Digital twin and multimodal neural networks for automated coastal railway bridge maintenance

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Khudhair, Ali and Zhu, Xiaofeng and Li, Haijiang and Ahmadian, Reza and Adeagbo, Mujib and Liu, Jiucai; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Digital twin and multimodal neural networks for automated coastal railway bridge maintenance. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 173-182. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093263)

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Abstract

Coastal railway bridges are exposed to accelerated deterioration due to harsh marine environments, making their inspection and maintenance both costly and complex. This paper proposes a semi-automated framework that integrates Digital Twin (DT) technology with a Multimodal Neural Network (MNN) to generate natural language repair strategies directly from visual inspection data. The system combines an EfficientNet-based convolutional encoder with a Transformer decoder, trained on a domain-specific dataset of corroded bridge components annotated by experts. Unlike conventional damage detection pipelines, the proposed model outputs actionable, human-readable maintenance recommendations that are programmatically embedded into Industry Foundation Classes (IFC)-based BIM models as structured property sets. This enables seamless integration into Building Information Modelling (BIM)-based DT environments, supporting downstream decision-making and lifecycle asset management. Experimental results show that the model achieves a semantic similarity score of 0.7285 and a BLEU-3 score of 0.4193, indicating strong alignment with expert-authored strategies. While exact match accuracy is limited to 24.18%, this reflects the inherent linguistic variability in valid maintenance descriptions. The system also incorporates expert feedback to support human-in-the-loop learning and continuous improvement. These findings demonstrate the feasibility of combining DL and openBIM standards to enable scalable, automated, and semantically enriched maintenance planning for coastal railway infrastructure.

  • Item type:Book Section
    ID code:93263
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 10:48
    Last modified:12 Apr 2026 00:46
    URI:https://strathprints.strath.ac.uk/id/eprint/93263
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