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<p>The incorporation of deep learning (DL) methodologies such as Neural Networks, Convolutional Neural Networks (CNNs), and CNNs-based hybrid AI systems, has tremendously shifted the paradigm in the field of structural retrofitting. This review analyses the architectural frameworks, practical implementations, and the structural safety measures undertaken using DL models aimed at improving the performance and cost efficiency in retrofitting techniques. Additional focus areas include damage identification, performance assessment of treated structures, and retrofitting design optimisation. The review critically assesses the data sufficiency, model training steps, and validation processes within the scope of civil engineering to deploy DL driven models. Clearly, further work is warranted with respect to sparsity of data, the ‘black box’ nature of the models, high computational costs, and absence of uniform benchmark criteria. Interdisciplinary approaches—combining civil engineering, data science, and legal policy—are essential to mitigate these challenges and fully exploit AI-enhanced capabilities for retrofitting. This paper will serve as a single point of reference for anyone intending to research or practically implement intelligent, adaptable, and safety-oriented retrofitting strategies.</p>

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Neural networks, CNNs, and hybrid models in structural retrofitting: a deep learning perspective

  • 期刊论文

  • 发表日期：

    2025年7月21日

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  • Pradeep K. S. Bhadauria,
  • Nilesh Zanjad,
  • Sanket Gajanan Kalamkar,
  • Amitkumar Ranit,
  • Pravin Chaudhary

摘要

The incorporation of deep learning (DL) methodologies such as Neural Networks, Convolutional Neural Networks (CNNs), and CNNs-based hybrid AI systems, has tremendously shifted the paradigm in the field of structural retrofitting. This review analyses the architectural frameworks, practical implementations, and the structural safety measures undertaken using DL models aimed at improving the performance and cost efficiency in retrofitting techniques. Additional focus areas include damage identification, performance assessment of treated structures, and retrofitting design optimisation. The review critically assesses the data sufficiency, model training steps, and validation processes within the scope of civil engineering to deploy DL driven models. Clearly, further work is warranted with respect to sparsity of data, the ‘black box’ nature of the models, high computational costs, and absence of uniform benchmark criteria. Interdisciplinary approaches—combining civil engineering, data science, and legal policy—are essential to mitigate these challenges and fully exploit AI-enhanced capabilities for retrofitting. This paper will serve as a single point of reference for anyone intending to research or practically implement intelligent, adaptable, and safety-oriented retrofitting strategies.