# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 1 Introduction  
In recent years, the issue of early warnings of staff safety status as well as alarms during tunnel construction has become a research hotspot, and is one of the important aspects to ensure the safe development of a project. Taking into account the special characteristics of tunneling projects, some studies have established a big data platform for tunnel boring machine engineering management integrating intelligent monitoring, comprehensive analysis, collaborative management and big data application, which provides a reference for the design and development of big data platforms in the field of tunnel construction from the management and technical perspectives, and improves the efficiency and intelligent management of tunnel boring machine construction [1]. However, this big data platform still requires professional operation and maintenance personnel to rely on their work experience to carry out human-machine interaction, which is not conducive to automated information control. In order to promote the intelligent development of information management, [PERSON] coupled a genetic algorithm and a limit-based learning machine, and used the genetic algorithm to optimize the initial weights and thresholds of the limit-based learning machine, thus greatly improving the accuracy of prediction [2]. This combination could better predict the results under different working conditions, which is the goal of the combination of intelligent algorithms in engineering safety assurance. Along with the development of machine learning and deep learning theory research, there are a wide range of applications in various areas of engineering, and there are many algorithmic techniques in machine learning methods that can be used for safety detection, among which the single-classification support vector machine has a strong classificationability and simple model principle. [PERSON] et al. applied a single-classification support vector machine to fault detection of aero-engines to identify fault data accurately and quickly and find abnormal engine operation status in time [3]. [PERSON] used a two-threshold OCSVM algorithm with a two-level decision boundary for wake detection [4], and the experimental results showed that the improved algorithm improved the detection accuracy at different signal-to-noise ratios. [PERSON] proposed an equipment fault-detection method based on a single-classification support vector machine (OCSVM) [5], and the accuracy of the established prediction model for gas outflow from coal mining workings was higher than the prediction accuracy when applying a BP neural network. [PERSON]'s distribution network communication network traffic anomaly detection method using a single-classification support vector machine (OCSVM) [6] greatly improved the efficiency of anomalous-state detection. In the field of deep learning, in order to effectively solve the traditional construction site safety production management problem in China and reduce the problem of major casualties caused by personal negligence of construction workers, such as not wearing helmets, [PERSON] and other researchers proposed a deep-learning-based helmet-wearing detection and tracking technology [7]. The combination of other deep learning technologies in the field of engineering and construction, using deep learning algorithms to automatically train a set of target-detection models and thus improve the efficiency, is a developing trend [8]. In recent years, there have been numerous studies on safety management and risk assessment. Among these, [PERSON] et al. [9] proposed a new risk-calculation method called risk gridding (RM) for three-dimensional risk assessment based on field theory, which laid the theoretical foundation for further development of three-dimensional risk assessment based on field theory. Safety management and risk assessment are a type of system engineering, which is extensive, difficult to manage, and comprehensive [10], and it is difficult to evaluate it scientifically. [PERSON] et al.