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A digital twin approach based method in civil engineering for classification of salt damage in building evaluation

Author

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  • Guzmán-Torres, J.A.
  • Domínguez-Mota, F.J.
  • Alonso Guzmán, E.M.
  • Tinoco-Guerrero, G.
  • Tinoco-Ruíz, J.G.

Abstract

The integration of digital twins and machine learning models in civil engineering has revolutionized the inspection and maintenance of buildings and structures. Digital twins, as precise virtual replicas of physical assets, enable continuous monitoring and predictive maintenance, enhancing the reliability and efficiency of structural assessments.

Suggested Citation

  • Guzmán-Torres, J.A. & Domínguez-Mota, F.J. & Alonso Guzmán, E.M. & Tinoco-Guerrero, G. & Tinoco-Ruíz, J.G., 2025. "A digital twin approach based method in civil engineering for classification of salt damage in building evaluation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 233(C), pages 433-447.
    • Handle: RePEc:eee:matcom:v:233:y:2025:i:c:p:433-447
    DOI: 10.1016/j.matcom.2025.02.003
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    References listed on IDEAS

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    1. Hossein Omrany & Karam M. Al-Obaidi & Amreen Husain & Amirhosein Ghaffarianhoseini, 2023. "Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    2. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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