In this study, Marshall test results of hot mix asphalt samples having various Hydrated Lime (HL) content rates were modelled using Fuzzy Logic (FL) and Artificial Neural Networks (ANN). Test sets having various HL content were prepared in order to investigate the effect of HL. Marshall Stability test was performed on the samples to obtain the optimal Asphalt Content (AC) ratio. The results were evaluated in order to determine HL additives’ sensitivity on the mixture parameters. The Marshall Test results were used to develop the FL and ANN models. The models developed produced acceptable estimations of the mixture parameters.

If asphalt pavements are exposed to cold weather conditions and high humidity for long periods of time, cracking of the pavement is an inevitable consequence. In such cases, it would be a good decision to focus on the filler material, which plays an important role in the performance variation in the hot asphalt mixtures used in the pavement. Although the use of hydrated lime as a filler material in hot asphalt mixtures is a common method frequently recommended to eliminate the adverse effects of low temperature and to keep moisture sensitivity under control in asphalt pavements, the sensitivity of the quantities of the material cannot be ignored. Therefore, in this study, an amount of filler in the mixture was replaced with hydrated lime (HL) filler additive at different rates of 0%, 1%, 2%, 3% and 4%. These asphalt briquettes, designed according to the Marshall method, have optimum asphalt contents for samples with specified HL content. In this study, where the temperature effect was examined at five different levels of −10 °C, −5 °C, 0 °C, 5 °C and 25 °C, the samples were produced in two different groups, conditioned and unconditioned, in order to examine the effect of water. The indirect tensile strength (ITS) test was applied on the produced samples. Experimental study showed that HL additive strengthened the material at low temperatures and made it more resistant to cold weather conditions and humidity. In the second part of the study, two different prediction models with varying configurations were introduced using nonlinear regression and feed-forward neural networks (FFNNs) and the best prediction performance among these was investigated. Examination of the performance measures of the prediction models indicated that ITS can be accurately predicted using both methods. As a result of comparing the developed models with the experimental data, the model provides significant contributions to the evaluation of the relationship between the ITS values obtained with the specified conditioning, temperature changes and HL contents.

The primary objective of this study is to understand the aggregate–emulsion interaction in slurry seal coatings and to obtain cost-effective, improved performance characteristics by overcoming the restrictive effects of aggregate chemistry on workability through hybrid designs. In this study, the interactions of granite (GR), basalt (BA), and limestone (LS) aggregates with bitumen emulsion were examined; specifically, limestone-substituted designs were analyzed to overcome workability problems stemming from the high reactivity of basalt and to achieve optimum performance. Laboratory specimens were subjected to mixing time, cohesion, Wet Track Abrasion (WTAT), and Loaded Wheel (LWT) tests in accordance with the procedures specified by the International Slurry Surfacing Association (ISSA); and the effect of the determined optimum emulsion content on performance was analyzed with ±2% sensitivity. While experimental findings indicated that the predicted optimum emulsion contents for all selected aggregate types satisfied the specification limits, the mixture with 30% basalt substitution (LS70+BA30) among the hybrid designs achieved the highest design compatibility by providing an exact match between theoretical and experimental optimum points. Conversely, despite having lower design sensitivity (66.7% match), the mixture with 50% basalt substitution (LS50+BA50) offered a superior alternative for situations requiring quick opening to traffic by exhibiting 54% higher early cohesion strength (20 kg-cm) at 120 min compared to pure limestone. Statistical analyses confirmed that aggregate origin is the most determinant factor on mixing time and that the fluidity characteristic of the system is predominantly controlled by water content. Furthermore, correlation matrices demonstrated the need to optimize the liquid phase balance in hybrid designs according to aggregate mineralogy by revealing the rheological sensitivity developed by limestone towards water and granite towards emulsion. Through the study outputs, the restrictive effects of aggregate chemical content on workability—which have not been sufficiently detailed in the literature compared to the frequently discussed effects of particle size distribution and mineral filler in slurry seal mixtures—were identified, and solution strategies were developed.

Accurate, rapid, and consistent evaluation of pavement condition across large-scale road networks is critical for sustainable maintenance and rehabilitation planning. However, conventional approaches largely rely on manual visual inspections, which are time-consuming, subjective, and difficult to implement at the network level. In this study, a semi-automated pavement distress evaluation framework that integrates field-based assessment with computer vision techniques is proposed. The study was conducted on a 3 km roadway network located within the Yıldız Technical University Davutpaşa Campus. Field-based distress observations were used as reference data, while street-level images obtained from the Mapillary platform were analyzed using a deep learning-based YOLOv8 model trained on the RDD2022 dataset, which was specifically developed for road distress detection. The analysis focuses on crack and pothole distress, which have a dominant influence on PCR and are highly distinguishable in image-based approaches. Correlation analyses between automated detection results and field-based data demonstrate a strong agreement, reaching values of approximately $\rho \approx 0.90$ in some routes. These findings indicate that these distress types are effective in representing variations in pavement condition. The results demonstrate that multi-source image data and deep learning-based detection methods can be reliably used for section-level pavement condition assessment. The proposed approach addresses a key gap in the literature by transforming image-level detections into engineering-based decision-support information. Furthermore, by leveraging publicly available data sources, the framework offers a low-cost and scalable solution that enables rapid preliminary assessment over large road networks, thereby providing significant potential for sustainable infrastructure management and the development of data-driven maintenance strategies. Several practical challenges encountered during the detection process—including sensitivity to contrast enhancement parameters, false positives from shadows and surface reflections, heterogeneous image resolution across crowdsourced imagery, and training distribution gaps for locally prevalent infrastructure features—are discussed, and directions for reducing human intervention through adaptive preprocessing and targeted model refinement are identified.