<p>The enhancement of machine learning (ML) models relies heavily on the volume and integrity of data, emphasizing the importance of efficient data collection and rigorous ground-truth labeling. While deep learning, particularly its subset convolutional neural network (CNN), has shown promise in crack detection, there remains a need for sophisticated algorithms to identify structural defects accurately. This study presents a novel deep CNN model tailored for the binary classification of concrete surfaces, addressing a significant need in infrastructure engineering. The deep CNN model was developed using a comprehensive dataset (40,000 images each measuring 227 × 227 pixels). Various metrics, including precision, sensitivity, binary accuracy, and F1 score, were utilized to evaluate the model's performance. Additionally, the model's generalization capability was assessed by testing its proficiency in accurately classifying unseen data. The study demonstrates that the model’s predictive performance improves with additional epochs, indicating enhanced learning over learning cycles. Validation metrics suggest potential generalization capability despite slight accuracy declines, showcasing the model’s robustness in accurately classifying positive instances. The findings reveal significant advancements in deep CNN models for concrete material classification, surpassing previous comparable models. Employing CNN models holds promising outcomes for quality control and repair processes in infrastructure engineering applications. Future research directions include exploring the application of the deep CNN model to classify alternative materials and assessing its generalization capability using larger and more diverse datasets. Overall, this study contributes to the advancement of ML techniques in infrastructure engineering, with implications for optimizing material classification processes and enhancing infrastructure repair outcomes.</p>