IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v233y2025icp433-447.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475425000382
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2025.02.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. José M. Navarro-Jiménez & José V. Aguado & Grégoire Bazin & Vicente Albero & Domenico Borzacchiello, 2023. "Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2345-2358, June.
    2. Houljakbe Houlteurbe Dagou & Asli Pelin Gurgun & Kerim Koc & Cenk Budayan, 2025. "The Future of Construction: Integrating Innovative Technologies for Smarter Project Management," Sustainability, MDPI, vol. 17(10), pages 1-29, May.
    3. Jiaqi Zhao & Xiaolong Qian & Yunzhou Zhang & Dexing Shan & Xiaozheng Liu & Sonya Coleman & Dermot Kerr, 2024. "A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 841-857, February.
    4. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    5. Dexing Shan & Yunzhou Zhang & Shitong Liu, 2025. "Multi-modal background-aware for defect semantic segmentation with limited data," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3313-3325, June.
    6. Ahmed Mohammed Abdelalim & Ahmed Essawy & Alaa Sherif & Mohamed Salem & Manal Al-Adwani & Mohammad Sadeq Abdullah, 2025. "Optimizing Facilities Management Through Artificial Intelligence and Digital Twin Technology in Mega-Facilities," Sustainability, MDPI, vol. 17(5), pages 1-30, February.
    7. Nhat-To Huynh, 2024. "A multi-subpopulation genetic algorithm-based CNN approach for ceramic tile defects classification," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1781-1792, April.
    8. David Ornelas & Daniel Canedo & António J. R. Neves, 2025. "Semantic Segmentation of Corrosion in Cargo Containers Using Deep Learning," Sustainability, MDPI, vol. 17(14), pages 1-28, July.
    9. Abtin Djavadifar & John Brandon Graham-Knight & Marian Kӧrber & Patricia Lasserre & Homayoun Najjaran, 2022. "Automated visual detection of geometrical defects in composite manufacturing processes using deep convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2257-2275, December.
    10. Li Wei & Mahmud Iwan Solihin & Sarah ‘Atifah Saruchi & Winda Astuti & Lim Wei Hong & Ang Chun Kit, 2024. "Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review," SN Operations Research Forum, Springer, vol. 5(3), pages 1-71, September.
    11. Zichen Bai & Junfeng Jing, 2024. "Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3315-3330, October.
    12. Shuanlong Niu & Yaru Peng & Bin Li & Yuanhong Qiu & Tongzhi Niu & Weifeng Li, 2024. "A novel deep learning motivated data augmentation system based on defect segmentation requirements," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 687-701, February.
    13. Jae-Eun Park & Young-Keun Kim, 2025. "Semi-supervised learning for steel surface inspection using magnetic flux leakage signal," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1021-1031, February.
    14. Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.
    15. Yuanyuan Wang & Ling Ma & Lihua Jian & Huiqin Jiang, 2023. "Conductive particle detection via efficient encoder–decoder network," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3563-3577, December.
    16. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    17. Vladimir Badenko & Nikolai Bolshakov & Alberto Celani & Valentina Puglisi, 2024. "Principles for Sustainable Integration of BIM and Digital Twin Technologies in Industrial Infrastructure," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
    18. Razeen Hashmi & Huai Liu & Ali Yavari, 2024. "Digital Twins for Enhancing Efficiency and Assuring Safety in Renewable Energy Systems: A Systematic Literature Review," Energies, MDPI, vol. 17(11), pages 1-34, May.
    19. Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
    20. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:233:y:2025:i:c:p:433-447. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.