This study conducted load-bearing capacity tests to quantitatively analyze the impact of permafrost degradation on the vertical load-bearing capacity of railway bridge pile foundations. Meanwhile, a prediction model vertical load-bearing capacity for pile foundations considering permafrost degradation was developed and validated through these tests. The findings indicate that the permafrost degradation significantly influences both the failure patterns of the pile foundation and the surrounding soil. With the aggravation of permafrost degradation, damage to the pile foundation and the surrounding soil becomes more pronounced. Furthermore, permafrost degradation aggravates, both the vertical ultimate bearing capacity and maximum side friction resistance of pile foundations exhibit a significant downward trend. Under unfrozen soil conditions, the vertical ultimate bearing capacity of pile foundations is reduced to 20.1 % compared to when the permafrost thickness 160 cm, while the maximum side friction resistance drops to 13.2 %. However, permafrost degradation has minimal impact on the maximum end bearing capacity of pile foundations. Nevertheless, as permafrost degradation aggravates, the proportion of the maximum end bearing capacity attributed to pile foundations increases. Moreover, the rebound rate of pile foundations decreases with decreasing permafrost thickness. Finally, the results confirm that the proposed prediction model can demonstrates a satisfactory level of accuracy in forecasting the impact of permafrost degradation on the vertical load-bearing capacity of pile foundations.

The dynamic response of piles is a fundamental issue that significantly affects the performance of pile foundations under vertical cyclic loading, yet it has been insufficiently explored due to the limitations of experimental methods. To address this gap, a hydraulic loading device was developed for centrifuge tests, capable of applying loads up to 2.5 kN and 360 Hz. This device could simulate the primary loading conditions encountered in engineering applications, such as those in transportation and power machinery, even after the amplification of the dynamic frequency for centrifuge tests. Furthermore, a design approach for model piles that considers stress wave propagation in pile body and pile-soil dynamic interaction was proposed. Based on the device and approach, centrifuge comparison tests were conducted at 20 g and 30 g, which correspond to the same prototype. The preliminary results confirmed static similarity with only a 1.25% deviation in ultimate bearing capacities at the prototype scale. Cyclic loading tests, conducted under various loading conditions that were identical at the prototype scale, indicated that dynamic displacement, cumulative settlement, and axial forces at different burial depths adhered the dynamic similarity of centrifuge tests. These visible phenomena effectively indicate the rationality of centrifuge tests in studying pile-soil interaction and provide a benchmark for using centrifuge tests to investigate soil-structure dynamic interactions.

Determining earth pressure on jacked pipes is essential for ensuring lining safety and calculating jacking force, especially for deep-buried pipes. To better reflect the soil arching effect resulting from the excavation of rectangular jacked pipes and the distribution of the earth pressure on jacked pipes, we present an analytical solution for predicting the vertical earth pressure on deep-buried rectangular pipe jacking tunnels, incorporating the tunnelling-induced ground loss distribution. Our proposed analytical model consists of the upper multi-layer parabolic soil arch and the lower friction arch. The key parameters (i.e., width and height of friction arch B and height of parabolic soil arch H1) are determined according to the existing research, and an analytical solution for Kl is derived based on the distribution characteristics of the principal stress rotation angle. With consideration for the transition effect of the mechanical characteristics of the parabolic arch zone, an analytical solution for soil load transfer is derived. The prediction results of our analytical solution are compared with tests and simulation results to validate the effectiveness of the proposed analytical solution. Finally, the effects of different parameters on the soil pressure are discussed.

Introduction The formation of ruts induced by vehicle traffic poses a significant challenge for agricultural soils due to soil compaction both at the surface and deeper layers. This phenomenon compromises vehicle performance increases energy consumption, and leads to long-term environmental degradation, such as soil erosion and fertility reduction. To enhance vehicle performance and reduce soil damage, it is crucial to accurately predict how factors such as vehicle speed, vertical load, and the number of passes impact rut depth. The findings of this study hold significant practical implications, facilitating the development for the creation of more efficient agricultural practices, while simultaneously minimizing environmental impact. The complexity of these interactions necessitates using machine learning models, especially artificial neural networks (ANNs), to predict rut depth based on input parameters. In this study, two machine learning models, namely the multilayer perceptron (MLP) and the radial basis function (RBF) networks, were employed to predict rut depth. Materials and Methods Experiments were conducted using a soil bin that allows for precise control of independent parameters, measuring 24 meters in length, 2 meters in width, and 0.8 meters in depth. The soil used was agricultural soil, comprising 35% sand, 22% silt, and 43% clay, with a moisture content of 8%. The tests included three independent parameters: vertical load (2, 3, and 4 kN), forward speed (1, 2, and 3 km h(-1)), and number of wheel passes (up to 15). Two types of traction devices, including a rubber wheel and a track wheel, were tested. A caliper was used to measure the rut depth after each pass with an accuracy of 0.02 mm. The data collected from soil bin tests were used to train neural network models in MATLAB 2021-b software. The MLP model had a topology with two hidden layers and included three inputs and one output. In the RBF model, the network topology had a single hidden layer. The trial-and-error method was used to adjust the hyperparameters of the neural networks, including the number of neurons in the hidden layers, the learning rate, and momentum for the MLP network, as well as the spread rate and regularization rate for the RBF network. Results and Discussion Experimental data confirmed that increasing the vertical load and the number of passes resulted in deeper ruts. Conversely, an increase in speed led to a reduction in rut depth, particularly during the initial pass. Both artificial neural network (ANN) models accurately predicted rut depth, with the multilayer perceptron (MLP) neural network outperforming the radial basis function (RBF) neural network. Specifically, the root mean square error (RMSE) for the optimal MLP model, which utilized a learning rate of 0.001 and a momentum of 0.67, was 0.10. In contrast, the optimal RBF model, with an expansion rate of 0.23456, yielded an RMSE of 0.12. The findings indicate that the MLP artificial neural network model surpasses the RBF neural network model in terms of accuracy and overall performance. However, the RBF neural network exhibits a faster response time, making it particularly suitable for real-time applications. Conclusion This study demonstrates the efficacy of machine learning techniques, particularly artificial neural networks (ANNs), in predicting rut depth caused by off-road vehicle traffic. Both multilayer perceptron (MLP) and radial basis function (RBF) neural networks exhibited robust predictive capabilities, with the MLP model providing slightly superior accuracy and the RBF model offering better computational efficiency. These findings highlight the potential of machine learning in modeling complex interactions between soil and vehicles, which can enhance vehicle performance, mitigate soil erosion, and guide the design of off-road vehicles. Future research directions could include investigating additional soil parameters, various vehicle configurations, and the real-world implementation of autonomous off-road vehicles to promote more environmentally sustainable operations.

The tripod foundation (TF) is a prevalent foundation configuration in contemporary engineering practices. In comparison to a single pile, TF comprised interconnected individual piles, resulting in enhanced bearing capacity and stability. A physical model test was conducted within a sandy soil foundation, systematically varying the length-to-diameter ratio of the TF. The investigation aimed to comprehend the impact of altering the height of the central bucket on the historical horizontal bearing capacity of the foundation in saturated sand. Additionally, the study scrutinized the historical consequences of soil pressure and pore water pressure surrounding the bucket throughout the loading process. The historical findings revealed a significant enhancement in the horizontal bearing capacity of the TF under undrained conditions. When subjected to a historical horizontal loading angle of 0 degrees for a single pile, the multi-bucket foundation exhibited superior historical bearing capacity compared to a single-pile foundation experiencing a historical loading angle of 180 degrees under pulling conditions. With each historical increment in bucket height from 150 mm to 350 mm in 100 mm intervals, the historical horizontal bearing capacity of the TF exhibited an approximately 75% increase relative to the 150 mm bucket height, indicating a proportional relationship. Importantly, the historical internal pore water pressure within the bucket foundation remained unaffected by drainage conditions during loading. Conversely, undrained conditions led to a historical elevation in pore water pressure at the lower side of the pressure bucket. Consequently, in practical engineering applications, the optimization of the historical bearing efficacy of the TF necessitated the historical closure of the valve atop the foundation to sustain internal negative pressure within the bucket. This historical measure served to augment the historical horizontal bearing capacity. Simultaneously, historical external loads, such as wind, waves, and currents, were directed towards any individual bucket within the TF for optimal historical performance.

The mechanical response of energy pile groups in layered cross-anisotropic soils under vertical loadings is studied with the aid of the coupled finite element method- boundary element method (FEM-BEM). The single energy pile is simulated based on the finite element theory, which then is extended to energy pile groups. The global flexibility matrix for soils is obtained by considering the coupling effects of vertical and thermal loadings. The coupled FEM-BEM equation for the interaction between energy pile groups and soils is derived based on the displacement compatibility condition at the pile-soil interface. According to the displacement coordination condition and force balance in the rigid cap, the displacement of the cap and axial forces of pile groups can be solved. The presented theory is validated by comparing the calculated results with numerical simulations and field test results in existing literature. Finally, effects of the thermal loading, pile-soil stiffness ratio, pile spacing, cross-anisotropy of Young's modulus and the stratification are discussed.