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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Abstract

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions.

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Data is available from the authors upon reasonable request.

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Funding

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Author information

Authors and Affiliations

  1. Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan

    Sayel M. Abualigah & Ahmad F. Al-Naimi

  2. Gagan Sachdeva, Dubai, UAE

    Gagan Sachdeva & Omran AlAmri

  3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  4. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia

    Laith Abualigah

  6. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  8. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

    Laith Abualigah

  9. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia

    Laith Abualigah

  10. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  11. College of Engineering, Yuan Ze University, Taoyuan, 32003, Taiwan

    Laith Abualigah

Authors
  1. Sayel M. Abualigah
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  2. Ahmad F. Al-Naimi
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  3. Gagan Sachdeva
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  4. Omran AlAmri
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  5. Laith Abualigah
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Corresponding author

Correspondence to Laith Abualigah.

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Abualigah, S.M., Al-Naimi, A.F., Sachdeva, G. et al. IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18998-z

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  • DOI: https://doi.org/10.1007/s11042-024-18998-z

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