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%.

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.

To investigate the stress characteristics of the primary support in the shallow buried biased section of the tunnel in fully weathered granite strata. Based on the Xiangsishan Tunnel, laboratory tests were conducted to determine the mechanical parameters including cohesion and internal friction angle of fully weathered granite. The construction process was simulated by FLAC 3D numerical software for the shallow buried bias section of the tunnel entrance. To study the effects of varying ground slopes, tunnel depths, and soil-rock interface locations on the stress characteristics of the primary support structure. The results indicate that, under various impact factors, the axial force and bending moment of the primary support exhibit the distribution characteristics of “upper large and lower small”, while the safety factor displays the opposite trend. The internal stresses of the primary support are distributed asymmetrically as the ground slope increases. The positive bending moment shifts toward the deeper-buried side and gradually increases, while the negative bending moment and axial force increase at the arch waist on the shallow-buried side. When the ground slope reaches 40°, the safety factor of the primary support falls below the standard allowable value. However, the stability of the primary support can be enhanced by implementing multi-stage variable slopes. As the depth of the tunnel increases, the asymmetric distribution of internal stresses in the primary support decreases, while the overall magnitude of internal stresses continuously increases. When the soil-rock interface crosses the tunnel at various locations, the magnitude of the internal stresses in the primary support is significantly affected, whereas the distribution pattern remains relatively stable. The reliability of the stress characteristics of the primary support under various impact factors was verified through on-site monitoring of the stress variations in the steel arch and concrete at various locations of the tunnel.

Large section tunnel in-situ expansion excavation is prone to induce ground settlement, posing a threat to the service safety of surrounding structures. However, the settlement evolution of the overlying strata during tunnel expansion excavation are not yet clear. A method combining theoretical analysis, physical model testing, and engineering practice was adopted to investigate the settlement evolution of the overlying strata during expansion excavation of tunnels. A theoretical model for tunnel expansion excavation settlement was established. The research findings indicate that the settlement of the overlying strata above the tunnel exhibits a sudden increase characteristic, with the expansion excavation settlement zone showing a parabolic distribution, which is primarily related to the cohesive force of the rock mass and its brittle fracture characteristics. The strata settlement shows a nonlinear increasing relationship with the distance from the tunnel, mainly influenced by the non-uniform attenuation of excavation unloading disturbance. The theoretical model curves can reflect the settlement evolution consistent with the physical model tests, with an average deviation of 4.8% between the experimental and theoretical values. Considering the influence of the correction coefficient α for tunnel support on the measured engineering values, the model with α=0.7 and α=0.4 can better predict the range of surface settlement after tunnel expansion excavation and support. The research results provide a theoretical method for calculating strata settlement during tunnel in-situ expansion excavation.

The optimization and operational control of ventilation systems on the offshore platforms is of great significance for improving cabin environmental quality and ensuring occupant health. In response to the current lack of a comprehensive design standard system for offshore platform ventilation, domestic and international specifications for shipboard and land-based ventilation systems were systematically classified and summarized to establish a dedicated design standard framework tailored to offshore platforms. Ventilation rate models, indoor dynamic models, air quality models, and energy consumption models were analyzed. Furthermore, an overview of ventilation optimization methods was provided for three critical areas: living areas, equipment areas, and storage areas. Current challenges and technical difficulties in system design and operation were analyzed, and feasible future development strategies for ventilation system design and optimization were proposed. The research results provide scientific basis and technical guidance for the design, operation, and energy-saving measures of offshore platform ventilation systems.

With the emergence of deep learning technologies, speech enhancement methods based on deep learning have seen widespread application and generally surpass traditional approaches in performance. The fundamental framework of noise reduction signal processing in speech enhancement was outlined and progressively delved into the latest advancements in deep learning-driven speech enhancement models. A comprehensive organization of deep learning-based speech enhancement algorithms was provided, detailing the principles, characteristics, evaluation metrics, and representative studies of various neural network-based methods. The advantages and limitations of these approaches were thoroughly assessed. Finally, in light of the current developmental landscape, the core challenges encountered in the speech enhancement process were analyzed, and future developmental trajectories were discussed and predicted.

The application of building information modeling(BIM) technology in the construction industry in China has developed rapidly, and its further development is inseparable from the support of BIM standards. In view of the importance of BIM standards, it is necessary to review the existing researches on BIM standards. Firstly, three key research topics, namely theoretical research, technical research and application research of BIM standards, were determined by keyword cluster analysis method. Secondly, for each research topic, the methods and results of existing research were explained and summarized through critical review, with typical cases being enumerated. Then, the evolution trend of BIM standard research was analyzed by using the timeline map generated by keywords. The results indicate that the extension of industry foundation classes(IFC) standard and the technical and application research based on IFC will still be the research hotspots. At the same time, research on BIM standard framework will continue. Furthermore, future research on BIM standards should also focus on three cutting-edge areas: technological integration, expansion of application scenarios, and cross-disciplinary collaboration. The research results will help further develop BIM standards and improve the formulation and application level of BIM standards.

To mitigate the risk of annular pressure buildup caused by solid-phase deposition in the B and C annuli of deep-water wells, experimental tests were conducted on sedimentation behavior using common deep-water drilling fluid systems. The sedimentation height and post-settling solid-phase permeability of various drilling fluids were measured. Based on the parameters of solid phase percolation characteristics, and considering the impact of annular fluid solid deposition, a predictive analytical method was established for annular pressure under percolation conditions. Case analysis was conducted to validate the approach. Results show that the sedimentation height follows the order: oil-based drilling fluid > EZFLOW drilling fluid > HEM drilling fluid. In contrast, the post-settling solid-phase permeability is ranked as EZFLOW drilling fluid > HEM drilling fluid > oil-based drilling fluid, with a maximum permeability of 2.216 μm². Under annular fluid solid-phase deposition conditions, reductions in annular fluid viscosity, increases in formation permeability, and longer open-hole cement sheath sections reduce fluid viscous resistance, enlarge the seepage contact area with the formation, and enhance fluid flow. Therefore, reducing drilling fluid viscosity and extending the open-hole cement sheath length can improve the pressure release capacity in the B and C annuli of deep-water wells. However, the presence of solid-phase deposition significantly restricts seepage flow rates compared to conditions without deposition, leading to a potential risk of incomplete pressure relief following solid-phase sedimentation.

Due to the high calorific value of energetic materials combined with their flammable and explosive nature. The propagation in the tube will affect the shape and geometric dimensions of the tube, resulting in the propagation of detonations is affected. In order to deeply investigate the influence of annular perturbation on the propagation of detonation waves, accurately and clearly observe the propagation dynamics of the detonation wave in annular tube, and better reflect the influence of boundary layer effect and curvature tube wall on detonation waves.An annular disturbance detonation experimental device was built up. Annular disturbance was realized by adding disturbance tubes with different diameters to the end of the smooth circular tube. The detonation wave velocity, triple-point trajectory, and cell structure were observed by using a data acquisition system composed of pressure sensors and smoke films. The experimental system of annular disturbance detonation was constructed successfully. It was able to obtain the ideal propagation data with remarkable regularity and high reliability. After verification, the detonation experimental device built has excellent data acquisition capabilities, and the obtained data confirms that the device is scientific and effective. The experimental system built contributes to providing theoretical support for accident prevention and control, allowing scientific instruments to play a more significant role in supporting technological innovation and societal development.

To improve the accuracy of precipitation forecasts and address the limitations of traditional numerical weather prediction models in forecast precision and computational efficiency, a meteorological large model was combined with a deep learning post-processing approach was combined. A case study was conducted for precipitation forecasts over Shaanxi Province during 2008—2018. Based on meteorological variable fields output by the FourCastNet model, a pre-trained model mapping meteorological fields to regional precipitation was constructed using Bayesian-optimized convolutional neural networks (CNN)/long short-term memory (LSTM) networks. The results indicate that this method outperforms traditional numerical weather prediction models in terms of spatial resolution and forecast accuracy. The regionally fine-tuned forecasts more accurately capture the spatiotemporal distribution of precipitation. Furthermore, the Bayesian-optimized deep learning post-processing algorithm effectively mitigates the impact of initial field biases on forecast results. These findings demonstrate the significant potential of integrating meteorological large models with deep learning post-processing algorithms for accurate precipitation forecasting, providing scientific support for disaster prevention, agricultural production, and water resource management.