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Through research on the mechanism of vibration compaction, a “Roller-Subgrade” coupling system model for roadbed vibration compaction construction was established, and dynamic analysis and calculations were performed on this model. Meanwhile, the MATLAB software was utilized to conduct simulation studies on this coupling model, with detailed elaboration on the determination of basic simulation parameters and the design of corresponding simulation programs. Through simulation analysis, the displacement and velocity response of the vibratory wheel of the vibratory roller under fixed mechanical and soil parameters were explored.

To mitigate the impact of subgrade settlement on pavement in permafrost regions, an innovative prefabricated pavement structure system composed of longitudinal and transverse beams combined with concrete slabs was proposed and designed. The mechanical performance indicators under the most unfavorable conditions were evaluated using ANSYS finite element software. The direct shooting method was employed to systematically optimize the dimensions of the prefabricated pavement structure components. The results indicate that the prefabricated pavement structure exhibits excellent deformation resistance during subgrade settlement. The direct shooting method optimized the prefabricated pavement structure, ensuring structural safety while reducing weight and minimizing construction costs. Furthermore, a correlation analysis was conducted on the dimensions of the structural components affecting maximum deformation, maximum equivalent stress, and overall structural volume. The analysis identified the significant influence of the upper flange plate of the steel beam and the bottom steel plate on the overall mechanical performance and steel consumption of the pavement structure.

This paper introduces a new approach to managing subgrade settlement through finite element inverse analysis, focusing on creep effects in soft soil. Using MATLAB to connect Abaqus with an Improved Whale Optimisation Algorithm (IWOA) allows researchers to effectively extract calculation parameters for soft soil layers. The simulation of settlement deformation in subgrade soil, with due consideration of the creep effect, is achieved through the use of the modified Drucker-Prager yield criterion model and the time hardening creep law coupling model. The innovative approach enhances the Whale Optimisation Algorithm's ability to avoid local extrema stagnation and improves the overall optimisation process. The IWOA was evaluated on 16 benchmark functions and compared with six other Al algorithms. lts effectiveness in global function performance was analysed using the Friedman test on the mean values from 30 simulation results. The evidence suggests that IWOA performs exceptionally well. Thereafter, the proposed method's effectiveness was confirmed by developing a finite element inversion model for a highway subgrade on soft soil. The model was used to simulate subgrade deformation throughout construction, allowing for a comparison with experimental data to validate its reliability. The outcomes demonstrate a clear correlation between subgrade deformation from inversion analysis and real-world data. It is projected that, for the next 10 years, the post-construction settlement will be limited to a maximum variation of 10 cm. These projections have the potential to offer valuable theoretical insights for improving the efficiency of highway management and maintenance.

Road pavement thickness and their depth of construction take a chunk of the overall cost of road construction. This has called for a need for reduced road pavement thickness by improving the engineering properties of subgrade such as the California bearing ratio (CBR). The CBR of road subgrade has been a major determining factor for road pavement thickness, and expansive subgrades generally have a low CBR, resulting in major road defects. In this study, road pavement thickness and construction depth optimization were conducted using the CBR values achieved in this study. Additives proportions of 8% lime and 20% cement were used in expansive subgrade to improve their engineering properties, making them suitable for use in road construction. The study investigated the characteristics, mineral structure, Atterberg limit, compaction, CBR, swell and microstructural properties of expansive subgrade. The results show a reduction in road pavement thickness and a construction depth with an increase in CBR value. All CBR values for treated samples were above 2%, making them usable in road construction. A reduction in swell potential up to 0.04% was observed for treated expansive subgrade. The study concluded that pavement thickness and construction depth can be reduced by enhancing subgrade materials and using cement and lime as binders.

Construction and demolition (C&D) materials obtained from the demolition of buildings are proven to be qualified and sustainable subgrade fillers. The permanent deformation response of subgrade C&D materials under different moisture contents, degrees of compaction, deviator stresses, and confining pressures was revealed by carrying out dynamic triaxial texts. Then, using a four-factor and three-level orthogonal test and by calculating the Gray correlation degree of each factor, the influence degree of each factor on the permanent deformation was determined. The results indicated that two different response types of the permanent deformation of subgrade C&D materials, plastic shakedown and plastic creep, were identified as reason behind the increase in stress levels. Also, according to the Gray correlation analysis results, the permanent deformation of highway subgrade filled with C&D materials is influenced by the deviator stress most significantly, followed by moisture content, degree of compaction, and confining pressure. Finally, a permanent deformation prediction model about this kind of subgrade filler with a reasonable prediction accuracy was proposed.

The utilization of industrial by-products as stabilizers is gaining attention from the sustainability perspective. Along these lines, granite sand (GS) and calcium lignosulfonate (CLS) are used as alternatives to traditional stabilizers for cohesive soil (clay). The unsoaked California Bearing Ratio (CBR) was taken as a performance indicator (as a subgrade material for low-volume roads). A series of tests were performed by varying the dosages of GS (30%, 40%, and 50%) and CLS (0.5%, 1%, 1.5%, and 2%) for different curing periods (0, 7, and 28 days). This study revealed that the optimal dosages of granite sand (GS) are 35%, 34%, 33%, and 32% for dosages of calcium lignosulfonate (CLS) of 0.5%, 1.0%, 1.5%, and 2.0%, respectively. These values are needed to maintain a reliability index greater than or equal to 3.0 when the coefficient of variation (COV) of the minimum specified value of the CBR is 20% for a 28-day curing period. The proposed RBDO (reliability-based design optimization) presents an optimal design methodology for designing low-volume roads when GS and CLS are blended for clay soils. The optimal mix, i.e., 70% clay blended with 30% GS and 0.5% CLS (exhibiting the highest CBR value) is considered an appropriate dosage for the pavement subgrade material. Carbon footprint analysis (CFA) was performed on a typical pavement section according to Indian Road Congress recommendations. It is observed that the use of GS and CLS as stabilizers of clay reduces the carbon energy by 97.52% and 98.53% over the traditional stabilizers lime and cement at 6% and 4% dosages, respectively.

The railway subgrades in the sandy areas act as an obstacle interfering wind-blown sand, causing sand erosion and sedimentation, which can disrupt the safe and stable operation of the railway system. Most previous studies mainly focus on the flow field around railway subgrades, however, the real erosion and sedimentation patterns are rarely studied. This study aims to analyze the erosion and sand sedimentation patterns of wind-blown sand over the subgrades with different heights and steel rails using the ratio of the wall shear stress to the critical value of erosion shear stress. Results show that wind erosion near the top of the upwind slope of the embankment and the shoulder on the upwind side are more severe, and the severity increases with an increase in the height of the embankment. With the increase of wind velocity, sand sedimentation both on the windward and leeside of the subgrade decreases and wind erosion by reverse flow occur. This study indicates that railways in sandy areas should be constructed with a moderate subgrade height (4 m).

Existing prediction model for the dynamic resilient modulus of subgrade clay ignores the effects from loading frequency. This study focuses on the frequency dependence of the dynamic resilient modulus of clay subgrades. An experimental study was performed to investigate the dynamic resilient modulus of the clay subgrade material at selected loading frequencies, which corresponds to different vehicle speeds on the road. The frequency dependence of the dynamic resilient modulus of the clay subgrade was significantly affected by the compaction degree and moisture content of the material. In addition, a modified model for predicting the dynamic resilient modulus of a subgrade was proposed by explicitly considering frequency dependence. A preliminary calibration of the model was accomplished, and its validation under various stress states, material conditions, and loading frequencies was found to be promising.

This article presents a comprehensive study on the efficacy of xanthan gum (XG) biopolymer as a green construction material in treating problematic weak subgrade soil (i.e., expansive soil). In this regard, a wide range of geotechnical properties i.e., compaction, unconfined compressive strength (UCS), elastic modulus (E50), energy absorption capacity (Ev), soaked and unsoaked California bearing ratio (CBR), swelling potential, consolidation parameters along with microstructural studies of untreated and treated soils were investigated. The soil was treated with varying percentages of XG (i.e., 0, 0.5, 1.0, 1.5, 2.0, and 5.0%) considering the long-term aging period (i.e., 0, 4, 7, 14, 28, and 60 days). Results showed a slight decrease in the maximum dry density of treated soil with increased optimum moisture content. At an optimum XG content of 1.5%, the strength parameters, i.e., UCS-value, E50, Ev, soaked and unsoaked CBR, were significantly increased by 1.8–9 orders of magnitude, transforming the weak subgrade into a hard-quality subgrade for pavement construction. In addition, compression and rebound indices were significantly reduced by 83 and 82%, while swell percentage and pressure were decreased by 79 and 86%, respectively. The microstructural studies showed the cross-linking and binding of soil grains by cementitious hydrogel, which is responsible for ameliorating geotechnical parameters. Based on the findings, XG biopolymer was found to be a promising green construction material for the amelioration of problematic weak subgrade soil.

Monitoring uneven subgrade settlement in ballastless track systems remains challenging due to the limited accuracy and discontinuous nature of conventional detection methods. In this study, we propose a novel deep learning approach based on a convolutional neural network and long short-term memory (CNN-LSTM) model to accurately identify uneven subgrade settlement by analyzing vehicle-rail dynamic response data. First, a vehicle-track-subgrade coupled model simulates vibration responses under differential settlements. Through sensitivity analysis of vehicle and track dynamics, we identify carbody vertical acceleration, nodding angular velocity, and rail displacement as optimal CNN-LSTM inputs. By leveraging the convolutional neural network (CNN)’s capability to extract spatial features and the long short-term memory (LSTM)’s strength in capturing temporal dependencies, the hybrid network effectively models the relationships between dynamic indicators and subgrade settlement. The results indicate that combined vehicle-rail responses enhances identification, particularly for track-subgrade deformation mismatches. The CNN-LSTM model achieves a detection accuracy of 99.26%, outperforming four benchmark models—backpropagation (BP) neural network, radial basis function (RBF) network, CNN, and LSTM—which validates its robustness and practical effectiveness. This research provides both theoretical insights and practical guidelines for intelligent monitoring of subgrade settlement in ballastless track systems.