| Summary: | The judgment of tunnel personnel’s safety status mainly requires the collection of construction personnel’s physical signs and cave environment data, and the early warning of abnormal status usually requires professional staff to make rapid judgments in a short time, which is costly and inefficient in terms of operation and maintenance. A single-classification support vector machine-based personnel safety status detection and early warning model is proposed to address this phenomenon. First, by deploying sensor devices at the site, we obtain data on the safety state of an actual tunnel construction scene and construct an OCSVM model for abnormal state prediction. Then the model is retained for early warning state testing, collecting relevant environmental data as well as construction personnel’s physical signs data from engineering examples. Finally, we conduct horizontal different parameter model experiments and vertical different early warning state proportional data experiments to evaluate the performance of the model for personnel information security state judgment. The experimental results show that the accuracy rate of personnel security status early warning reaches more than 90%. In particular, it provides a more efficient detection means for the judgment of personnel security status.
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