Transportation Geotechnics最新文献

The longitudinal structural resilience performance of shield tunnels is an important concern given the dramatic development of underground systems and the increasing demand for maintenance work. In this paper, a new model using longitudinal relative differential settlement as the index of tunnel structural resilience performance is proposed. The resilience metric (Re) is defined as the ratio of the area integrated by the residual performance during environmental disruptions to the area integrated by the normal performance for the corresponding duration. Then, the proposed resilience analysis model is applied to a well-documented case in Shanghai, where the existing metro tunnel is disrupted by a newly constructed large-diameter shield tunnel undercrossing and subsequently repaired by soil grouting. The variations of tunnel settlement concerning construction parameters and driving distance of the shield machine are analyzed. The performance degradation characteristics of the tunnel during disruption and recovery are effectively captured. The results show that 32.3 % of the performance loss is attributed to the new tunnel undercrossing in the first 38 days. After the completion of the grouting reinforcement, the tunnel performance is improved from 0.677 to 0.868, accounting for approximately 59.1 % of the performance loss during the crossing period. Moreover, the resilience metric (Re) is calculated as 0.764, indicating a high level of resilience for the existing metro tunnel in this case. In addition, other performance indexes based on tunnel longitudinal settlement are discussed, demonstrating the good rationality and applicability of the proposed index.

The extensive development of high-speed railways in mountainous areas has underscored the significant challenge posed by tunnel floor heave, affecting the operational reliability of ballastless tracks. Such heave induces track deformation and structural impairments, critically undermining the operational safety and track serviceability. This investigation enhances the understanding of ballastless tracks’ mechanical responses to tunnel floor heave by introducing a sophisticated nonlinear analytical model that encapsulates the interplay between the track system, tunnel infrastructure, and the encasing geological environment. Utilizing the concrete damaged plasticity approach to model the track’s concrete structure, this research integrates these parameters with the track’s numerical representation, taking into account the role of internal reinforcement. Through an in-depth examination of track deformation, the interstitial gap, and damage progression within the track, it is demonstrated that comprehensive consideration of both the material’s constitutive model and reinforcement structuring is imperative. The analysis results indicate that the heave’s amplitude and wavelength exert limited influence on the deformation amplitude ratio, whereas variations in heave characteristics significantly alter the wavelength transmission ratio, engendering a distinct “M” shaped gap profile. It is observed that the propensity for material damage escalates in areas experiencing pronounced tensile stress, particularly under conditions of reduced wavelength and increased amplitude heave, necessitating prioritized attention in track maintenance protocols.

Use of traditional lime-cement binders on stabilizing soft sensitive clays pose a significant challenge for the construction sector to reach Finland’s carbon neutrality goals by 2030. Traditional stabilization recipes consisting of cement as binders contributing significantly to CO₂ emissions ($\cong$ 500 kg CO₂ eq./ton in deep mixing alone). This laboratory study explores the feasibility of achieving near carbon-negative stabilization of soft clay leveraging accelerated CO₂ curing (ACC) in biochar (BC) enhanced cementitious composites. BC, a by-product of the biofuel industry, is used as partial replacement of cement (0 %, 10 %, and 50 % of binder) in developing precast cementitious piles. One non-carbonated treatment and two ACC treatments are employed to assess their uniaxial compressive strength, thermogravimetric properties and CO₂ sequestration capacity. The results demonstrate that synergistic effects of using BC with ACC not only enhances the compressive strength of the composites but also promotes CO₂ uptake due to formation of stable carbonates. BC due to its surface functional groups, honeycomb porous structure, and hydrophilicity facilitated uniform CO₂ diffusion in the clay matrix and likely improved internal curing. In ACC treated composites, the replacement of 50 % of cement with BC resulted in sufficient load-bearing capacity (≥50 kPa as per Finnish Guidelines) for both shallow and deep clay layers, making a suitable subgrade media for many types of geotechnical applications. The measured bound CO₂ increased gravimetrically from 2 % to 41 % when cement was partially replaced by BC. In case of non-carbonated samples, 10 % partial replacement of BC provided high strength ($\ge 200kPa$). Life Cycle Assessment (LCA) of a case study of utilizing BC stabilized clay in deep mixing operations can potentially reduce net carbon emissions to −50 kg CO₂ eq./ton.

A pipeline with long-term “hidden leakage” will greatly reduce the stability of the ground between the pipeline and tunnel in the process of tunneling through existing pipelines in unsaturated soil. Excessive settlement of the surrounding strata and pipelines can occur when the shield excavation face approaches below a pipeline, which can lead to engineering accidents. This study is based on a self-developed model experimental system for tunneling through an existing pipeline with a double-line tunnel shield. The ground settlement and pipeline deformation caused by shield construction with small-scale and no leakages are investigated. An experimental study is conducted and the accuracy of the results is verified through a comparison with theoretical solutions. The results demonstrate that there is a significant increase in ground settlement and pipeline deformation under the influence of leakage water. It is also determined that the displacement field generated by the excavation of a double-line tunnel is not simply a superposition of the displacement field generated by the excavation of a single-line tunnel. The repeated disturbances caused by the excavation of a double-line tunnel significantly influences the redistribution of the displacement field. Additionally, a three-dimensional (3D) model of shield construction considering the influence of pipeline leakage is established. This study discusses the ground settlement and pipeline deformation patterns caused by changes in the vertical and horizontal leakage diffusion ranges. The computational results indicate that the diffusion depth of a leakage is the primary factor controlling the extent of settlement.

This study employs the Random Field Finite Difference Analysis to assess how subgrade spatial variability impacts geogrid reinforcement’s strain-alleviating ability and the reinforced pavement’s rutting life. The geogrid’s abilities to reduce critical strains are evaluated using a strain-alleviating ratio and compared between deterministic and spatially variable scenarios. The analysis involves six geogrid reinforcement arrangements, considering two kinds of geogrid stiffness (G1 and G2) and three typical positions: top (L1), mid-depth (L1-2) and bottom (L2) of the base course. Key findings include: (a) Subgrade spatial variability significantly amplifies mean critical strains and leads to irregular strain and stress distributions, which in turn impacts the strain-alleviating ability of the geogrid reinforcements and potentially changes the optimal geogrid position. (b) The impacts of subgrade spatial variability on the geogrids’ strain-alleviating ability vary with the type of critical strains, the geogrid position, and the coefficient of variation and scale of fluctuation of subgrade modulus. When the geogrid is located at L2 (G_L2), its ability to alleviate critical subgrade strain is significantly compromised. (c) The adverse effect of subgrade spatial variability on the rutting life of G_L2 reinforced pavement is significant and can be mitigated by homogenising a very thin sublayer at the subgrade surface.

This paper aimed to investigate the feasibility of partially or completely replacing natural aggregates with recycled aggregates from construction and demolition wastes for low-carbon-emission use as coarse-grained embankment fill materials. The laboratory specimens were prepared by blending natural and recycled aggregates at varying proportions, and a series of laboratory repeated load triaxial compression tests were carried out to study the effects of material index properties and dynamic stress states on the resilient modulus and permanent strain characteristics. Based on the experimental results and by considering the main influencing parameters of the resilient modulus and permanent deformation, an artificial neural network (ANN) prediction model with optimal architecture was developed and optimized by the particle swarm optimization (PSO) algorithm, and its performance and accuracy were verified by supplementary analyses. A shakedown state classification method was proposed based on the unsupervised clustering algorithm, and a prediction model of critical dynamic stress was established based on the machine learning (ML) method and the shakedown state classification results. The research results indicate that the stress state has a greater influence on the resilient modulus and permanent deformation characteristics than other factors, and the shear stress ratio has a significant effect on the shakedown state. The resilient modulus and critical dynamic stress of such specimens vary linearly with confining pressure. The improved PSO-ANN prediction model exhibits high prediction accuracy and robustness, superior to several other commonly used ML regression prediction algorithms. The resilient modulus and critical dynamic stress prediction methods based on ML algorithms can provide technical guidance and theoretical basis for the design and in-service maintenance of similar unbound granular materials.

In recent times, significant advancements have been made in the development of road safety, making it a key focus for Highway Engineers. To ensure the safety of road users, various methodologies have been established for assessing vulnerability, threat, and risk in both road infrastructure and vehicles. This article proposes an evaluation of the risk associated with rockfall on roads, incorporating the analysis of geotechnical and road parameters, verification of risk and vulnerability criteria, statistical analysis of vehicle vulnerability, and the assessment of kinematic slope stability. Additionally, the classification of rock masses in terms of threat is considered, leading to the development of a novel methodology for risk assessment. The evaluation of threat, vulnerability, and risk utilizes conventional methods such as assessing rock mass quality, kinematic stability analysis, and statistical parameters. This methodology has been implemented on the Mérida-El Vigía Highway, giving results consistent with those observed on the ground, unlike the others implemented in the area.

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (q_u) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (D_R) significantly affects the variation in the bearing capacity factor (N_γ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on N_γ at higher Q values, and the unit weight (γ) changes minimally impact N_γ within the range of 16–22 kN/m³. Additionally, an increase in the critical state friction angle (ϕ_cv) consistently increases N_γ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (N_γ).

Stiffness of soil at very small strains G₀ is mainly affected by void ratio, effective stress and suction. Empirical equations considering those factors have been proposed to estimate G₀. However, for collapsible soil like loess, variations in suction might induce changes in void ratio of soil. The combined effect of these two factors poses challenges in accurately estimating of G₀. This paper first presents an experimental study on the G₀ of collapsible loess under various conditions, including as-compacted states, wetting/drying and K₀ loading. G₀ is estimated from shear wave velocity obtained with bender element technique. The changes of G₀ with respect to void ratio, suction, effective stress, and wetting under K₀ stress conditions are evaluated. Test results reveal that power relationships can be defined between G₀ and void ratio, suction and effective stress, respectively. The changes in G₀ along wetting/drying shows an “S” shape due to the different dominant effects on soil structure, as well as the induced non-uniform volume changes when suction change at different zones. Under K₀ loading, G₀ decreases upon wetting at stresses below the compaction stress, while it increases upon wetting at stresses above the compaction stress, due to the combined effects of densification caused by volume collapse during wetting and softening induced by suction decrease. Finally, a G₀ model considering net stress and suction as independent stress variable is proposed. This model could effectively capture the change of G₀ during wetting, drying and loading, as well as upon wetting under K₀ loading for collapsible loess.