Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision

Kalyan Naik Vankudothu; Wisam Bukaita

doi:doi:10.11648/j.ajtte.20251006.12

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Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision

Kalyan Naik Vankudothu^*

, Wisam Bukaita

Published in American Journal of Traffic and Transportation Engineering (Volume 10, Issue 6)

Received: 19 November 2025 Accepted: 1 December 2025 Published: 31 December 2025

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Abstract

The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.

Published in	American Journal of Traffic and Transportation Engineering (Volume 10, Issue 6)
DOI	10.11648/j.ajtte.20251006.12
Page(s)	150-167
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Structural Health Monitoring, Convolutional Neural Network, Crack Detection, Crack Quantification, Image Processing Techniques, Severity Grading Framework, Predictive Maintenance

References

[1]	Bukaita, W., Vankudothu, K. N., Khan, J. (2025). Automated Multi-Class Concrete Crack Detection and Severity Classification Using CNN-Based Deep Learning. American Journal of Civil Engineering, 13(4), 197-210. https://doi.org/10.11648/j.ajce.20251304.12
[2]	Park, Song Ee, Seung-Hyun Eem, and Haemin Jeon. 2020. “Concrete crack detection and quantification using deep learning and structured light.” Construction and Building Materials 252. https://doi.org/10.1016/j.conbuildmat.2020.119096
[3]	Ren, Yupeng, Jisheng Huang, Zhiyou Hong, Wei Lu, Jun Yin, Leiun Zou, and Xiaohua Shen. 2020. “Image-based concrete crack detection in tunnels using deep fully convolutional networks.” Construction and Building Materials 234. https://doi.org/10.1016/j.conbuildmat.2019.117367
[4]	Li, Shengyuan, Xuefeng Zhao, and Hayri Baytan Ozmen. 2019. “Image-based concrete crack detection using convolutional neural network and exhaustive search technique.” Advances in Civil Engineering 2019. https://doi.org/10.1155/2019/6520620
[5]	Qingyi, Wang, and Chen Bo. 2024. “A novel transfer learning model for the real-time concrete crack detection.” Knowledge-Based Systems 301. https://doi.org/10.1016/j.knosys.2024.112313
[6]	Zhang, Xinxiang, Dinesh Rajan, and Brett Story. 2019. “Concrete crack detection using context-aware deep semantic segmentation network.” Computer-Aided Civil and Infrastructure Engineering 34(11): 951–71. https://doi.org/10.1111/mice.12477
[7]	Hang, Jiaqi, Yingjie Wu, Yancheng Li, Tao Lai, Jie Zhang, and Yang Li. 2023. “A deep learning semantic segmentation network with attention mechanism for concrete crack detection.” Structural Health Monitoring. https://doi.org/10.1177/147592172311216710
[8]	Kim, Bubryur, N. Yuvaraj, K. R. Sri Preethaa, and R. Arun Pandian. 2021. “Surface crack detection using deep learning-based shallow CNN architecture for enhanced computation.” Neural Computing and Applications 33(15): 9289–9305. https://doi.org/10.1007/s00521-021-05950-4
[9]	Arfan, Palisa, AHM Muntasir Billah, and Tahsin Reza. 2024. “Deep learning-based concrete defects classification and detection using semantic segmentation.” Structural Health Monitoring 23(2): 383–409. https://doi.org/10.1177/14759217231158114
[10]	Golding, Vaughn Peter, Zahra Gharineiat, Suliman Munawar Hafiz, and Fahim Ullah. 2024. “Crack classification and quantification using deep learning.” Sustainability 14(4): 8147. https://doi.org/10.3390/su14138117
[11]	Wan, Chunfeng, Xiaobin Xiong, Bo Wen, Shuai Gao, Da Fang, Caigian Yang, and Songtao Xue. 2022. “Crack detection for concrete bridges with image-based deep learning.” Science Progress 105(4). https://doi.org/10.1177/00368504221128487
[12]	Yu, Shanshan, Jian Zhang, Chengpeng Zhu, Zeyang Sun, and Shuai Dong. 2024. “Full-field deformation measurement and cracks detection in speckle scene using the deep learning-aided digital image correlation method.” Mechanical Systems and Signal Processing 209. https://doi.org/10.1016/j.ymssp.2024.111131
[13]	Lin, Wang. 2023. “Automatic detection of concrete cracks from images using Adam-squeezenet deep learning model.” Fracture and Structural Integrity 17(65): 289–99. https://doi.org/10.3221/IGF-ESIS.65.19
[14]	Kolappa, Geetha Ganesh, and Sung-Han Sim. 2022. “Fast identification of concrete cracks using 1D deep learning and explainable artificial intelligence-based analysis.” Automation in Construction 143. https://doi.org/10.1016/j.autcon.2022.104572
[15]	Joshi, Deepa, Dinesh P. Singh, and Gargeya Sharma. 2022. “Automatic surface crack detection using segmentation-based deep-learning approach.” Engineering Fracture Mechanics 268. https://doi.org/10.1016/j.engfracmech.2022.108467
[16]	American Concrete Institute. 2001. ACI 224R-01: Control of Cracking in Concrete Structures. Farmington Hills, MI: American Concrete Institute.
[17]	American Concrete Institute. 2019. ACI 318-19: Building Code Requirements for Structural Concrete and Commentary. Farmington Hills, MI: American Concrete Institute.
[18]	Patel, Hetkumar, and Wisam Bukaita. 2025. “Deep Learning-Based Prediction of Lifespan Degradation in Concrete Bridges Due to Iron Oxidation.” American Journal of Traffic and Transportation Engineering 10(5). https://doi.org/10.11648/j.ajtte.20251005.11
[19]	Bowling, Carson, Luke Pierini, and Wisam Bukaita. 2025. “Deep Learning-Based Severity Classification of Concrete Cracks Using YOLOv8 for Structural Health Analysis.” Global Journal of Researches in Engineering, September. https://doi.org/10.34257/LJERVOL25IS3PG13

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APA Style

Vankudothu, K. N., Bukaita, W. (2025). Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. American Journal of Traffic and Transportation Engineering, 10(6), 150-167. https://doi.org/10.11648/j.ajtte.20251006.12

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ACS Style

Vankudothu, K. N.; Bukaita, W. Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. Am. J. Traffic Transp. Eng. 2025, 10(6), 150-167. doi: 10.11648/j.ajtte.20251006.12

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AMA Style

Vankudothu KN, Bukaita W. Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. Am J Traffic Transp Eng. 2025;10(6):150-167. doi: 10.11648/j.ajtte.20251006.12

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@article{10.11648/j.ajtte.20251006.12,
  author = {Kalyan Naik Vankudothu and Wisam Bukaita},
  title = {Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {10},
  number = {6},
  pages = {150-167},
  doi = {10.11648/j.ajtte.20251006.12},
  url = {https://doi.org/10.11648/j.ajtte.20251006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20251006.12},
  abstract = {The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.},
 year = {2025}
}

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TY  - JOUR
T1  - Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision
AU  - Kalyan Naik Vankudothu
AU  - Wisam Bukaita
Y1  - 2025/12/31
PY  - 2025
N1  - https://doi.org/10.11648/j.ajtte.20251006.12
DO  - 10.11648/j.ajtte.20251006.12
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 150
EP  - 167
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20251006.12
AB  - The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.
VL  - 10
IS  - 6
ER  -

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Author Information

Kalyan Naik Vankudothu

Math and Computer Science Department, Lawrence Technological University, Southfield, USA

Contact Email

http://orcid.org/0009-0001-2615-6455
Wisam Bukaita

Math and Computer Science Department, Lawrence Technological University, Southfield, USA

Contact Email

http://orcid.org/0000-0001-6255-3848

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APA Style

Vankudothu, K. N., Bukaita, W. (2025). Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. American Journal of Traffic and Transportation Engineering, 10(6), 150-167. https://doi.org/10.11648/j.ajtte.20251006.12

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ACS Style

Vankudothu, K. N.; Bukaita, W. Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. Am. J. Traffic Transp. Eng. 2025, 10(6), 150-167. doi: 10.11648/j.ajtte.20251006.12

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AMA Style

Vankudothu KN, Bukaita W. Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision. Am J Traffic Transp Eng. 2025;10(6):150-167. doi: 10.11648/j.ajtte.20251006.12

Copy | Download

@article{10.11648/j.ajtte.20251006.12,
  author = {Kalyan Naik Vankudothu and Wisam Bukaita},
  title = {Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {10},
  number = {6},
  pages = {150-167},
  doi = {10.11648/j.ajtte.20251006.12},
  url = {https://doi.org/10.11648/j.ajtte.20251006.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20251006.12},
  abstract = {The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Quantitative Analysis of Crack Growth and Severity in Reinforced Concrete Structures Using Deep Learning and Computer Vision
AU  - Kalyan Naik Vankudothu
AU  - Wisam Bukaita
Y1  - 2025/12/31
PY  - 2025
N1  - https://doi.org/10.11648/j.ajtte.20251006.12
DO  - 10.11648/j.ajtte.20251006.12
T2  - American Journal of Traffic and Transportation Engineering
JF  - American Journal of Traffic and Transportation Engineering
JO  - American Journal of Traffic and Transportation Engineering
SP  - 150
EP  - 167
PB  - Science Publishing Group
SN  - 2578-8604
UR  - https://doi.org/10.11648/j.ajtte.20251006.12
AB  - The accurate and objective assessment of reinforced concrete structures is paramount for maintaining structural integrity and optimizing long-term maintenance planning. This study introduces a unified deep learning and computer vision framework designed for the automated detection, classification, and standards-aligned quantitative analysis of concrete cracks. The methodology begins with the automated categorization of an approximately 7,000-image concrete surface dataset into seven specific defect types including Thermal, Serviceability, and Strength Failure Cracks based on geometric metrics like crack length and width. This automated pre-classification step successfully mitigates the subjectivity and inconsistency associated with traditional manual labeling, providing a robust foundation for model training. A Convolutional Neural Network (CNN), implemented using Python, TensorFlow, and Keras, was trained over 50 epochs to detect and classify these categorized defects. The model achieved a final classification accuracy of 91.1%, demonstrating strong generalization and outperforming models trained on unrefined datasets. Following detection, a quantitative damage measurement module utilizes Otsu thresholding, morphological filtering, and skeletonization to precisely extract geometric parameters. Automated functions estimated key crack metrics, including length (5–180mm) and width (0.2–4.5mm), and surface deterioration percentage. These measurements are used to assign a severity grade (minor, moderate, or severe), aligned with established ACI 224R-01 and ACI 318-19 guidelines. Visualization techniques, such as severity-based color coding and multi-panel views, enhance the interpretability and validate both the detection accuracy and measurement reliability. By integrating automated data refinement, CNN-based recognition, and objective standards-aligned quantitative assessment, this framework provides a scalable and reliable tool for real-time structural health monitoring.
VL  - 10
IS  - 6
ER  -

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