用GPT-4检测建筑图纸缺门缺窗，提升设计安全与效率
Architectural Flaw Detection in Civil Engineering Using GPT-4
- 基于GPT-4 Turbo视觉模型识别图纸中的缺失门窗
- 在人类验证数据上实现高精度、高召回的缺陷检测
- 适合建筑设计师和工程审核人员快速排查设计隐患
人工智能在土木工程中的应用正推动设计质量与安全性的变革。本文研究了先进大模型GPT-4 Turbo视觉能力在设计阶段检测建筑缺陷的潜力，重点识别缺失的门和窗。通过精确率、召回率和F1分数等指标评估模型性能，结果表明其在对比人工验证数据时具有高准确性。研究还拓展至识别承重问题、材料薄弱点及建筑规范合规性。结果表明，AI能显著提升设计精度，减少昂贵返工，支持可持续实践，最终推动土木工程向更安全、高效、美学优化的方向发展。
原文摘要 · Abstract (English)
The application of artificial intelligence (AI) in civil engineering presents a transformative approach to enhancing design quality and safety. This paper investigates the potential of the advanced LLM GPT4 Turbo vision model in detecting architectural flaws during the design phase, with a specific focus on identifying missing doors and windows. The study evaluates the model's performance through metrics such as precision, recall, and F1 score, demonstrating AI's effectiveness in accurately detecting flaws compared to human-verified data. Additionally, the research explores AI's broader capabilities, including identifying load-bearing issues, material weaknesses, and ensuring compliance with building codes. The findings highlight how AI can significantly improve design accuracy, reduce costly revisions, and support sustainable practices, ultimately revolutionizing the civil engineering field by ensuring safer, more efficient, and aesthetically optimized structures.
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