# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 1 Introduction
[11] considered that laboratory safety management evaluation is essentially a multi-indicator decision problem, and [PERSON] et al. [12] constructed an evaluation method that combines the Delphi method and hierarchical analysis. [PERSON] et al. [13] proposed an evaluation index system for university laboratory fire safety based on \"human, machine, environment and management\", used queuing theory to assign weights to evaluation indexes and established a comprehensive evaluation model of physical elements. [PERSON] [14] used hierarchical analysis and a fuzzy weighted average operator to make a fuzzy comprehensive evaluation of laboratory safety management. A series of scientific achievements have been made in research on construction site personnel localization, personnel attributes and component identification. Among them, the SVM algorithm has good feasibility and generalizability when applied to tunnel construction site safety management. In the field of tunnelling, the monitoring of personnel safeguards based on machine learning methods is widely used, and current research has focused on the wearing of personal protection equipment, simple personnel behavior, component breakage and component deformation degree recognition. These applications can solve specific safety management problems, such as helmet wearing and component crack detection. However, as a complex systemic project, tunnel construction safety management requires multiple considerations to meet various safety management needs. Based on this, the current research is somewhat one-sided and should be conducted systematically and thoroughly for the characteristics of construction safety management. For personnel safety, it is more direct and accurate to combine the characteristics of the internal environment of the tunnel to warn about the abnormal state of personnel signs. Since SVM methods have been maturely applied in research on machinery and aviation engineering, tunnel personnel safety detection belongs to a typical anomaly detection problem with a data imbalance, where anomalous data fluctuate in the case of training models with normal data. The OCSVM model can effectively solve the data imbalance problem and accurately predict anomalous states. The contributions of this paper are as follows:* This paper establishes a set of tunnel construction personnel safety status detection and early warning models based on the OCSVM algorithm. * This paper provides a dataset in the field of tunnel safety personnel status detection, which can be used for relevant research in this field. * This paper reports on ablation comparison experiments conducted to corroborate the accuracy of the model on datasets with different distribution features and to consider the influence of the abnormal data proportion on the accuracy of the model. The accuracy of the model under four feature cases was also compared horizontally, and the performance of the model was evaluated comprehensively. The identification of tunnel construction personnel's safety status requires fast and efficient detection of personnel vital signs, and timely feedback on the health status and location of abnormal personnel, so that when an accident occurs, we can efficiently, quickly and accurately obtain the internal environment of the tunnel, the status of personnel and their location, which are beneficial to project managers and rescue workers to select a rescue plan to protect personnel's lives. Therefore, this article takes the analysis of the factors affecting the signs of tunnel construction personnel and environmental factors as the entry point, and combines the OCSVM safety warning model for intelligent safety management to realize the information control of intelligent site safety.