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Updated: May 26, 2025

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An R-Based Landscape Validation of a Competing Risk Model
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建筑工地安全管理的多标签材料和人类风险因素识别模型
Jeongeun Park1, Sojeong Seong2, Soyeon Park3
1Department of Human Computer Interaction, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan, 15588, South Korea.
Journal of safety research
|February 25, 2025
概括
本研究引入了一个深度学习框架,用于自动识别多个建筑工地安全风险. 该模型准确地识别了物质和人为因素,改善了预防性安全管理.
科学领域:
- 建筑工程安全 建筑工程安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 建筑工地存在许多安全风险,管理人员很难手动识别这些风险.
- 实践安全管理需要自动识别多个并发的物质和人类风险因素.
研究的目的:
- 开发和优化一个深度学习模型,用于建筑工地自动多标签风险因素识别 (MRFR).
- 了解和改进预防性安全管理的MRFR模型的决策过程.
主要方法:
- 从建筑工地收集了14605例八种类型的物质和人为风险因素的数据集.
- 开发了一个最佳的多标识识别模型来处理并发的风险因素.
主要成果:
- 该MRFR框架实现了高性能,F1得分为0.9981和Hamming损失为0.0008.
- 使用模型可视化对错误预测的分析确定了视觉模两可和对象接近的挑战.
结论:
- 该模型需要足够的能力来有效地检测多种风险因素.
- 该MRFR框架提供了一个可行的算法,用于在现实建筑环境中预先和自动识别风险因素.
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