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Semantic segmentation and deep CNN learning vision-based crack recognition system for concrete surfaces: development and implementation

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Abstract

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.

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Acknowledgements

The authors extend their appreciation to Researchers Supporting Project number (RSP2025R433), King Saud University, Riyadh, Saudi Arabia.

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  1. Department of Civil Engineering, College of Engineering, King Saud University, 12372, Riyadh, Saudi Arabia

    Yassir M. Abbas & Hussam Alghamdi

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Abbas made substantial contributions to the conception and design of the study. Abbas and Alghamdi were responsible for data acquisition, analysis, and interpretation. Abbas conducted the machine learning modeling and statistical analyses. Alghamdi contributed to the development of the validation framework and supervised the research process. Abbas drafted the manuscript, and Alghamdi critically revised it for important intellectual content. All authors reviewed the manuscript, approved the version to be published, and agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Abbas, Y.M., Alghamdi, H. Semantic segmentation and deep CNN learning vision-based crack recognition system for concrete surfaces: development and implementation. SIViP 19, 339 (2025). https://doi.org/10.1007/s11760-025-03913-2

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