# Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
## 3 Data and Methods
### Modeling
With its 18-layer structure, ResNet18 offers a more compact alternative to deeper models like ResNet50 while still incorporating the innovative residual connections characteristic of the ResNet family. These residual connections address the challenge of vanishing gradients in deep network training by allowing information to bypass certain layers, thus promoting better gradient flow and more effective learning across the network. Each residual block in ResNet18 includes two convolutional layers, batch normalization and ReLU activation, leveraging these connections to enhance feature extraction. This design not only reduces complexity but also enhances computational efficiency. ResNet18 efficiently captures complex features with fewer computational resources, making it suitable for environments where computing power is limited. It excels in analyzing pavement distress imagery, utilizing its residual blocks to extract and propagate features critical for the accurate classification of various pavement issues. While ResNet18 may not capture as detailed features as deeper models, its optimized design strikes a balance between complexity and computational efficiency [38,39]. This balance renders ResNet18 particularly valuable in scenarios requiring lightweight models or strict computational constraints. It proves effective for real-time processing and deployment on resource-constrained devices, offering a reliable solution for precise and efficient pavement distress classification. The third structure used in our research is the EfficientNetB3 network. EfficientNetB3 is part of the EfficientNet family, which marks a significant advancement in developing efficient and effective convolutional neural network architectures. Unlike traditional models like ResNet, which mainly increase network depth to enhance performance, EfficientNetB3 uses a unique compound scaling method that optimizes depth, width, and resolution together. This method allows it to achieve better performance with fewer parameters and makes it exceptionally resource-efficient, ideal for use in environments with limited resources like mobile or edge computing devices [40]. The EfficientNetB3 architecture (Figure 5) is meticulously designed with multiple blocks that specifically enhance feature extraction efficiency. These blocks incorporate depth-wise convolutions and squeeze-and-excitation modules to boost operational effectiveness. Depth-wise convolutions, which apply a separate filter to each input channel, reduce computational demands significantly by limiting the number of parameters while maintaining robust performance. Squeeze-and-excitation modules heighten the model's focus on important features during training, significantly enhancing accuracy and the network's ability to distinguish between various inputs. Figure 4: A graphical description of the ResNet18 architecture, adapted from [36]. EfficientNetB3 stands out for its lightweight design, which provides quick inference times and a minimal memory footprint, making it exceptionally suited for real-world scenarios where computational resources are limited. Despite its compact structure, EfficientNetB3 does not sacrifice performance. It excels at extracting both hierarchical features and spatial dependencies from images of pavement distress, considerably improving its classification capabilities over more traditional models like ResNet [30]. In summary, EfficientNetB3 provides an excellent mix of both efficiency and effectiveness, establishing it as a preferred option for classifying pavement distress. The model's success comes from its innovative compound scaling method and thoughtful architectural choices, which allow it to deliver outstanding performance under the typical constraints of real-world applications. Additionally, using SwinGAN to augment the dataset further boosts EfficientNetB3's ability to provide precise and dependable results in pavement distress classification.