Cao Yuhao, Yun Bai
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China.
College of International Studies, National University of Defense Technology, Nanjing 210039, China.
Heliyon. 2024 Aug 21;10(16):e36450. doi: 10.1016/j.heliyon.2024.e36450. eCollection 2024 Aug 30.
Tunnels represent complex, high-risk, and technically demanding underground construction projects. The safety of construction workers in tunnels is influenced by various factors, including physiological indicators, tunnel dimensions, and internal environmental conditions. Analyzing safety based solely on static factors is inadequate for modern tunnel engineering safety management requirements. To address this challenge, this paper provides a comprehensive analysis of factors impacting safety and employs the Analytic Hierarchy Process (AHP) to identify seven significant factors with high importance: body temperature, heart rate, internal temperature, internal humidity, CO concentration, chlorine concentration, and the relative positioning of personnel. Considering these factors essential for assessing worker safety, we introduce a novel model named Tunnel-APH-AD. For training models aimed at anomaly detection, we performed data augmentation and utilized four distinct machine learning models. Additionally, ensemble learning techniques were applied to aggregate the predictions from individual models, thereby enhancing the effectiveness of detecting safety states for tunnel workers. We also evaluated the performance of these models on out-of-distribution (OOD) samples to test their robustness and generalizability. The experimental results indicate that, under similar ventilation and tunnel conditions, the ensemble learning model exhibits superior overall performance compared to individual models, underscoring the effectiveness of model combination in improving the accuracy and reliability of safety alerts. Through experimental validation, this study provides interpretable, scalable, and scientifically generalized applications of machine learning theories in systems for tunnel construction worker safety alerts. These findings contribute to advancing safety management practices in tunnel engineering, enabling proactive and effective measures to mitigate potential risks and ensure the well-being of workers.
隧道是复杂、高风险且技术要求苛刻的地下建设项目。隧道施工人员的安全受到多种因素影响，包括生理指标、隧道尺寸和内部环境条件。仅基于静态因素分析安全性不足以满足现代隧道工程安全管理要求。为应对这一挑战，本文全面分析了影响安全的因素，并采用层次分析法（AHP）确定了七个具有高度重要性的显著因素：体温、心率、内部温度、内部湿度、一氧化碳浓度、氯气浓度以及人员的相对位置。考虑到这些对评估工人安全至关重要的因素，我们引入了一种名为Tunnel-APH-AD的新型模型。对于旨在进行异常检测的训练模型，我们进行了数据增强，并使用了四种不同的机器学习模型。此外，应用集成学习技术汇总各个模型的预测结果，从而提高检测隧道工人安全状态的有效性。我们还在分布外（OOD）样本上评估了这些模型的性能，以测试它们的稳健性和泛化能力。实验结果表明，在相似的通风和隧道条件下，集成学习模型相比单个模型具有更优的整体性能，凸显了模型组合在提高安全警报准确性和可靠性方面的有效性。通过实验验证，本研究为机器学习理论在隧道施工工人安全警报系统中提供了可解释、可扩展且科学通用的应用。这些发现有助于推进隧道工程中的安全管理实践，能够采取积极有效的措施来减轻潜在风险并确保工人的福祉。
