Subgrade Settlement Research Articles

Deflection characteristics of semi-rigid base asphalt pavement under traffic speed deflectometer loading

The three-dimensional viscoelastic finite element simulation and on-site validation was combined, so as to study the characteristics of the traffic speed deflection slope curve of the semi-rigid base asphalt pavement. Results show that there is an inert point in the deflection slope curve when only the surface or base structural state changes. With the loading center approaching transverse crack, the ‘three extreme values’ attribute transitions gradually to ‘single extreme value’. For the surface-base penetrating longitudinal crack caused by uneven subgrade settlement, the peak value of deflection slope curve increases significantly as the crack approaches the loading center, reaching an order of magnitude difference, and the attribute transforms from ‘three extreme values’ to ‘single extreme value’. Indicators characterizing the layered structural state were proposed, and the indicator change rules and applicability for the pavement with different surface and base layer thicknesses, under different deflectometer speeds, with or without cracks, were discussed.

Physics-Informed Neural Network (PINN) model for predicting subgrade settlement induced by shield tunnelling beneath an existing railway subgrade

For the shield tunnelling beneath an existing railway subgrade, factors such as disturbance during shield tunnel excavation can cause settlements of the existing subgrade above. Traditional prediction methods for subgrade settlement may not fully reflect the construction site conditions. Recently, machine learning has been widely applied due to its fast computation speed and accurate results. However, data-driven machine learning is considered a “black-box” algorithm, lacking a mechanical theory framework and having weak generalization capabilities. To address the limitations of machine learning and enhance the efficiency and accuracy of subgrade settlement prediction during shield tunnel construction, this paper proposes a prediction model based on a physics-informed neural network (PINN). Firstly, by integrating the non-uniform displacement mode function of the tunnel, the physical equations are constructed for the shield tunnel excavation process. Subsequently, the physical equations are coupled with the machine learning framework to develop a predictive model for shield tunnelling beneath an existing railway subgrade based on the PINN model. The performance of the proposed PINN model is evaluated through the application of two engineering cases and compared with traditional numerical simulations and data-driven machine learning. The results indicate that the proposed model can not only predict the settlement of the existing railway subgrade under scant and noisy monitoring data but also have accuracy in the inversion of tunnel displacement mode parameters. Finally, parametric analyses are conducted to reveal the influence of critical variables on the prediction performance of the PINN model.

Study on settlement deformation law of new and old subgrade of expressway reconstruction and expansion based on CPTU

In the reconstruction and expansion of expressways in soft soil areas, controlling the differential settlement between the new and existing subgrades is of vital importance. To investigate the settlement and deformation characteristics of both the new and existing subgrades, piezocone penetration test (CPTU) and dissipation tests were conducted on these subgrades. The CPTU dissipation data was utilized to determine the soil’s degree of consolidation, and settlement calculations for the new and existing road subgrades were based on the CPTU test results. Subsequently, a finite element model was developed using the CPTU test findings to analyze the horizontal displacements, vertical settlements, and differential settlements of the new and existing subgrades before and after the reconstruction and expansion. Based on the measured settlement results, the new and old subgrade settlement calculation results are verified. The outcomes revealed that the degree of consolidation for the existing road subgrade of the Lianhuai Expressway ranged between 42 % and 96 %. The maximum horizontal displacement of the subgrade pre- and post-expansion occurred at the slope toe. Before expansion, the maximum vertical settlement was observed along the road’s centerline, while after expansion, it was located in the centerline of the widened section. The maximum additional settlement amounted to 274.77 mm. During the new road construction phase, the differential settlement between the new and existing road subgrades increased rapidly over time, peaking at its maximum value. However, during the operational phase of the new road, this differential settlement tapered off as time progressed.

Numerical analysis of the effects of subgrade settlement on top-down cracking in epoxy asphalt pavement

Numerical analysis of the effects of subgrade settlement on top-down cracking in epoxy asphalt pavement

Road Site Inspection and Measures to Avoid Further Deformation

Strength and stability of the roadbed and engineering structures in permafrost conditions and deep seasonal freezing of soils always remain complex scientific, technical and technological problem in construction and reconstruction of roads in northern regions. Particularly important is the spread of subgrade subsidence during construction and further operation and maintenance of roads in these regions. The road destruction is mostly caused by both global warming and geological and hydrogeological conditions.To prevent the deformation development, it is necessary to provide a number of special engineering measures to prevent thawing of permafrost soils or strengthen supra-permafrost layers of foundations. The purpose of the work is to study methods for eliminating deformation of the road bed in permafrost and choosing the main method for eliminating subsidence of the road.The article discusses several ways to solve the problem of eliminating subsidence on of the federal highway. Options for reconstruction of the roadbed are considered. These are different methods of thermal stabilization, i.e., horizontal (sloping) thermal stabilization system with inclined thermal stabilizers, temporary vertical thermal stabilizers for pre-construction freezing of foundation soils, and hybrid thermal stabilization system (horizontal and vertical thermal stabilizer in one product). A method is proposed for stabilizing the road base by installing soil-cement piles. The main advantages of and disadvantages the methods are noted and cost assessment is made. A conclusion is drawn on the effectiveness of different methods to restore the subgrade structure in order to ensure the road base stability. The main method is elimination of subsidence by stabilizing the road base with installation of soil-cement piles.

Impact of surface temperature changes on subgrade settlement of permafrost regions railway: a case study of the Qinghai-Tibet railway, China

ABSTRACT Parts of sections in permafrost regions of the Qinghai-Tibet Railway (QTR) have been deformed due to permafrost degradation, the global warming and human activities. Therefore, monitoring the subgrade settlement of the QTR in permafrost regions has been a major working in QTR maintenance. For this reason, this paper investigates the subsidence characteristics and derivative mechanisms of railway subgrades in the permafrost regions of the QTR. Additionally, it analyses the impact mechanisms of surface temperature changes on the subsidence of permafrost subgrades. Building on this investigation, a C-I-GWO-BP neural network prediction model was developed to forecast subgrade settlement in permafrost regions. The findings reveal a substantial correlation (R2 = 0.834) between the deformation of railway subgrades in permafrost regions and changes in surface temperature. Meanwhile，with the intensification of global warming and human activities, the subsidence of permafrost subgrade has shown an increasing trend year by year. The primary areas of deformation are predominantly concentrated in ice-rich permafrost regions, including rivers, lakes, and snowmelt runoff areas. Additionally, when compared to traditional BP neural network models, the C-I-GWO-BP neural network prediction model proposed in this article showcases diminished prediction errors and heightened accuracy in identifying optimal weights and thresholds. This model offers technical support for the efficient, accurate, and automated prediction of railway subgrade subsidence in permafrost regions.

Effects of uneven subgrade settlement on the dynamics of train-CRTS III slab track nonlinear coupling system

Effects of uneven subgrade settlement on the dynamics of train-CRTS III slab track nonlinear coupling system

Damage characteristics and mechanical properties of CRTS III track structure in curved sections under longitudinal uneven subgrade settlement

This paper first presents a CRTS III track structure (TS) model specifically designed for curved sections, highlighting its adjustable superelevations feature that enhances adaptability to various high-speed railway curve profiles. The model considers the distribution of steel bars within the TS, concrete damaged plasticity, as well as contact nonlinearity. Afterward, the intricate impacts of longitudinal uneven subgrade settlement (LUSS) position (7 positions), amplitude (5–30 mm), wavelength (5–30 m), and TS superelevation (15–175 mm) on structural damage, stress, deformation, and interlayer gap are studied. The findings reveal that: 1) Under different LUSS positions, the concrete base (CB) experiences the most severe damage, particularly on its thick side. For the TS with 175 mm superelevation, in the wavelength range of 10–20 m, the maximum damage value of CB exceeds 0.9 when LUSS amplitude reaches 20 mm. With the movement of LUSS position, the main damage position of CB changes obviously. When the concrete stress reaches the tensile strength, the steel bars located in adjacent positions progressively assume the primary role in load-bearing. 2) The influence of LUSS on TS deformation and interlayer gap is inevitably associated with the distance between settlement center and expansion joints. Interlayer gaps vary among structural components, which are primarily distributed near settlement center, expansion joints, and settlement edges. 3) As the amplitude increases, both CB and self-compacting concrete layer (SCCL) damage are aggravated. Increasing wavelength initially causes damage to increase, then decrease, and 10–20 m is the most unfavorable wavelength range for most cases. 4) The effect of superelevation on TS damage is complex, varying with structural components and settlement characteristics. In most cases, as superelevation increases, the damage to CB increases, whereas the deformation decreases.

Permeability and Disintegration Characteristics of Loess Solidified by Guar Gum and Basalt Fiber.

Loess has the characteristics of loose, large pore ratio, and strong water sensitivity. Once it encounters water, its structure is damaged easily and its strength is degraded, causing a degree of subgrade settlement. The water sensitivity of loess can be evaluated by permeability and disintegration tests. This study analyzes the effects of guar gum content, basalt fiber content, and basalt fiber length on the permeability and disintegration characteristics of solidified loess. The microstructure of loess was studied through scanning electron microscopy (SEM) testing, revealing the synergistic solidification mechanism of guar gum and basalt fibers. A permeability model was established through regression analysis with guar gum content, confining pressure, basalt fiber content, and length. The research results indicate that the addition of guar gum reduces the permeability of solidified loess, the addition of fiber improves the overall strength, and the addition of guar gum and basalt fiber improves the disintegration resistance. When the guar gum content is 1.00%, the permeability coefficient and disintegration rate of solidified soil are reduced by 50.50% and 94.10%, respectively. When the guar gum content is 1.00%, the basalt fiber length is 12 mm, and the fiber content is 1.00%, the permeability of the solidified soil decreases by 31.9%, and the disintegration rate is 4.80%. The permeability model has a good fitting effect and is suitable for predicting the permeability of loess reinforced with guar gum and basalt fiber composite. This research is of vital theoretical worth and great scientific significance for guidelines on practicing loess solidification engineering.

A new method for predicting consolidation settlements of soft ground based on monitoring results

Excessive settlement or differential settlement of subgrade will lead to the deterioration of line operational conditions, the reduction of passenger comfort, and even endanger the safety of traffic. Therefore, it is of great significance to study the settlement prediction of subgrade. In order to predict the settlement of foundation under the next level of loading earlier during the embankment construction process, a new method of predicting settlement of soft soil subgrade is proposed. Firstly, based on monitoring results of soft soil foundation, the consolidation parameters of soil layer are back-calculated according to the three-point method. Then, combined with the theory of the consolidation degree of graded loading, the formula that can predict settlement under different loading conditions are derived. Eventually, the practical application of the method is verified by the prediction and comparative analysis of measured settlements based on engineering examples. The result of research shows that the method can predict the foundation settlement after loading during construction of engineering fill. This method has obvious advantages over the traditional curve fitting method and can guide the actual engineering construction.

A Railway Roadbed Deformation Monitoring System Using Deep Learning and AI Intelligent Technology

The author has designed an automated monitoring system for settlement and deformation of high-speed railway subgrade. Firstly, an automated monitoring system is designed based on sensors, data collection and transmission, client tracking and querying, monitoring result processing, automated warning, manual monitoring data analysis, monitoring data analysis and evaluation. Secondly, the system software and hardware are designed, the author calculated and analyzed engineering examples from the perspective of practical applications using artificial neural network methods, obtained corresponding deformation analysis models, and predicted deformation. Finally, the practical application of the system was analyzed for its effectiveness. The experimental results indicate that, the BP artificial neural network method is used to model and predict the Deformation monitoring data. On the premise of 20 learning samples and 4 prediction samples, the Root-mean-square deviation of the prediction is 0.32mm, which shows that the deformation prediction using BP model is feasible and effective in a certain precision range. It has been proven that the application of artificial neural network methods in monitoring and prediction of practical engineering has certain practical significance.

Intelligent identification of differential subgrade settlement of ballastless track system based on vehicle dynamic responses and 1D-CNN approach

The millimeter-scale deformation control standards of high-speed railways make the monitoring and identification of subgrade settlement a key technology, especially for ballastless track systems. Addressing the uncoordinated deformation between track and subgrade caused by high track stiffness, this paper proposes an intelligent identification method for subgrade settlement based on vehicle vibration signals and deep-learning technology. Firstly, a high-speed vehicle-ballastless track-subgrade coupled dynamics model considering the track-subgrade nonlinear contact state is utilized, to obtain various vibration responses of the vehicle subsystem due to differential subgrade settlement. After data preprocessing, a multi-channel one-dimensional convolutional neural network (1D-CNN) is established to extract features from the vehicle vertical acceleration dataset and output subgrade settlement status automatically. Finally, the robustness of the model is verified using Gaussian white noise. The research indicates that the vertical accelerations of the car body and bogie frame contain dynamic characteristics caused by medium-long wavelength settlements, while the vertical acceleration of the wheelset is more sensitive to short-wavelength settlements. It is feasible to identify the degree of subgrade settlement using car body, bogie frame and wheelset vertical accelerations as parameterized multi-source input of the 1D-CNN network. The three-channel input model significantly improves the identification accuracy and demonstrates the robustness against noise interference compared to the single-input and dual-input models. This method can effectively enhance the identification capability of ‘concealed’ subgrade settlements in high-speed railways, offering a scientific methodology for status maintenance of ballastless track lines.

A comprehensive evaluation on particles breakage characteristics of waste argillaceous gangue as subgrade filler

Reutilising waste argillaceous gangue (AG) in road engineering is crucial to reducing its environmental pollution. To effectively control settlement and long-term performance of subgrade filled with AG, the effects of humidification and loading on the particle crushing evolution of AG were investigated. The effects of dry-wet cycles, compaction work, and initial coarse grain content (CGC) on the breakage characteristics were evaluated. The relationship among fractal dimension D and compaction work, initial CGC, and breakage index Bg was established. The results show that the initial CGC and compactness are essential factors affecting the breakage characteristics of AG. It is recommended that the CGC be controlled at 50% when AG is used as subgrade filler. The fractal dimension increases logarithmically with the increase of compaction work and decreases linearly with the rise of CGC. Fractal dimensions D and Bg can effectively evaluate the breaking process of AG and the gradation after compaction.

A numerical method for calculating the long-term settlement of three-dimensional subgrade under numerous traffic loads

Settlement problems caused by various traffic loads significantly impact infrastructure sustainability. Accurately predicting the long-term settlement of subgrade is crucial for improving infrastructure resilience. This study determined the element strain of soil using an explicit strain model, based on the bounding surface model and associated flow rules. Using the method of generalized shear strain equivalent, a cyclic cumulative factor is proposed for determining the magnitude of cumulative strain increment. The direction of plastic strain increment was determined using the bounding surface model combined with associated flow rules. A cumulative strain calculation method integrating the bounding surface and explicit strain models under general stress states was established. This computational method, combined with equivalent finite elements, can be used for calculating the long-term settlement of three-dimensional subgrade under numerous cyclic loads at general stress states through a user-defined subroutine provided by the commercial finite element modeling (FEM) software. The reliability of the method was verified through unit tests.

Synthesising microstructures of a partially frozen salty sand using voxel-based 3D generative adversarial networks

Generating synthetic material microstructures is essential in the numerical modelling of geomaterials. The occurrence of permafrost and saline groundwater overlapping regions is crucial in a series of phenomena, such as carbon emissions and subgrade settlements. The microstructure of geomaterials in these regions is of particular complexity because of the multiphase nature with salty water and ice crystals. This complexity renders existing generative models ineffective in synthesising their microstructures. Traditional generative methods are limited in the sense that require prior knowledge of material descriptors. Recently, machine learning generative models achieved unprecedented levels of performance and realism, but still lack the means to assess posterior error. This work aims to bridge the gap between traditional methods and deep learning generative models by assessing posterior image quality in the latter. A 3D Generative Adversarial Network (GAN) model is trained with image samples from an X-ray CT of a partially frozen salty sand. The metrics retained to assess posterior quality are particle fabric (shape parameter and anisotropy) and homogenised elastic coefficients obtained with Finite Element Method (FEM) simulations. A hyperparametric study on batch size and latent dimension serves to select the best configuration based on particle fabric. FEM simulations determine the deviation in the generated images elastic coefficients being 7.55% on average with respect to real samples. With 803 voxels, generated images are the largest up-to-date in a three-phase material, allowing to reach REV criteria. Applications range from the generation of microscales in double-scale models to the calibration of image processing tools.

Settlement Control Standards of Construction and Expansion for Mountainous Highway Roadbeds

This article analyzes the differential settlement of new and old roadbeds after widening and its characteristics based on a highway reconstruction and expansion project case study. The research proposes a subgrade settlement control standard that states that the maximum differential settlement value should be less than 5 cm when the embankment fill height exceeds 20 m. Similarly, the maximum differential settlement value should be less than 10 cm when the embankment fill height does not exceed 20 m. The findings of the study can provide a useful reference for the design of roadbed widening in highway reconstruction and expansion projects.

A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China’s High-Speed Railways

China has the world’s longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.

Field measurement and CPT-based settlement prediction of existing railway subgrade reinforced by battered helical piles with grouted shafts

In order to ensure the normal operation of the existing freight railway, it is difficult to adopt the common reinforcement measures (e.g., jet grouting or deep mixing) to meet the requirements. Reinforcement of existing railway subgrade by battered helical piles can meet the normal operation requirements of existing railways. However, there is no report on the research on the reinforcement of existing railway subgrade with battered helical piles. This paper reports on a field case study of railway subgrade improved by battered helical piles with grouted shafts (BHPGS) to mitigate the ongoing settlement due to temporary backfilling of subgrade without meeting the compaction requirement after heavy rain, which can be potentially harmful to the long-term operation of the railway. The measured coefficients of subgrade reaction (K30) of the 3 locations after BHPGS installation increased by 48 %∼166 % compared with the installation before. The long-term settlement of the railway subgrade reinforced with BHPGS was monitored by hydrostatic level and gradually stabilized after an adjustment period of about half a year, with the maximum settlement of 2.76 mm located at the center of the subgrade reinforcement section. In addition, a simplified method of calculating settlement based on CPT for the railway subgrade reinforced with BHPGS is proposed, with the calculated value of 2.99 mm in good agreement with the measured 2.76 mm.

Review of Subgrade Soil Stabilised with Natural and Synthetic Fibres

Subgrade soil is an essential component in the design of road structures as it provides lateral support to the roadway. One of the main reasons for pavement failure is subgrade settlement, which leads to a loss of subgrade strength. If the mechanical properties of subsoils are lower than required, a soil stabilisation method may be an option to improve the soil properties of the weak subsoil. Soil stabilisation is one of the techniques for improving poor subsoil, which results in significant improvement in tensile strength, shear strength and bearing capacity of subsoil. Soil stabilisation can be broadly divided into four types: thermal, electrical, mechanical, and chemical. The most common method of improving the physical and mechanical properties of soils is stabilisation with binders such as cement and lime. However, soil stabilisation with conventional methods using cement and lime has become uneconomical in recent years, so an alternative such as fibres may be sought. This review provides a comprehensive comparison of the effectiveness of natural fibres and synthetic fibres in stabilising subgrade soils.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement. The main objective of this research is to analyze the macro- and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles. First, an improved Viola–Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler. Shape indexes of 2D subgrade filler are then computed and statistically analyzed. Finally, numerical simulations are performed to quantitatively investigate the effects of the aspect ratio (AR) and interparticle friction coefficient (μ) on the macro- and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method (DEM). The results show that with the increasing AR, the coarse particles are narrower, leading to the increasing movement of fine particles during compaction, which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles. Moreover, the average displacement of particles is strongly influenced by the AR, indicating that their occlusion under power relies on particle shapes. The displacement and velocity of fine particles are much greater than those of the coarse particles, which shows that compaction is primarily a migration of fine particles. Under the cyclic load, the interparticle friction coefficient μ has little effect on the internal structure of the sample; under the quasi-static loads, however, the increase in μ will lead to a significant increase in the porosity of the sample. This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.