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开发基于变压器的自动化文本分析框架,用于监测建筑物中的防火门缺陷
1Institute for Environmental Design and Engineering, University College London, London, WC1H 0NN, UK. seung-hyun.wang@ucl.ac.uk.
Scientific reports
|December 16, 2025
概括
使用变压器模型的自动防火门缺陷检测显著提高了建筑安全管理. 优化的RoBERTa模型平均F1得分为85.44%,改善了住宅建筑的消防安全.
科学领域:
- 建设管理建设管理.
- 人工智能的人工智能
- 建筑安全工程 建筑安全工程
背景情况:
- 住宅建筑中的防火门缺陷对居住者安全和建筑管理构成重大风险.
- 现有的缺陷检测方法往往是手动的,耗时的.
- 确保消防门的完整性对于建筑物中有效的消防安全至关重要.
研究的目的:
- 评估基于变压器的文本分类模型在自动防火门缺陷检测方面的有效性.
- 为了比较各种变压器模型的性能,包括BERT,RoBERTa,ALBERT,DistilBERT和XLNet.
- 确定最优的模型,以改善建筑中的消防门安全管理.
主要方法:
- 提出并优化了五种基于变压器的文本分类模型 (BERT,RoBERTa,ALBERT,DistilBERT,XLNet).
- 使用常见参数和方法特定参数进行了超参数优化,最终产生了1458个模型变体.
- 用多个指标评估模型性能,包括针对特定缺陷类型的F1分数.
主要成果:
- 优化的RoBERTa模型表现出卓越的性能,平均F1得分为85.44%.
- 对于特定的缺陷,F1得分很高:92.13% (框架间隙),96.66% (密封部件) 和98.43% (机械部件).
- 罗伯塔显著优于其他优化模型,包括传统的机器学习和深度学习方法.
结论:
- 基于变压器的方法,特别是RoBERTa,对于自动检测消防门缺陷非常有效.
- 这种方法为增强现实建筑场景中的安全管理提供了有前途的解决方案.
- 自动缺陷检测可以改善住宅建筑的消防安全和降低风险.
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