# Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
## 2 Literature Review  
Ensuring the health and integrity of pavements is essential for maintaining efficient and safe infrastructure systems worldwide. Pavement health assessment, particularly through accurate crack detection, plays a pivotal role in infrastructure maintenance strategies [5; 6]. Cracks in pavements, whether on roads, airport runways, or other transportation networks, can lead to accelerated deterioration and safety hazards if left untreated [7]. Effective assessment and early detection of cracks not only extend the lifespan of pavements but also minimize the need for costly repairs and ensure uninterrupted operation of critical transportation routes [8]. In recent years, advancements in machine learning, particularly convolutional neural networks (CNNs), have revolutionized crack detection by automating and enhancing the accuracy of detection processes [9]. This literature review explores the evolving methodologies and technologies in pavement health assessment, focusing on the role of machine learning in optimizing infrastructure management practices. The literature on pavement damage classification and detection reveals significant advancements in the application of machine learning and deep learning techniques. Recent studies emphasize the role of computer vision in automating the assessment process, reducing reliance on manual inspections, and improving accuracy and efficiency [10]. Key developments include the use of convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) for image analysis and data augmentation, respectively [11; 12]. Researchers have demonstrated the effectiveness of various models, such as EfficientNet and ResNet, in identifying different types of pavement distress [13; 14]. Furthermore, innovations like the Swin Transformer and other vision transformers have introduced new paradigms in handling complex image data [15]. This literature review explores these advancements, providing a comprehensive understanding of the current state of the art and identifying the technological foundations that underpin our research. Integrating insights from recent research into the application of advanced machine learning and deep learning techniques in pavement distress classification reveals a strong trend toward sustainability in infrastructure maintenance. These innovations not only enhance road functionality and lifespan but also contribute significantly to environmental and economic sustainability. Advancements such as the Pavement Image Classification Transformer (PicT) use vision transformers to effectively model the relationships between image patches through self-attention mechanisms. PicT's dynamic pseudo-labeling process reduces manual labeling efforts and costs, enhancing the adaptability of models to recognize new or rare types of distress, marking a significant step towards sustainable machine learning applications in road maintenance. Similarly, the adoption of Deep Metric Learning for few-shot learning underscores efficiency in adapting to various pavement distress types with minimal data requirements. This method conserves resources and reduces the carbon footprint associated with extensive data collection, representing a substantial advance toward sustainable computing in infrastructure management. In [16], the authors illustrate how sophisticated algorithms can effectively manage infrastructure needs and environmental impacts through predictive modeling techniques used in maritime port emissions and pavement performance prediction models. For instance, predictive modeling of sulfur dioxide emissions in maritime facilities using machine learning enables proactive environmental strategies and regulations, helping to minimize the ecological and health impacts of port operations. In the assessment of road pavement health and safety, crack detection is crucial for proactive maintenance strategies.