With the increasing scarcity and cost of virgin materials for asphalt mixtures, the exploration of alternative components has intensified. Reclaimed asphalt pavement (RAP), crumb rubber (CR), steel slag (SS), and waste engine oil (WEO) have emerged as promising alternatives. Individually, RAP enhances rutting resistance but may compromise cracking tolerance; CR boosts cracking resistance; WEO affects cracking and rutting differently; and SS can influence moisture sensitivity. However, their combined impacts on asphalt performance, specifically on moisture damage, rutting, and cracking resistance, remain underexplored. In this study, 44 mixtures were assessed with varying RAP (0–75%), WEO (0–15%), and CR (0–15%) contents, alongside a constant SS aggregate (0% or 20%). The results indicate that specific combinations of these alternative materials can satisfy all performance thresholds for rutting, cracking, and moisture damage. To pinpoint ranges of optimal material contents for different high-traffic scenarios, prediction models were crafted using techniques like feed-forward neural network (FNN), generalized linear model (GLM), support vector regression (SVM), and Gaussian process regression (GPR). Among these, GPR demonstrated superior efficacy, effectively identifying regions of satisfactory performance.

This literature review explores the application of machine learning (ML) techniques in civil engineering material testing, with a focus on asphalt mixtures, concrete properties, and pavement system classification. The review provides a comprehensive comparison of various ML models, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), Gradient Boosting (GB), and Gaussian Process Regression (GPR), assessing their strengths and limitations in predicting material performance. Key findings indicate that ensemble methods, such as Gradient Boosting and XGBoost, consistently outperformed other models in terms of prediction accuracy and handling nonlinear relationships, although they require significant computational power. In contrast, simpler models like SVM and ANN demonstrated strong predictive capabilities with smaller datasets but were prone to overfitting and computational challenges. Additionally, unsupervised learning methods, such as K-means clustering and Principal Component Analysis (PCA), proved effective in classifying pavement conditions and detecting anomalies, with K-means offering simplicity and efficiency at the cost of sensitivity to initialization and cluster definitions. The review concludes by emphasizing the potential of hybrid and ensemble models to improve prediction accuracy and reduce computational costs, highlighting the need for further research to address data availability, model interpretability, and practical implementation challenges in real-world applications.

Asphalt mixture laboratory cracking evaluation methods often rely on the use of notched test specimens that are tested using a constant displacement rate to obtain fracture properties. Disk-shaped compact tension (DCT) test is one of the most widely adopted fracture tests for asphalt mixtures. It is standardized as ASTM D7313-20, Standard Test Method for Determining Fracture Energy of Asphalt Mixtures Using the Disk-Shaped Compact Tension Geometry. Variability in load-displacement curves among replicates of the same asphalt mixture type complicates the calculation of performance indices obtained from asphalt fracture tests for cracking evaluation. Abnormalities such as irregularities under the curve, unusual maximum loads, or slopes after the peak can occur. To improve the precision and dependability of cracking performance assessments, it is essential to detect and remove these atypical replicates. This research examines four outlier detection methodologies: the David, Hartley, and Pearson (DHP) method, the Tietjen-Moore (T-M) method, the uniform variance estimator (UVE), and the robust estimator (RE), focusing on their effectiveness and precision when applied to DCT performance indices. Both fracture energy and post-peak index (PPI) parameters were evaluated, with the RE method consistently demonstrating superior outlier detection capabilities. This was attributed to the RE method’s adaptability to both normally and nonnormally distributed data and its heightened sensitivity to outlier presence. As the nonnormality of the PPI datasets escalated, the T-M method tended to overestimate outliers, thereby further substantiating the robustness of the Mahalanobis distance (MD) methods, which include both UVE and RE. Even though the UVE method significantly surpassed the DHP method in outlier detection, it still showed sensitivity to existing outliers. Collectively, these findings underscore the significance of accounting for the nature of distribution for the dataset when selecting an outlier detection method and corroborate the potential of the RE method as a versatile and robust choice for outlier detection.

Asphalt concrete overlays are one of the most common methods for rehabilitating and preserving existing pavements. However, their service life in cold climates is often shortened by premature crack...