Closely-spaced outburst coal seams (COCS) is the main condition of coal seams in southwest China, and gas disasters are one of the major problems affecting coal mine safety. Mining a protective seam and pre gas extraction are the most safety way to improve the efficiency of mining under these conditions. However, low pre-mining gas extraction efficiency coupled with the close proximity of adjacent working faces is a problem. When mining at an old working face has been completed but the new working face is not yet ready to be mined, coal-bed gasses can flow into the new working face from adjacent seams and this commonly causes methane monitoring instruments to sound an alarm. These gas extraction difficulties lead to a conflict between mine safety and profit. To solve these problems, a sequential approach for integrated coal and gas mining of closely-spaced outburst coal seams is introduced in this paper. Two fundamental principles are proposed: (1) Fully coordinating the spatiotemporal relationships between gas extraction, roadway development, and coal mining to maximize both mine safety and coal and gas production; (2) Defining a mining sequence for outburst coal seams and choosing the coal seam with the weakest outburst risk as the protective seam. A system for comprehensive gas extraction in underground coal mines is divided into four stages for gas extraction: gas extraction before coal roadway tunneling, gas extraction before coal mining, gas extraction during coal mining, and gas extraction from the goaf after coal mining. The Songzao mining area, China, is used as a case study to demonstrate the effectiveness of this model, and it brings three major benefits: it improves underground coal mine safety with physical gas accidents decreased by 66.8%, it makes underground coal-bed methane (CBM) extraction more efficient with the average gas extraction rate were respectively 45.13 m3/t and 62.4%, the highest in China, and it reduces greenhouse gas emissions equivalent to 3.5 million tonnes of carbon dioxide. This study could be used as a valuable example for other coal deposits being mined under similar geological conditions.

Tunnel lining crack is the most common disease and also the manifestation of other diseases, which widely exists in plain concrete lining structure. Proper evaluation and classification of engineering conditions directly relate to operation safety. Particle flow code (PFC) calculation software is applied in this study, and the simulation reliability is verified by using the laboratory axial compression test and 1:10 model experiment to calibrate the calculation parameters. Parameter analysis is carried out focusing on the load parameters, structural parameters, dimension, and direction which affect the crack diseases. Based on that, an evaluation index system represented by tunnel buried depth (H), crack position (P), crack length (L), crack width (W), crack depth (D), and crack direction (A) is put forward. The training data of the back propagation (BP) neural network which takes load-bearing safety and crack stability as the evaluation criteria are obtained. An expert system is introduced into the BP neural network for correction of prediction results, realizing classified dynamic optimization of complex engineering conditions. The results of this study can be used to judge the safety state of cracked lining structure and provide guidance to the prevention and control of crack diseases, which is significant to ensure the safety of tunnel operation.

Submerged floating tunnels (SFTs) are an innovative traffic structure for transportation in deep and long-distance ocean environments. SFTs have the risk of being subjected to wave action in the complex ocean environment. Therefore, in order to ensure the safety and stability of SFTs during their service, the dynamic behavior of an SFT subjected to the action of waves is experimentally investigated in this study. Based on the wave–current flume, a physical scale-model experiment of an SFT for studying the dynamic behavior of an SFT subjected to regular waves interactions is deeply and thoroughly explored. The results show that the wave outputs from the wave-making system are verified to be reliable. The influence of major load parameters on the dynamic behavior of the SFT are deeply and thoroughly explored. The effects of wave height and wave period on acceleration, wave pressure, anchor cable force and displacement in an SFT are discussed. This experimental study has theoretical and practical significant for SFT design and safety.

Aiming at the tunnel gas disaster can produce major safety problems such as combustion, explosion, and coal and gas outbursts. Firstly, a time series consisting of the tunnel gas concentration was used as the entry point for the article, and the gas prediction models based on multiple intelligent computational methods were established for comparison to determine the optimal network prediction model. Then, this study proposed a stepwise prediction method which is based on the optimal network prediction model for gas disaster prevention during the construction period of tunnels at the excavation workface. The length of the input step, output step, and interval step were considered by the method to investigate the effect on the predictive performance of the model. The model was extrapolated by the rolling prediction method, and the adaptive grid search method was used to determine the optimal parameter combination of stepwise prediction. Finally, a stepwise prediction of short-term gas concentration trends was achieved for each construction process at the excavation workface. As a result, the best LSTM network prediction model was preferred with an R² value of 0.94 for the fit and MAE and RMSE values of 3.2% and 4.3%. Results based on stepwise predictions showed that single-step prediction is more accurate than multi-step predictions when a reasonable input step size was determined. Moreover, with the length of both the interval step and the output step, the model prediction accuracy showed a decreasing trend. Generally speaking, the single-step continuous and interval prediction of the gas concentration at the excavation workface can be realized by the gas stepwise prediction method, and the gas concentration value can be obtained at any time in the prediction. It can also realize the transformation from single-step point prediction to multi-step trend prediction, and obtain the accurate prediction of gas concentration change trends in the stepwise prediction range (t+1~t+5). Therefore, the important security guarantee can be provided by stepwise prediction for subsequent gas disaster safety prevention and efficient tunnel production.