Aiming at the problem of insufficient accuracy caused by the insufficient mining of spatiotemporal features in the process of unsafe behavior and danger identification of construction personnel, the traditional two-stream convolution model is improved, and a two-stream convolution dangerous behavior recognition model based on Faster R-CNN-LSTM is proposed. In this model, the Faster R-CNN network is connected in parallel with the LSTM network. The Faster R-CNN network is used as the spatial flow, and the human spatial motion posture is divided into static and dynamic features to extract the anchor point features, respectively. The fusion of the two is used as the output of the spatial flow. An improved sliding long-term and short-term memory network is used in the time flow to increase the extraction ability of the time series features of the construction personnel. Finally, the two branches are fused in time and space to classify and identify whether the construction personnel wear safety helmets. The results show that the MAP of the improved Faster R-CNN-LSTM network framework is increased by 15%. The original CNN-LSTM network framework detected four targets, but there was one misdetection, with an accuracy of 91.48%. The improved frame detection accuracy reaches 99.99%, and there is no error detection. The proposed method is superior to the pre-improvement and other methods that can effectively identify the unsafe behavior of construction workers on construction sites and also has a good distinction effect on fuzzy actions.

Soft coal is characterized by low strength and weak bonds, which play a key role in the occurrence and development of dynamic disasters. A more thorough understanding of the failure mechanics and infrared radiation characteristics of soft coal at various moisture contents is needed. In this study, infrared radiation experiments were conducted for soft coal at various moisture contents. The results indicate that moisture content affects compressive strength and elastic modulus of soft coal with compressive strength and elastic modulus being highest at moderate. Moisture has a substantial influence on the infrared radiation of compressed coal samples. Soft coals with high moisture contents show a smaller fluctuation of the average infrared radiation temperature curve and a stronger direct relationship of temperature and stress in the linear elastic stage. The coal samples with various moisture contents show a warming trend during loading. The increased amplitude of infrared radiation temperature per unit stress shows a linear relationship with moisture content. A large number of high temperature red spots appear along the diagonal line of coal samples with 0% moisture content before fracture, while changes in the infrared thermal image of the coal sample with 4% moisture content were negligible during loading.

Underground coal seam mining can redistribute the stress of the coal and rock, in which gas distribution and migration occurred. In order to draw out the gas in the goaf effectively, reduce the goaf gas emission and avoid the over ranging, the high level drilling site is employed for goaf gas drainage, and rock crack development situation and gas enrichment area are simulated by RFPA^2D-Integrated to locate the position of drilling hole. The result indicated that the caving-in zones and fracturing zones of the coal cave mined out area are 0-13m and 13-42m to the roof of the coal, respectively. The optimal position of drilling hole is 14-35m. The practice survey revealed that the overall quantity of gas extraction is 8.47m³/min, and it solves the problem of goaf gas emission better. The result of simulation is accord with the field.

There has been extensive research on the partial differential equations governing the theory of gas flow in coal mines. However, the traditional Proper Orthogonal Decomposition–Radial Basis Function (POD-RBF) reduced-order algorithm requires significant computational resources and is inefficient when calculating high-dimensional data for coal mine gas pressure fields. To achieve the rapid computation of gas extraction pressure fields, this paper proposes a model reduction method based on deep neural networks (DNNs) and convolutional autoencoders (CAEs). The CAE is used to compress and reconstruct full-order numerical solutions for coal mine gas extraction, while the DNN is employed to establish the nonlinear mapping between the physical parameters of gas extraction and the latent space parameters of the reduced-order model. The DNN-CAE model is applied to the reduced-order modeling of gas extraction flow–solid coupling mathematical models in coal mines. A full-order model pressure field numerical dataset for gas extraction was constructed, and optimal hyperparameters for the pressure field reconstruction model and latent space parameter prediction model were determined through hyperparameter testing. The performance of the DNN-CAE model order reduction algorithm was compared to the POD-RBF model order reduction algorithm. The results indicate that the DNN-CAE method has certain advantages over the traditional POD-RBF method in terms of pressure field reconstruction accuracy, overall structure retention, extremum capture, and computational efficiency.