results. The findings demonstrated that fiber-modified mixtures exhibited superior performance, with increased tensile strength and complex shear modulus. Among the fiber types, Basalt fiber showed the best results, outperforming the others, while Polyester and Lignin fibers displayed nearly identical performance. The Basalt fiber mixture outperformed the SBS-7% mixture by 25% in rutting resistance and 28% in fatigue damage.

Impacts of autonomous truck’s passes on pavement have been analyzed in this research. Two types of lateral positioning namely zero wander and uniform wander along with a super single wide tire and a dual tire have been analyzed with variable traffic speeds in ABQUS. The study concludes with the results in favor of usage of a super single wide tire under a uniform wander mode. The highest amount of pavement damage in terms of maximum rut depth is caused by the dual wheel assembly moving under a zero-wander mode. The magnitude of rut depth increases by a factor of two when a dual tire assembly is used instead of a wide tire assembly. At a uniform wander mode, rut depth increases by 0.2 mm for every 10 km/h decrease in traffic speed within 90 km/h to 70 km/h range.

Two different tire configurations consisting of a dual tire and a super single wide tire having different range and distribution of contact pressures have been analyzed. Along with the effect of speed on development of pavement damage at speeds of 5, 50 and 80 km h⁻¹ under zero and uniform wander modes. Results show that at super slow speeds of 5 km h⁻¹, at dual wheel moving at zero wander mode, decrease in fatigue life of the pavement is 3.5 years, which is 1.45 times more than the dual wheel moving at uniform wander and 3.4 times more than wide tire moving at uniform wander mode. The difference between fatigue damage at different lateral wander modes is prominent at speeds greater than 50 km h⁻¹. A wide tire performs better than the dual wheel under zero wander configurations.

Selection of optimum platoon pattern based on types of trucks inside the platoon, the number of trucks in the platoon, headway distance, interplatoon distance as well as the use of different lateral wander modes for autonomous trucks has been analyzed. The objective of this research is to study the impacts of axle configurations, truck grouping, headway distance and lateral wander options on the performance of truck platoons. Four different headway distances from 2 to 5 m are compared. The first platoon PT-1 only consists of semi trailers, the second platoon PT-2 only consists of rigid body trucks and the third platoon PT-3 consists of equally distributed random traffic mix. Analysis has been conducted using the dload subroutine for projecting zero wander and uniform wander movements for each truck in the platoon on a three layered pavement crossection at vehicle speeds of 90 km/h for a total of 15 years of pavement lifetime consisting of 1.4 million equivalent single axle loads in finite element software ABAQUS. Results show that PT-3 platoon yields the minimum accumulation of damaging strains when compared against other platoon types. A headway distance of 5 m is suggested when using a zero wander mode and 3 m when using a uniform wander mode. In case of zero wander mode, fatigue life of the pavement decreases by 1.2 years and the use of uniform wander mode delays the rutting by 1.6 years, thereby increasing lifetime of the pavement.