Yürekli Yusuf, Özarpa Cevat, Avcı İsa
TCDD Railway Maintenance Directorate, Karabük University, Karabük 78100, Türkiye.
Department of Biomedical Engineering, Ankara Medipol University, Ankara 06050, Türkiye.
Sensors (Basel). 2025 Jun 26;25(13):3992. doi: 10.3390/s25133992.
Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük-Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy.
铁路上的落石在山体滑坡主题下被视为一种自然灾害。这是一种因地形和气候而在不同地区有所差异的事件。除了交通密度外，卡拉比克－耶尼塞铁路线还穿过山区、河流交汇处，并且季节性降雨量大。这些情况使得有必要采取积极措施来降低诸如落石、树木倒塌、山体滑坡以及其他威胁铁路线的地质灾害等风险。未被察觉的环境事件对铁路运营安全构成重大威胁。该研究旨在利用通过光纤电缆发出的振动来实现对环境现象的早期检测。本研究提出了一种将分布式声学传感（DAS）与混合集成学习模型相结合的实时灾害检测系统。利用光纤电缆和Luna OBR - 4600询问器，该系统在土耳其卡拉比克一条6公里长的铁路走廊沿线捕捉环境振动。使用CatBoosting、支持向量机（SVM）、LightGBM、决策树、XGBoost、随机森林（RF）和梯度提升分类器（GBC）算法来检测传入信号。然而，投票分类器混合模型是使用SVM、RF、XGBoost和GBC算法开发的。铁路线上的信号系统通过检测环境因素为安全提供关键信息。诸如落石、树木倒下和山体滑坡等重大自然灾害由于环境因素会引起高强度振动，而这些振动可以通过光纤电缆检测到。在本研究中，采用投票分类器方法开发了一种混合模型，以准确检测和分类振动。该模型利用一组分类算法来准确分类各种环境干扰。该系统通过在实际条件下成功检测到落石、山体滑坡和树木倒下等环境事件，在精确率、召回率、F1分数和准确率方面达到了98%的成功率，证明了其有效性。
