Summary of Architectural Flaw Detection in Civil Engineering Using Gpt-4, by Saket Kumar et al.
Architectural Flaw Detection in Civil Engineering Using GPT-4
by Saket Kumar, Abul Ehtesham, Aditi Singh, Tala Talaei Khoei
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential of GPT4 Turbo vision model in detecting architectural flaws during the design phase in civil engineering. The study evaluates the model’s performance using precision, recall, and F1 score metrics, demonstrating its effectiveness in accurately identifying missing doors and windows compared to human-verified data. Additionally, the research investigates AI’s broader capabilities, including identifying load-bearing issues, material weaknesses, and ensuring compliance with building codes. The findings highlight how AI can improve design accuracy, reduce costly revisions, and support sustainable practices, revolutionizing the civil engineering field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence (AI) in architecture to make sure buildings are safe and well-designed. AI helps detect mistakes during the design phase, like missing doors or windows. The study shows that AI is really good at finding these errors and can even identify other problems like structural issues or material weaknesses. This technology can help make building designs more accurate, reduce costly changes later on, and support sustainable practices. |
Keywords
» Artificial intelligence » F1 score » Precision » Recall
