# Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
###### Abstract  
In this study, we introduce an advanced system for sustainable pavement management that leverages cutting-edge machine learning and computer vision techniques to detect and classify pavement damage. By utilizing models such as EfficientNetB3, ResNet18, and ResNet50, we develop robust classifiers capable of accurately identifying various types of pavement distress. To further enhance our dataset, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to synthetically generate images of pavement cracks, thereby augmenting the training data. Our approach aims to improve the efficiency and accuracy of pavement damage assessment, contributing to more effective and sustainable road maintenance practices. This research aligns with the sustainable development goals by fostering innovative methods that extend the lifespan of infrastructure, reducing the need for resource-intensive repairs, and promoting the longevity and reliability of road networks. The outcomes of this study are discussed in terms of their potential impact on infrastructure safety and sustainability, with suggestions for future research directions. This study demonstrates how integrating advanced machine learning techniques into pavement management systems can enhance decision-making, optimize resource allocation, and improve the sustainability of infrastructure maintenance practices. By leveraging big data and sophisticated algorithms, stakeholders can proactively address pavement deterioration, extend asset lifespan, and optimize maintenance efforts based on real-time data-driven insights. pavement damage classification; machine learning; computer vision; deep learning; generative adversarial networks (GANs) +
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such as cracks and potholes, is crucial for maintaining and improving transportation infrastructure [1]. In this work, we evaluate the performance of several state-of-the-art deep learning models, including EfficientNetB3, ResNet18, and ResNet50. These models have been meticulously tested for their effectiveness in identifying and classifying pavement damage. To further enhance our system, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to create synthetic images of pavement cracks. This innovative approach to data augmentation significantly improves the robustness and accuracy of our damage detection system. The central issue addressed in this research is the precise classification of pavement damage using advanced deep learning models. This paper introduces an automated system powered by deep learning algorithms that can accurately identify various types of pavement damage from images. This innovative approach significantly streamlines the assessment process, enhancing both its speed and consistency. The improvements in the reliability and accuracy of pavement condition evaluations enable proactive maintenance strategies, thereby promoting infrastructure sustainability. Our research addresses a vital aspect of transportation infrastructure maintenance: the classification of pavement damage. By leveraging machine learning and deep learning techniques, particularly computer vision, we developed a system that accurately detects and classifies various types of pavement damage, such as cracks and potholes. Our study assesses the performance of models including EfficientNetB3, ResNet18, ResNet50, and SwinGAN. Additionally, we employed a Generative Adversarial Network (GAN) to generate synthetic images of cracks, enhancing our dataset and improving the robustness of our damage detection system. This work aligns with the goals of sustainability by promoting innovative methods to ensure the longevity and reliability of transportation infrastructure, ultimately supporting sustainable development and contributing to the 2030 Agenda for Sustainable Development. Our research contributes to the sustainable development goals by introducing innovative methods that enhance the longevity and reliability of road infrastructure.