Song, Honghong, Zhu, Xiaofeng, Li, Haijiang ![]() |
Abstract
As bridges age, manual repair decision-making methods struggle to meet growing maintenance demands. This paper develops AI systems that can imitate experts’decision processes by mining implicit relationships between bridge damage images and corresponding repair proposals. A multimodal deep learning-based end-to-end decision-making method is proposed to extract and map features of bridge damage images and repair proposal texts, automating damage repair proposal generation. The model is trained and validated using a dataset from historical inspection reports. The model’s image feature extraction is evaluated using Class Activation Mapping (CAM), while text generation achieved BLEU-1 to BLEU-4 scores of 0.76, 0.743, 0.712, and 0.705, respectively, with 82 % accuracy in human evaluation. The results indicate the model’s effectiveness in handling complex image features and generating long text, addressing challenges in automated bridge repair decision-making.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TG Bridge engineering |
Publisher: | Elsevier |
ISSN: | 0926-5805 |
Date of Acceptance: | 7 January 2025 |
Last Modified: | 24 Jan 2025 12:15 |
URI: | https://orca.cardiff.ac.uk/id/eprint/175402 |
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