Abstract:Progressive toppling of rock slopes with fractured slab structures is quite common in the slopes of hydropower and highway engineering, posing a potential safety risk during both construction and operational phases. The field investigation found that progressive toppling mostly occurs on slopes with concentrated tectonic stress, intense river incision, and high steepness. Typically, these slopes are composed of hard, thinly-layered, and overthrusted rock mass with a fractured slab structure. From a macroscopic point of view, progressive toppling failure is neither a collapse nor a slide but a form of discontinuous deformation. The traditional limit equilibrium method for slope stability analysis is not suitable for evaluating such slopes. In this paper, by taking the progressive toppling of the slopes along the G108 Highway in the Qinling Mountains as an example and considering the uplift of the mountain and river downcutting, a discontinuous deformation analysis (DDA) method was employed to simulate the entire process of progressive toppling, and the mechanisms of its evolution were analyzed. The results show that as the river downcutting progresses, the rock mass experiences interlayer shearing along bedding planes under the influence of gravity, gradually tilting toward the river valley. Once a through-going rupture area develops, it transforms into a landslide failure. For the evaluation of such slopes, a numerical model should be used to simulate the entire deformation failure process. This allows for the estimation of the current state and development trends, providing a basis for reinforcement and management.

Abstract:Existing methods for determining the humidity adjustment factor and dry-wet cycle reduction factor of the resilient modulus of subgrade soil are mostly based on laboratory tests that consume a large amount of manpower and time, and the prediction accuracy is limited due to the constraints of specification value ranges. In order to achieve fast and accurate prediction of these two factors, the effects of stress state, moisture content, and the number of dry-wet cycles on the resilient modulus of subgrade soil were investigated through laboratory dynamic triaxial tests. Based on existing literature, physical parameters, state parameters, and stress parameters of subgrade soil were selected, and an artificial neural network prediction model optimized by a genetic algorithm was developed to enable rapid prediction of the humidity adjustment factor and dry-wet cycle reduction factor of subgrade soil. The results show that moisture content and dry-wet cycles have a significant influence on the resilient modulus of subgrade soil, while the humidity adjustment factor and dry-wet cycle reduction factor show stress dependence. The intelligent prediction model demonstrates high prediction accuracy for both the humidity adjustment factor and the dry-wet cycle reduction factor.

Abstract:The traditional piling methods of precast concrete (PC) piles are difficult to pass through the hard soil layers and tend to induce severe soil squeezing effects. To address this issue, the technology of a post-planted PC pile with prebored grouting was analyzed. Based on the project of the Hefei?Zongyang section of G3W Dezhou?Shangrao Expressway, the bearing characteristics of post-planted PC piles were researched by the load tests of five piles in two different field test sections. The results show that the bearing capacity of the PC piles can be effectively improved by setting the cement mortar coating layer around the PC piles. The settlement of the pile top under the engineering design load is only about 1.96?3.28 mm. By considering the structural characteristics of the composite pile body, the resistance of the interface between the inner PC pile and the outer cement mortar changes from large at both ends to small in the middle, and the resistance of the interface between the cement mortar and the surrounding soil increases with the depth, which indicates that the cement mortar coating layer can effectively transfer the load of the inner PC pile, enlarge the diameter of the post-planted PC pile, and reinforce the pile body.

Abstract:This article aims to investigate the low-temperature crack resistance performance of asphalt mixtures under different aging conditions and loading rates and select appropriate indicators to accurately characterize and quantify their performance. Based on the semi-circular bending test, the influence of different aging degrees (unaged, short-term aged, long-term aged for three days, and long-term aged for five days) and loading rates (50 mm/min, 5 mm/min, 0.5 mm/min, and 0.1 mm/min) on the low-temperature performance of asphalt mixtures was investigated. The evaluation effect and applicable range of five crack resistance performance indicators, namely fracture energy (G_f), stress intensity factor (K_IC), flexibility index (I_FI), crack resistance index (I_CRI), and balanced cracking index (I_BCI), were studied through variance analysis and variability analysis. The results indicate that aging significantly decreases the low-temperature performance of asphalt mixtures. After short-term aging, G_f decreases by approximately 26%, and as the degree of aging deepens, it continues to decrease at an attenuation rate of about 3%. As the loading rate increases, the evaluation effectiveness of I_FI and I_BCI deteriorates. Conducting the semi-circular bending test at a loading rate of 0.1 mm/min provides a more comprehensive evaluation of the low-temperature performance of asphalt mixtures. The results of variance analysis and variability analysis indicate that G_f and I_CRI can significantly characterize the influence of aging and loading rate on the low-temperature performance of asphalt mixtures. The variability of I_FI and I_BCI is significantly higher than that of the other three indicators. Hence, it is recommended to adopt G_f and I_CRI from the semi-circular bending test as more reasonable indicators for evaluating the low-temperature performance of asphalt mixtures.

Abstract:To address the issues of overfitting, low computational speed, and insufficient target information extraction in pavement crack segmentation tasks, this study proposed a Swin-U network model based on the U-Net architecture. The model adopted the Swin-Transformer as the feature extraction module to enhance the model’s fitting capability and enable more accurate crack feature extraction, thereby enhancing segmentation accuracy. Additionally, a stable loss function, Focal Loss, was introduced to improve the accuracy of target segmentation. Experiment results on a proprietary pavement crack dataset show that the Swin-U network model achieves pixel-level segmentation of crack images and significantly outperforms the traditional U-Net. On the test set, it improves the intersection over union and F1 score by 25.00% and 27.61%, respectively. This improved model not only provides more reliable technical support for road maintenance decision-making but also offers a reference for the optimization of pavement crack segmentation methods.