To solve the durability of flexible base asphalt pavement, especially its anti-rutting problem, a design method on durable asphalt pavement of flexible base on anti-rutting performance was put forward in the paper, based on many experiments and calculations. Firstly, a method that asphalt could be selected according to penetration and the anti-rutting factor of its base asphalt was found, which solved the problem of the asphalt selection of the flexible base asphalt mixture design. Meanwhile, a method of skeleton-density structure gradation design was proposed based on the fractal void ratio of coarse aggregate, fractal volume of fine aggregate in coarse aggregate, penetration, fractal dimension of gradation particle size, and rutting tests, which effectively solved in advance the rutting and fatigue performance of flexible base asphalt mixtures. Then, on the basis of the fatigue damage, a calculation method of fatigue life was suggested, which solved the problem that the fatigue damage of asphalt mixtures rarely considered the combined effects of creep damage and fatigue damage. In addition, a calculation method of rutting was formulated based on vehicle dynamic load and ANSYS 16.0 software. Lastly, the feasibility of the design method on durable asphalt pavement of flexible base on anti-rutting performance was verified combining with the real engineering of a supporting project and several numerical calculations and tests.

The research on the dynamic response and influencing factors of shield tunnel lining under earthquake demonstrates significant engineering value in guiding the design of antiseismic tunnels. In this paper, a nonlinear finite element model of soil-tunnel interaction is established based on FLAC finite difference software, and then MohrCoulomb elastoplastic model and dynamic plastic damage model are used to simulate the dynamic characteristics of soil and lining damage of tunnel, and the seismic waves of South Iceland are selected to analyze the residual internal force, dynamic internal force distribution, and the relative deformation of the top and bottom of the arch of the shield tunnel under the earthquake load. Meanwhile, the effects of depth tunnel, lining thickness, and tunnel diameter on the dynamic response of the tunnel are discussed. In addition, the interaction law of horizontal parallel tunnel and the amplification effect on the surface acceleration are also studied. The results show that under the action of a strong earthquake, the bearing capacity of the tunnel decreases sharply, the lining is destroyed, and a large residual internal force appears. When the buried depth of the tunnel is small, the nonlinear effect is more significant, and the R value increases at first and then decreases with the increase in the seismic acceleration. The maximum dynamic bending moment and maximum dynamic axial force of the tunnel lining aggrandize obviously with the increase in tunnel diameter and lining thickness. In particular, the dynamic bending moment has internal force redistribution and deflection under the condition of large tunnel diameter and small lining thickness. Moreover, the interaction of parallel tunnels affects the distribution of internal force and the magnitude of adjacent surface acceleration.

This study takes the multisensor monitoring data of tunnel support structure and surrounding rock mechanical properties during TBM construction as the research object, integrates the monitoring data of multisensor through an intelligent optimization algorithm, and explores the self‐consistent fusion characteristics between the multisensor structural mechanical properties and the field monitoring data. At the same time, the support structure parameters and construction progress in the tunnel construction process are dynamically adjusted and scientifically integrated through the information feedback of monitoring data. The research results are as follows: (1) The tunnel excavation rate is different under different rock strengths and surrounding rock levels. The overall performance is that the harder the surrounding rock, the lower the surrounding rock level, and the faster the tunnel excavation rate. The relationship between the FPI parameters of surrounding rock and the deformation of tunnel surrounding rock is closer, and the correlation coefficient reaches 98%. The mechanical properties of the surrounding rock have the greatest influence on its deformation. (2) The rotation speed, thrust, and torque of TBM machinery as well as the surrounding rock workability index and the tunnel surrounding rock deformation maintain a good fitting relationship, with a fitting rate of more than 85%. After the improved PSO‐BP algorithm is used to calculate and fit the four parameters of torque, rotational speed, thrust, and surrounding rock drivable coefficient during TBM mechanical construction, the fitting accuracy is higher than 94%. (3) At the same time, according to the analysis of the fitting results, the fitting effect of TBM mechanical thrust is the best among the four‐parameter algorithm analysis fitting degrees, which is 99.3%. (4) The measured points are evenly distributed near the surrounding rock deformation surface calculated by the improved PSO‐BP algorithm, and the fitting variance is 0.972. With the tunnel excavation, the settlement deformation of surrounding rock first increases rapidly, and then, the settlement deformation enters a slow growth stage and finally reaches a relatively stable state. (5) After the jam occurs, the tunnel is expanded from the telescopic shield or tail shield to the outer side of the shield to reduce the contact area between the shield and the surrounding rock to reduce the extrusion of the surrounding rock pressure on the shield.

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications.An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R² value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.

This study presents a fire risk assessment of traditional wooden dwellings in Southern Hunan, focusing on Zhoujia Compound—a nationally protected cultural heritage site. By applying Pyrosim fire simulation software, we modeled fire spread, smoke dispersion, and temperature variation under localized architectural and environmental conditions. The simulations, informed by real-time wind speed monitoring, revealed that key fire risks stem from open flame activities during festivals, charcoal heating, and inadequate electrical wiring. Structural features such as interconnected wooden beams and open courtyards exacerbate fire spread. The results identified high-risk zones and demonstrated that wind speed and building orientation significantly affect fire dynamics. Based on these findings, we propose targeted fire prevention strategies, including fire-retardant treatments, improved compartmentalization, and community-level fire education. This research offers a novel, simulation-based approach to improving fire safety in traditional villages, contributing to both cultural heritage protection and rural fire risk mitigation.

Trajectory planning for the center of mass (CoM) constitutes a fundamental challenge in bipedal locomotion. Conventional approaches, which predominantly rely on experimental measurements or simplified analytical models, are often constrained by discretization errors, parameter uncertainty, and prohibitive experimental costs. To address these limitations, this paper proposes a numerical framework for generating CoM trajectories directly from ground reaction force (GRF) dynamics. The methodology first determines key gait event positions and formulates a coupled system of governing equations that integrate force and motion. The method then establishes a unified spatio-temporal-GRF coordinate system and incorporates boundary conditions, along with load-sharing mechanisms between the trailing and leading legs, to derive the force and geometric constraint equations. The resulting reference trajectory is represented using Fourier coefficients, which are determined via an Adam-based constrained residual minimization solver. This Fourier-based representation offers notable advantages, including a compact parameter set, high computational efficiency, and the capacity to capture the essential kinematic features of bipedal walking. To validate the proposed reference trajectory, a sliding mode control (SMC) framework is employed for bipedal walking control, in which the walker successfully tracks the reference trajectory with stable gait execution. The results confirm that the generated trajectory faithfully reproduces the characteristic CoM response of bipedal walking, thereby providing a high-fidelity reference for robust motion control. Furthermore, a systematic analysis of the control parameters is conducted to evaluate their influence on walking stability. This work offers a new paradigm that synergistically combines physical interpretability with computational efficiency, holding significant promise for applications in humanoid robot motion control and biomechanical analysis.