Advanced perception and control method of harmful gas during construction period of coal tunnel based on DeepAR

Abstract

Effective real-time treatment and control of harmful gases are key to ensuring the safety of tunnel construction workers. Currently, the monitoring ability of harmful gases is insufficient to match the processing needs, which poses significant risks to the safety of tunnel construction workers. This paper proposes an advanced perception and treatment method for harmful gases during tunnel construction, utilizing the DeepAR algorithm. Real-time monitoring of the concentration and diffusion of harmful gases is conducted, and a harmful gas concentration prediction model is established using the DeepAR algorithm, achieving advanced perception of harmful gases during tunnel construction. The harmful gas treatment plan is developed in advance, and the effectiveness of the proposed method is demonstrated by simulation testing under realistic field scenarios and comparing with other prediction models. The method was applied in a coal mine tunnel in Qinghai Province, achieving an accuracy rate of 94.3%, which is higher compared to those obtained using RNN and LSTM algorithms. Moreover, the computational time is less than 60 s. The method provides timely perception of the concentration distribution of harmful gases in the tunnel and proposes targeted treatment measures, verifying the effectiveness of the prediction model from the perspective of practical engineering application.