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Daily Arxiv

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Automated Interpretation of Non-Destructive Evaluation Contour Maps Using Large Language Models for Bridge Condition Assessment

Created by
  • Haebom

Author

Viraj Nishesh Darji, Callie C. Liao, Duoduo Liao

Outline

This study presents an innovative approach that leverages large-scale language models (LLMs) to enhance the efficiency of nondestructive testing (NDE) data interpretation for bridge maintenance and safety. We demonstrate the effectiveness of LLMs in interpreting NDE contour maps using various LLMs and providing detailed analysis of bridge conditions. Specifically, nine LLMs were used to interpret five NDE contour maps and evaluated in terms of image description generation, defect identification, actionable recommendations, and accuracy. The results show that four LLMs generate detailed and effective image descriptions, while ChatGPT-4 and Claude 3.5 Sonnet are more effective in generating comprehensive overviews. This study suggests that LLM-based analysis can enhance the efficiency of bridge inspection workflows while maintaining accuracy.

Takeaways, Limitations

Takeaways:
We demonstrate that leveraging LLM can significantly improve the efficiency of NDE data interpretation.
We suggest that LLM-based analytics can accelerate bridge maintenance decision-making.
Identify the potential for improving bridge safety assessment and infrastructure management using LLM.
We present criteria for selecting the optimal model by comparing the performance of various LLMs.
Limitations:
This study is a small-scale (five NDE contour maps) pilot study and further research using larger datasets is needed.
Generalizability to different types of NDE data and bridge structures needs to be verified.
Additional validation procedures are needed to further increase the reliability and accuracy of the interpretation results of LLM.
Consideration should be given to the costs and technical challenges associated with utilizing an LLM.
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