This paper examines the feasibility of applying inorganic thermal-insulating concrete in high geothermal roadways in underground coal mines. This innovative material is based on a mixture of ceramsite, glazed hollow beads, cement, and natural sand, enhanced with varying degrees of basalt fibers. Fibers were used as a partial substitute in the mixture, in the following volumes: 0% (reference specimen), 5%, 10%, 15%, and 20%. Their compressive strength, permeability resistance, and thermal conductivity were studied. A high content of fibers tends to entangle into clumps during mixing, resulting in a significant reduction in the mechanical properties of compressive strength. The appropriate amount of fiber content can improve impermeability, and the permeability height of 5% fiber concrete was reduced by 22.5%. Experiments on thermal behavior showed that an increase of basalt fibers leads to a significant reduction in thermal conductivity. For concrete containing 20% fiber, the thermal conductivity for the reference specimen (0%) in the wet state was reduced from 0.385 W/(m∙°C) to 0.098 W/(m∙°C). There was a slight increase in thermal conductivity when the temperature increased from 30 °C to 60 °C. Despite the reduced mechanical strength, the resulting concrete is well-suited for use in the insulation of underground roadways, as numerical simulations showed that insulating concrete with optimal fiber content (15%) can reduce the average temperature of the wind flow in a high ground temperature roadway of 100 m in length in a mine by 0.3 °C. The final cost-benefit analysis showed that insulating concrete has more economic benefits and broad development prospects when applied to high geothermal roadway cooling projects.

The current research aimed to develop lightweight, environmentally friendly mortar materials using crop straw fibers with better insulation properties. The lightweight mortar samples were tested for moisture content, thermal conductivity and compressive strength on days 3, 7 and 28, respectively. Scanning electron tomography (SEM) was performed on the fiber–matrix bonding interface and internal fiber structure. The permeability rating was also measured to check the impermeability of the lightweight fiber mortar. Due to the high hygroscopicity of plant fibers, the thermal conductivity of the mortar was high at the initial molding stage; the thermal conductivity measured at day 28 decreased with increasing fiber content, while the mechanical properties gradually decreased. The impermeability test showed that the straw fiber mortar had better impermeability than the standard mortar. However, with the addition of 2% of 10 mm long fibers, we increased the compressive strength and thermal insulation properties. Numerical simulations verified that the fiber insulation mortar has good thermal insulation properties in high-temperature tunnels.

Current research efforts aim to develop insulating building materials to cope with hot tunnels by using crop straw fibers. The amount of straw fiber incorporated included different percentages of the gelling material mass: 0% (reference specimen), 2.5%, 5%, 7.5%, and 10%. The study focused on the concrete’s compressive strength, permeability, and thermal coefficient. The thermal coefficient of dry concrete decreased with increasing fiber content. However, the porosity of concrete mixed with more straw fibers increased, thus decreasing compressive strength. Concrete with a fiber content of 7.5% had the best overall performance, and it had a low thermal conductivity (0.158 W/(m·k)). According to COMSOL simulation results, the tunnel wind flow temperature of concrete with 7.5% fiber content was lower than the tunnel wind flow temperature of plain concrete. Thus, the obtained straw fiber heat-insulating concrete is a promising candidate material for tunnel insulation.

To investigate drivers’ gaze behavior and the characteristics of their gaze positions while driving, a natural driving behavior test method was employed alongside a non-contact eye-tracking device to conduct an in-vehicle experiment for collecting gaze data. Initially, we utilized the traditional approach to delineate the area of interest, analyzing variations in pupil diameter, gaze positions, and the duration spent in each area throughout the driving task, thereby compiling statistics on drivers’ gaze patterns. Subsequently, harnessing the You Only Look Once version 5 architecture, we can precisely identify the position of vehicles and obstacles from the captured images. Enhancements to the network model—including streamlining and integrating an attention mechanism—have significantly refined target detection accuracy. In the final analysis, by correlating drivers’ gaze data with the positional information of upcoming obstacles, we can accurately assess where drivers are looking. This fusion of data allows for a more nuanced observation of gaze dispersion and position within a one-second timeframe, providing valuable insights into drivers’ attention distribution and driving behaviors.