Wood consists of a number of early and latewood alternately, in order to predict the strength of the mechanical properties of the specimen according to the proportion of early and latewood, a composite material model with early and latewood as the basic modeling unit was established. Starting from the fine level of wood, the wood is regarded as a composite material ideally bonded by two kinds of materials with different mechanical properties of early and late wood. Taking Yunnan pine as the research object, the respective mechanical parameters of early and late wood were obtained respectively, and according to the theory of composite plywood, the composite material tensile strength model of early and late wood with smooth grain was set up, and the test is utilized to verify the results. The results show that it is feasible to consider the wood as early and latewood composite material and use the laminate theory to predict its mechanical properties. The relative errors between the theoretical values of tensile elastic modulus and tensile strength and the experimental values are within 10%, which is highly reliable, and according to this model, the tensile strength and elastic modulus can be calculated through the measurement of the volume fraction of the latewood in the material, which will provide a good basis for the subsequent early and latewood tests and the further study of the early and latewood related models. This model can provide a reference basis and theoretical foundation for the in-depth study of the subsequent early and late material test and the related model of early and late material.

Due to the negative impact of abandoned powdered clay on land waste and pollution, it is beneficial to improve the powdered clay and use it for backfill in engineering construction.The effects of lignin fiber content and cement content on the unconfined compressive strength of silty clay excavated from a tunnel along the Yangtze River in Hangzhou,Zhejiang Province were studied. The formation mechanism of the compressive strength was analyzed. Finally,the pore microstructure of the sample was quantitatively analyzed by SEM experiment. The results show that the compressive strength reached the maximum value when the lignin fiber content is 4%,and the unconfined compressive strength was greater than that when the lignin fiber content is 2%, 6% and 8%. No matter how much lignin fiber content is,the unconfined compressive strength q'_u increases gradually with the increase of cement content. With the addition of lignin fiber,the average diameter of pores gradually concentrated in the range of particle size less than 1 μm,and the proportion of pores 1~2 μm and 2~4 μm increased with the increase of cement content. With the increase of lignin fiber and cement content,pore abundance mainly concentrated in the range of 0.2~0.5.With the addition of lignin fiber,the particle abundance mainly concentrated in the range of 0.1~0.6,and the particle distribution showed a “mountain” pattern with the addition of cement.

To investigate the mechanism of interaction between root-soil composites and rock interfaces in the ecological protection of rocky slopes, physical models of interfaces were established, considering different plant species, types of soil for slope protection, and degrees of rock weathering. Under natural stress conditions, direct shear tests on the root-soil-rock interfaces were conducted. The variation patterns of shear strength and shear displacement at the interfaces were revealed, and the influence mechanisms of plant root morphology, types of protective soil, and rock weathering degree on the shear strength of the interfaces were analyzed. The results indicate that plant roots, penetrating through the slope protection soil and into the bedrock fissures, significantly enhance the shear strength of the soil in the shear zone and the anti-slip capacity of the soil-rock interface, thereby improving the overall cohesion between the slope protection soil and the rock. The shear strength of the interface is positively correlated with the vertical extension of plant roots and the degree of rock weathering. Compared to scenarios without plants, the planting of vetiver grass increased the peak shear strength by 53.9%, and under highly weathered bedrock conditions, the peak strength increased by 22.4%. Compared to ordinary soil, substrate soil containing binders and aggregating agents significantly improved the shear performance of the soil, increasing the peak shear strength of the interface by 20.1%.

The data-driven approach of machine learning enables the intelligent construction of TBM(tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE (kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F₁ of the four-classification problem is 0.705, and the average F₁ of the two-classification problem is 0.797, which are better than the four selected base classifiers.

Exploring the impact of various hole shapes on the acoustic emission properties of rocks and the fracture mechanisms within rock structures containing holes is of utmost importance, as it enables the detection of fracture progression in rock engineering and the prediction of instability and failure in defective rocks. The mechanical properties, acoustic emission evolution patterns, precursor characteristics of failure, and failure mechanisms of red sandstone samples with different hole shapes were investigated through uniaxial compression tests and acoustic emission systems. The results indicate that the compressive strength, elastic modulus, and strain energy of the intact specimen are approximately 1.4 times, 1.3 times, and 1.7 times greater, respectively, than those of the specimen containing holes. There is a mutation point in the evolution of the multifractal spectrum of A_E at 86%~95% of the peak stress, where the width undergoes a transition from an average low value to a sudden increase before and after the mutation point. This change is accompanied by an increase in fluctuation range, from small to large. The A_E waveform characteristics are distinguished by the presence of dual main frequency bands. As the sample approaches failure, a significant number of low-frequency and high-amplitude signals, along with high-amplitude and high-amplitude signals, are generated. These peculiar trends in acoustic emission can be used as precursors to the critical instability of red sandstone. Based on the correlation between the main frequency of the acoustic emission signal of the rupture event and the fracture mechanism of the rock, the failure mechanism of the intact and square cavity red sandstone is mainly characterized by tension-shear mixed failure, and the shear failure of circular cavity red sandstone is revealed, which effectively avoids the subjectivity of the RA-AF failure mode classification method, and the research results can provide certain guiding significance for engineering design and optimization.

The study focused on wrap-around reinforced soil retaining walls and proposed a calculation method for panel displacement. The horizontal displacement was divided into two components for calculation: the horizontal displacement caused by the strain of reinforcement and the overall horizontal displacement generated by the horizontal earth pressure acting on the back of the reinforced zone. When calculating the horizontal displacement caused by the reinforcement strain, the reinforced zone was divided into subzones through the potential failure surface of reinforced soil retaining wall and the natural repose angle of soil. The horizontal distribution of the reinforcement load was assumed, yielding a simplified calculation model for the horizontal displacement caused by reinforcement strain. For the calculation of the overall horizontal displacement of the reinforced zone, the zone was treated as a ‘cantilever beam’, taking into account the variation in elastic modulus of the reinforced zone with height. The theoretical results obtained through the proposed method were compared with experimental and numerical simulation results. The distribution trend of the displacements was basically consistent, indicating that the proposed method can effectively calculate the panel displacement of wrap-faced reinforced soil retaining walls.

Addressing challenges such as large memory footprint, high computational complexity, and insufficient real-time detection speed in road crack detection models for complex scenarios, a highly efficient and precise algorithm named FCG-YOLO was proposed. Lightweight modules and attention mechanisms were integrated, and traditional feature fusion pyramids were enhanced.The algorithm incorporates PConv into the residual calculation module of YOLOv8n to introduce the improved C2f_Faster structure, thereby reducing model parameters and computational complexity. To enhance detection accuracy, GAM(global attention mechanism) was introduced into the backbone, and the Feature Fusion Pyramid SPPF was improved to SPPFCSPC module, enhancing the model’s ability to represent and fuse features of road cracks.The impact of each module on algorithm performance was verified through ablation experiments, identifying a lightweight and accurate model configuration. Furthermore, the robustness and generalization of the algorithm were explored in practical application scenarios.FCG-YOLO demonstrates outstanding detection efficiency, achieving a detection accuracy of 90.3% mAP50 and 74.4% mAP50-95 on the validation set, with a detection speed of 345 frames per second. These results highlight its high detection efficiency and significant practical value.

Frost damage is a key problem in the construction of alpine tunnels. A numerical model was established to consider heat conduction and convection for designing effective thermal insulation measures. Orthogonal test conditions were designed to analyze the significance of different thermophysical parameters on lining temperature and their influence on lining damage. The results show that the convection coefficient, the thermal conductivity of the insulation layer, and the thermal conductivity of the surrounding rock have significant influences. The thermal conductivity of the insulation layer has the most significant impact. As its thermal conductivity increases, the lowest lining temperature decreases, and the damage level increases. An increase in the convection coefficient decreases the minimum lining temperature and increases damage. Higher thermal conductivity of surrounding rock raises the minimum lining temperature, spreading damage from the vault to the sides. Reducing the thermal conductivity and convection coefficient of the insulation layer, while increasing the thermal conductivity of the surrounding rock, improves overall damage distribution.

To solve the engineering problem of unclear standards and strong subjective experience when shield tunneling drivers set excavation parameters, which makes it difficult to control the shield tunneling attitude, an intelligent prediction model for shield tunneling attitude that considers the comprehensive effect of geological conditions, tunnel structure, and excavation parameters was proposed. Firstly, AWPSO (adaptive inertia weight particle swarm optimization) algorithm was established. Then, a shield attitude prediction model was constructed by combining GRU (gated recurrent unit) neural network, where the AWPSO algorithm was used to determine the optimal combination of hyperparameters in the GRU neural network. Finally, a case study was conducted to verify the on-site monitoring data between Zhangjiang Station and Resort Station on the Shanghai Suburban Railway Airport Connection Line. The results indicate that the proposed shield tunneling attitude prediction model based on AWPSO-GRU has high reliability and engineering practicality, which can provide reference and basis for setting construction parameters during shield tunneling.

Vehicle performance and energy efficiency can be significantly enhanced by PGS-FHEP (planetary gear set based flywheel hybrid electric powertrain). The main components were designed and matched, and DP (dynamic programming) control strategy was introduced based on ECMS (equivalent consumption minimization strategy) to obtain the optimal SOC (state of charge ) trajectory. The initial optimal equivalent factor obtained by GA (genetic algorithm) was adjusted in real time to ensure that the actual SOC trajectory is consistent with the optimal trajectory. Thus, a real-time A-ECMS (adaptive equivalent consumption minimization strategy) was built, and the three control strategies were simulated and compared under CLTC-C (China light-duty commercial vehicle test cycle) condition. The results show that under the control of A-ECMS, compared with the traditional ECMS, the comprehensive energy consumption of FHEV (flywheel hybrid electric vehicle) equipped with the PGS-FHEP is reduced by 2.51%, and the control effect is closer to the DP control strategy. The energy recovery rate of the PGS-FHEP is 57.72%, of which 23.64% is recovered in the form of mechanical energy. In addition, the participation of the flywheel significantly reduces the peak power of the battery during energy recovery process.