Asphalt mixtures show complex mechanical behavior due to their heterogeneous structure. Traditionally, the mechanical characterization of asphalt mixture is done through laboratory testing or micromechanical modeling. While laboratory tests and micromechanical models provide reliable measurements and physical interpretability, they are often resource-intensive and demand extensive calibration. Recent advances in machine learning address some of the above issues by enabling accurate predictions, though often lacking physical interpretability and stability. Hence, this study aims to present a novel micromechanics-infused neural network (MINN) framework for predicting asphalt mixture stiffness. The framework embeds micromechanical principles derived from the modified Hirsch model into the neural network's loss function, allowing the model to learn from experimental data while adhering to micromechanics-based constraints. In this study, feature selection is performed using BorutaShap, and Bayesian optimization is applied for hyperparameter tuning. Results show that MINN improves prediction accuracy, interpretability, and robustness.

Inconsistencies between performance data from laboratory-prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self-learning systems that could quickly transfer laboratory-based information to field-based information inside the PMS. The article aims to present a future-ready machine learning-based framework for analyzing the differences between laboratory and field-prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient-boosting decision trees-based framework was able to establish a good relationship between laboratory performance and field performance (R2test > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice.

This paper intends to assess the effect of different parameters on traffic congestion around universities. On the basis of the model outputs, it is possible to propose economic countermeasures for reducing traffic congestion, especially in developing countries. Structural equation modelling was used to assess the relevance between characteristics of students, features of different modes, environmental conditions and daily demand variations with traffic congestion. The Shahid Bahonar University of Kerman in Iran was considered as a case study. The results showed that it is necessary to decrease the demand first. For this purpose, rescheduling courses is essential to distribute classes more effectively within a week. Virtual classes can be used more frequently as a substitute for traditional on-campus courses. The probability of using buses should be increased by reducing waiting time and fares, and promoting their safety. Similarly, taxi use can be increased by improving safety and waiting time. To reduce the likelihood of using private cars, pricing strategies must establish more limitations for using university carparks.