In order to meet the needs of fast and efficient transportation for coal mine drilling construction operations with trackless transportation conditions, a trackless rubber tyre vehicle integrated drilling rig was used to travel directly from the ground to the drilling site without the need for secondary transportation. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. Based on the overall technical requirements, multiple key technologies of the trackless rubber tyre vehicle were designed and analyzed, and the static load capacity and dynamic characteristics of the frame under two working conditions, namely the driving process and the drilling process, were simulated and analyzed. The simulation results show that the stress and displacement meet the design requirements of the frame load capacity during the walking and drilling processes, and it has good stability. After the installation and adjustment of the rubber wheel chassis, a climbing test and comprehensive performance test were carried out. The test shows that the whole vehicle meets the 15 ° large angle climbing condition, all data meet the standard requirements, and the performance meets the transportation needs of the coal mine drilling rig. It can adapt to the complex working conditions underground in coal mines.

In order to guide the optimal design and daily maintenance of explosive capacity bombs, the response characteristics of 20 L explosive capacity bombs under the explosion action of explosives were studied. Internal explosion tests of different masses of trinitrotoluene (TNT) explosive capacity bombs were designed. The strain distribution and variation law of the outer wall of the explosive capacity bomb were measured, and the internal explosion impact overpressure of the explosive capacity bomb was obtained. According to the test results, the equal-scale simulation calculation model was calibrated, and the propagation law of shock wave inside the explosive capacitor bomb after the explosive explosion was calculated, and the pressure distribution in the explosion field was obtained. The results show that the simulation results of internal pressure of explosive charge are basically consistent with the experimental results. Under the action of explosive explosion, the internal pressure of explosive capacitor presents a complex structure of multi-peaks, the maximum pressure appears at the corner of explosive capacitor, and the maximum strain of the outer wall of explosive capacitor appears at the lower position of the explosion center plane.

Image segmentation is a fundamental problem in medical image analysis, the typical deep learning based UNet architecture (UNet) and its variants are widely used in retinal vessel segmentation. However, the UNet network extracts feature information from images through local convolution modules, which makes the global information of the images difficult to be correlated and the long-distance dependencies between pixels difficult to be effectively captured. Considering the problems with the UNet network model and the characteristics of retinal vascular images, an attention module was added to the skip connections of UNet to capture long-distance dependencies between blood vessels. In addition, to enhance the segmentation ability of the network, the group normalization(GN) was used instead of the original batch normalization (BN) of the UNet network model, and the corresponding groups were selected for different channels. To update parameters and optimize the network, the final cross entropy loss function was designed using the side output layer and the final output layer. Experiments are implemented on the DRIVE dataset and CHASEDB1 dataset, and the experimental results show that the proposed model has better image segmentation performance.

The cathode flow channel of proton exchange membrane fuel cell (PEMFC) serves as the site of oxidant reduction, and the interaction of the flow channel configuration and operating parameters is one of the keys to enhance the performance of the cell. A three-dimensional proton exchange membrane cell model with cathode sidewall shrinkage runner was established to meet the design requirements of PEMFC sidewall shrinkage runner regarding the operating parameters. The changing rules of electrochemical performance, temperature distribution on the membrane surface and water content distribution were investigated under different temperatures, pressures and cathode stoichiometric ratios. It is shown that under the constant operating parameters, the current density curves of the ridge centerline and the flow channel centerline are impulsively fluctuated, and the temperature curves of the membrane surface and the water content curves of the membrane surface are regularly fluctuated. The current density, temperature and water content at the ridge centerline are obviously higher than those at the flow channel centerline. Under the change of operating parameters, when the pressure is increased from 0.1 MPa to 0.3 MPa, the current density is increased from 0.860 A/cm² to 1.500 A/cm², with an increase of 74.4%. When the temperature is increased from 50 ℃ to 80 ℃, the current density is increased from 0.822 A/cm² to 0.856 A/cm², with an increase of 4.1%. And when the cathode stoichiometry ratio is increased from 10 to 90, the current density is increased from 1.502 A/cm² to 1.568 A/cm², with an increase of 4.4%. Furthermore, a PEMFC output performance evaluation method based on the combined assignment method and the improved radar diagram method has been established. The cathode sidewall-retracted proton exchange membrane fuel cell is shown to exhibit excellent output performance under the operating parameters of 0.25~0.3 MPa, 70~80 ℃ and the stoichiometric ratio in the range of 70~90.

Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.

In order to solve the defects of poor water stability and easy disintegration of red clay, industrial solid waste [fly ash (FA), phosphogypsum] combined with cement (C) was used to improve red clay. The mechanical properties, water stability and micro-mechanisms of the industrial solid waste-cement amended red clay were investigated through indoor tests. The results show that the strength of the improved soil shows a trend of increasing and then decreasing with the increase of the ratio (R) of phosphogypsum replacing fly ash.When the cement doping is 7% and R=7%, the maximum dry density of improved soil increases by 2.6%, the 7-day unconfined compressive strength (UCS) increases by 11%, and the 28-day UCS increases by 57%, which meets the bearing standard of subgrade for road use. There is no obvious change in the water-filled specimen after the 7-day maintenance, the resistance to disintegration is enhanced, and the water stability coefficient of the specimen reaches 92.9% in the 28-day maintenance. The water stability coefficient of the specimen reaches 92.9%, and the water stability coefficient increases 1.61 times. The microscopic analysis shows that the replacement of fly ash by phosphogypsum promoted the generation of new hydration products of ettringite and calcium-silicate-hydrate (C-S-H), which transforms the soil body from fragmented granular to a denser gel network structure, enhances the bonding between the red clay particles, and fills up the pore space at the same time. The results verifies the feasibility of industrial solid waste-cement-amended red clay as roadbed fill, provides a solid theoretical foundation and basis for engineering practice, and broadened the reuse of industrial solid waste.

The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation, located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region, pose significant challenges to reservoir evaluation and natural gas exploration and development. Traditional diagenetic facies identification methods are often low in accuracy, heavily reliant on specialized personnel, and time-consuming. There is an urgent need for a diagenetic facies identification method that is highly accurate, cost-effective, and fast. Firstly, based on cast thin section identification data, the lithology of the tight sandstone was determined using a ternary plot of components. Image processing techniques were then used to identify the types and proportions of pores and cements, and the diagenetic facies of the tight sandstone were classified. Secondly, the corresponding 1 019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range, median, uniformity, and skewness. These 6 types of well log data were standardized to a 0-1 range, and data imbalance was addressed using synthetic minority over-sampling technique (SMOTE). Finally, 10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison. The study found that ensemble learning algorithms, especially the extreme randomized trees (ET) algorithm, performs best in diagenetic facies identification, achieving higher accuracy and F₁ scores than traditional machine learning algorithms. This significantly improved identification accuracy and stability. The ET model was then used to predict the diagenetic facies of the JQ8 well, validating the feasibility of the method. This study provides effective technical methods and references for diagenetic facies research in tight sandstones.

In order to explore the influence of pore defects on the mechanical properties of concrete road and seek an equivalent model to replace porous concrete road to reduce computational time. Based on micromechanics methods, the effective elastic modulus, Poisson's ratio, coefficient of thermal conductivity and coefficient of thermal expansion of porous concrete were calculated. Three-dimensional double-layer concrete roads with randomly distributed, non-interference and varying sizes of spherical pores and their equivalent models were established to study their mechanical properties under three working conditions, namely, concentrated force, static vehicle load, and temperature-static vehicle load coupling, and further the simulation calculation time for each model was compared. The results show that under the coupling effect of temperature and static vehicle load, the increase of porosity has little effect on the temperature and displacement of porous concrete roads at the same depth and different times. Moreover, for the same porosity, the farther away from the pavement, the peak temperature shifts backward over time. When the porosity is within 8%, the actual porous model can be replaced by the Eshelby equivalent model, Mori-Tanaka equivalent model, or Self-Consistent equivalent model under concentrated force or static vehicle load, and by the equivalent model 2 under temperature-static vehicle load. With fixed computational power and constant porosity, the simulation time for the actual porous model far exceeds its equivalent model. Using equivalent models for research can significantly shorten the calculation time, and the computational efficiency can be approximately improved by about 99.8%.

The generalized finite difference method for seismic wavefields numerical simulation is capable of adapting to undulating stratigraphic interfaces, eliminating the staircase scattering effect caused by such interfaces, and enhancing the accuracy of forward modeling. However, when the second-order generalized finite difference method is used to solve the wave equation, low temporal accuracy can lead to temporal dispersion at larger time intervals, affecting the precision of forward simulation. A fourth-order generalized finite-difference forward modeling algorithm for the scalar wave equation was studied, along with its stability conditions and dispersion characteristics. By transferring the fourth-order time derivative to the spatial derivative term, fourth-order accuracy in time was achieved, effectively suppressing temporal dispersion. Compared to the second-order generalized finite-difference method, the fourth-order approach allows for larger time intervals, thereby reducing computational costs to some extent. Experimental results demonstrate that the proposed algorithm effectively mitigates both staircase scattering and temporal dispersion, yielding higher computational accuracy. When applied to reverse time migration, it produces high-quality imaging profiles.

The Shenzijing-Hashituo Subsag is located in the central part of the Changling fault depression in the Songliao Basin, an area with promising oil and gas exploration prospects. It represents a favorable oil and gas accumulation zone within the Songliao Basin. Understanding the tectonic evolution of this region and its influence on hydrocarbon genesis is crucial for guiding future exploration and development efforts in the Changling fault depression. Based on the structural characteristics, ancient drop of faults, activity rate and growth index method, the structural evolution of the depression was analyzed. Combined with the characteristics of stratum erosion, it was determined that the Shenzijing-Hasituo subsag was co-deposited in the early stage, and the Shahezi Formation was affected by compression and uplift at the end, resulting in overall erosion and segmentation. The uplift on the west side of the Hasituo depression was further strengthened during the Denglouku Formation period. At the same time, it is clear that the tectonic evolution of the fault depression layer in the Shenzijing depression-Hatshituo depression of the Changling Fault depression follows the longitudinal evolution law of fault depression-co-deposition-inversion. This early rapid subsidence and late inversion and uplift form a relatively favorable accumulation combination model of lower generation and upper storage. The research results can provide theoretical references for the restoration and further exploration of the ancient structure of the Changling fault depression.