The anisotropic behaviour of sands, which is associated with their grain-scale microstructural characteristics such as the distribution of voids and the spatial orientation of particles, can lead to significant variations in macro-scale predictions. In this paper, a bounding surface plasticity based anisotropic semi-micromechanical constitutive model is developed, within the multilaminate framework, to describe the effects of fabric on the cyclic behaviour of sands. A novel plastic strain driven semi-micromechanical fabric evolution framework fulfilling the premises of anisotropic critical state theory is proposed. Rather than using a single scalar-valued fabric anisotropic variable, which is the general practise in anisotropic critical state theory based models, independently evolving fabric anisotropic variables are employed at so-called sampling planes. In addition, the semifluidised state concept is utilised at low mean effective stresses to realistically capture post-liquefaction responses, including large shear deformations and accumulative plastic strains during flow liquefaction and cyclic mobility types of behaviour. The procedure for calibrating model parameters is briefly described and the prediction capabilities of the proposed model under drained and undrained monotonic and cyclic loading conditions at different stress states, relative densities and loading orientations are demonstrated by simulating experimental data for Toyoura sand using a single set of parameters.

Despite the advantages of using Bayesian networks for probabilistic risk assessment, adoption in practice has been limited due to the lack of realistic, facility-scale studies. Scaling up from systems to facility-level safety assessments poses challenges in (i) integrating external hazards and their cascading effects, and (ii) resolving non-homogeneity of various technical and human reliability models. The novelty of the study is in formalising risk integration using Bayesian networks, at facility scale, and demonstrating its effectiveness in addressing associated challenges. A Bayesian network-based multi-hazard risk framework is introduced and demonstrated for a nuclear power plant subject to flooding and earthquake hazards, capturing dependencies among hazards and consequences. Individual reliability models – conventionally extraneous to facility-wide risk models – are included as subnetworks by using Bayesian network-based surrogate models for technical systems and a Bayesian networks approach for human reliability modelling. Two approaches are used for subnetwork integration – object-oriented and unified Bayesian networks. The unified approach allows for prediction, diagnostics and inter-causal reasoning since Bayesian inference is bi-directional. Conversely, in the object-oriented approach, diagnostics are limited to within individual subnetworks and as a consequence the model can potentially neglect dependencies between objects. However, the object-oriented model requires only 50 % of the computational memory and consumes less than 25% of the runtime as the unified network, while improving visual clarity of the risk model. The model reveals key insights – for example, variations in operator stress or available response time during a hazard event can result in up to a 77 % change in top event probability – demonstrating its effectiveness in capturing critical relationships in complex, facility-scale risk scenarios. These findings can be used to suitably allocate resources towards risk mitigation and plant safety management.

Three-dimensional and spatial variability effects on slope failure processes are investigated for an idealised slope stability problem with the random material point method (RMPM). A 45 degree slope is brought to failure by either its own weight or by a combination of its own weight and an additional surface load applied at the crest. The ultimate failure load and potential failure processes are studied for various (heterogeneous) material strength profiles. In 3D, failures tend to spread sideways and backwards. For the slope geometry considered, the resistance to initial and secondary failures in 3D simulations tends to be higher than in 2D simulations, probably due to the additional resistance from the ends of the failure surfaces. The failure behaviour changes when a depth trend in the material strength is introduced. A depth trend in the material strength triggers a flow-like failure process, instead of distinct (approximately) circular failure surfaces which are encountered in a material without a depth trend. The flow-like behaviour causes an expansion in the failure zone in all directions while avoiding (where possible) local strong zones.

Geological materials exhibit spatial variability in their properties as a result of their formation. Many studies have focussed on how to characterise this spatial variation by means of the correlation length θ. Such a characterisation has been applied in the geotechnical design of geostructures at numerous sites where cone penetration tests (CPTs) were available, because θ can be relatively easily estimated from this in-situ information. However, the CPTs available at a given site are often part of the initial site investigation, and hence carried out before the application of any ground improvement technique. This raises the question of how (and by how much) the estimated θ is affected by subsequent stages of the construction project and, more specifically, by the application of ground improvement processes intended to alter the initially poor mechanical condition of the in-situ soil. This paper investigates in-situ data from three trial test sites, where CPT data before and after application of vibro-compaction are available. In addition to the expected overall mechanical improvement of the soil, the application of vibro-compaction has a significant impact on soil heterogeneity, with a substantial reduction in the coefficient of variation and θ of the cone tip resistance and sleeve friction.

Gas-induced fracturing in liquid-saturated clay-rich materials presents challenges in understanding and predicting fracture behaviour, due to the complex mechanical and transport properties of clays and the compressibility of gas. This paper introduces a novel experimental device for visualising fluid-driven cracks in clays. The device allows for the induction and observation of two-dimensional cracks in clay-rich, low-permeability materials through the injection of gas or water. The experimental setup comprises precision instrumentation for measuring compression forces, displacement, and fluid pressure, along with high-resolution imaging capabilities. Preliminary tests with Helium gas injection into Boom clay samples demonstrate the device's ability to track fracture evolution. This innovative experimental tool offers insights into the mechanisms governing fluid-driven fractures in clay-rich materials and provides a means to validate numerical models.

Free-Field (FF) boundaries have previously been developed to replicate the (infinite) far-free-field domain in the simulation of earthquake loading problems. Although they can yield accurate results under certain conditions, it has been observed that significant problems can occur if the behaviour of the material near the boundaries is highly non-linear or incorporates cyclic attributes, or if the boundaries are located close to the domain of interest. To address the inaccuracies caused by the use of traditional FF boundaries, a novel technique is proposed: Tied Free-Fields. This technique combines the principles of both standard earthquake boundary conditions (that is, Tied-Degree (TD) and FF boundaries) to accommodate earthquake loading at the domain boundaries in a direct and economical fashion. The proposed solution has been tested using one and two-dimensional benchmarks and an advanced constitutive model. The results show a significant improvement in accuracy over traditional FF boundaries in the modelling of surface settlements and computed energy released, as well as a significant improvement in computational efficiency over TD boundaries in the modelling of asymmetric problem domains.

As the Material Point Method (MPM) uses both a mesh and a point discretisation scheme, the application of boundary conditions is difficult, currently limiting the flexibility of the method. While many boundary condition options have been used in the literature, the accuracy of Neumann boundary condition options has not yet been studied. Four options have here been evaluated for 1D and 2D benchmarks, although none of the options were found to be both accurate and generally applicable in MPM. However, for the generalised interpolation material point method (GIMP), the application of surface tractions on support domain boundaries or on a detected surface are valid options. Large differences between these two accurate options and the application of tractions at surface material points, a method regularly used in the literature, have been observed.

Capturing the spatial variability in soil is crucial for ground response analyses in the context of seismic hazard mitigation. The lateral variability in thickness and properties of the different soil layers is one of the main factors that determines the variability of the ground motion spectrum from one location to another. The absence of such lateral variability information in the subsoil in between the locations of Cone Penetration Tests (CPTs) may be compensated by the use of more densely sampled seismic data. In this research we aim to derive a shear-wave velocity field through seismic full-waveform inversion that yields a model resolution approaching that of high-resolution seismic CPT surveys. Following this, a datadriven correlation between geophysical and geotechnical information is attempted through the application of new machine-learning-based approaches.

Stratification identification and spatial interpolation play a fundamental role in geotechnical site characterization. A unified approach is needed to perform these two tasks simultaneously to reduce overall uncertainty in site characterization. This paper explores the applicability of the Mixture of Gaussian Processes (MoGP) to address this gap, with a specific focus on characterizing and completing missing CPT data. The investigation encompasses both synthetic and real-world field CPT datasets and includes a comparison of the MoGP's interpolation accuracy with the use of a single GP for entire datasets. Additionally, the study examines the sensitivity of the model's performance with respect to the number of training data points. Although the interpolation performance of the MoGP model is promising with synthetic data, limitations appear in its application to real-site CPT data.

The absence of information on lateral variability in the soil is detrimental to estimating accurately the local site response in the event of an earthquake. To address this problem, the use of densely sampled seismic data together with sparsely distributed but detailed vertical soil profiles obtained from cone penetration tests (CPTs) is advantageous. This study explores the adaptation of suitable machine learning (ML) approaches to derive reliable, site- and depth-specific correlations between seismic shear-wave velocity (Vs) and cone-tip resistance (qc). Such correlation could be successfully established by combining information from seismic CPT surveys with available borehole information for the Groningen region in the Netherlands. It is found that, even over substantial distances, ML-based techniques offer site- and depth-specific correlations between Vs and qc.

This paper investigates the implementation of a nonlocal regularisation of the material point method to mitigate mesh-dependency issues for the simulation of large deformation problems in brittle soils. The adopted constitutive description corresponds to a simple elastoplastic model with nonlinear strain softening. A number of benchmark simulations, assuming static and dynamic conditions, were performed to show the importance of regularisation, as well as to assess the performance and robustness of the implemented nonlocal approach. The relevance of addressing stress oscillation issues, due to material points crossing element boundaries, is also demonstrated. The obtained results provide relevant insights into brittle materials undergoing large deformations within the MPM framework.

Geological uncertainty can significantly influence the computed response of a geotechnical structure. For example, ignoring the presence of a weak soil layer embedded within a stronger layer and assuming a deterministic stratigraphic boundary can significantly underestimate the probability of failure. In this paper, the coupled Markov chain method has been used for modelling this form of uncertainty. A strategy for estimating the horizontal transition probability matrix with limited data has been proposed, which is one of the biggest challenges with using this method. In particular, different sampling intervals in the vertical and horizontal directions have been considered in estimating the matrix for simulating realistic field situations. The applicability of the proposed method has been demonstrated using a set of CPTs in the Netherlands. The results highlight a problem that arises due to the coupling algorithm used in this method.

Uncertainty is inevitable in the characterisation of a geotechnical site, especially due to the inherently heterogeneous nature of the ground. In this paper, a method for characterising a subsurface with limited cone penetration test (CPT) data is proposed. The method is based on integrating predictions of CPT parameters with a probabilistic approach for subsoil classification at the CPTs. The predicted stratigraphy is able to capture the spatial variability of soil measured via CPTs and takes account of the uncertainties that arise from transforming CPT measurements into soil units as well as errors in the measurements themselves. The applicability of the proposed method is demonstrated for a site in the Netherlands. The results show that the proposed approach can identify the most likely classification in the domain with good accuracy. Furthermore, the significance of considering the uncertainties in predicting the most likely classification is illustrated via finite element stability analyses of a slope cut-out in the domain.

In recent years, the demand for sustainable, low carbon-emission energy solutions has risen, leading to an increased interest in shallow geothermal energy systems. These systems, such as ground source heat pump (GSHP) systems, harness the Earth's stable temperature at shallow depths as a heat sink and source of energy. Energy piles are a type of GSHP system that performs both structural and thermal energy-exchange functions. Despite the technological advantages of these systems, understanding the thermomechanical behavior of soils and soil-structure interfaces remains a challenge, limiting safe, efficient, and cost-effective designs. This research aimed to address these challenges by developing thermomechanical constitutive models for soils, implementing these models in boundary-value solvers, and analyzing the experimental results from direct shear tests performed on soils to understand their thermomechanical behavior at the soil-structure interface. By comprehensively examining the complex interactions between energy piles and surrounding soils, the research results offer a foundation for improved design standards and implementation. The methodology followed the following three main directions: Development of thermomechanical constitutive models for soils: A thermomechanical constitutive model based on the thermodynamic framework of Hyperelasticity-Hyperplasticity was developed to capture the major thermomechanical behaviors of fine-grained soils when subjected to temperature variations. By utilizing a flexible yield surface within the framework, a more precise representation of soil behavior can be simulated. This framework was extended to develop a dual-surface thermomechanical model with an additional yield surface and a temperature-dependent kinematic rule to capture the shakedown behavior of soils subjected to heating-cooling cycles more accurately. Numerical aspects to improve simulation capability: A new flexible yield function was introduced that addresses the common issue of undesired elastic domains and erratic or divergent gradients in the numerical implementation of constitutive models with flexible yield surfaces and plastic potentials for use in implicit stress integration schemes. Experimental analysis of the soil-structure interface including the impact of initial shear stress and thermal cycles: Laboratory-scale direct shear tests were conducted on fine- and coarse-grained soils to investigate their thermomechanical behavior at the interface with a concrete structure. This comprehensive investigation of the thermomechanical behavior of soils and soil-structure interfaces in the context of thermo-active geo-structures represents an essential step forward in optimizing energy pile systems for diverse applications. The developed constitutive models, numerical algorithms, and experimental analyses contribute valuable insights for improving energy pile design, ultimately leading to more efficient, safe, and cost-effective solutions. Future research should continue refining our understanding of the complex processes governing soil behavior in energy pile systems, with emphasis on consolidating the developed models and their implementation for a range of soil types and conditions.

Natural soil deposits may possess a highly anisotropic nature. The fabric anisotropy of soils which is induced during the soil formation process can lead to severe variation in field scale responses. Although the influence of fabric on the response of sands is well known and several advanced constitutive models have been developed to account for it, most of the studies incorporating anisotropy have focused on element test simulations while practical boundary value problem simulations are usually omitted. In this paper, the undrained response and liquefaction resistance of anisotropic sand deposits with different inherent fabric anisotropies are numerically investigated through element test simulations and one-dimensional nonlinear effective stress site response analyses. A novel semi-micromechanical constitutive model accounting for the effect of fabric anisotropy on sand liquefaction has been incorporated into a fully coupled dynamic in-house code employing the u-p formulation. The proposed numerical framework shows that, in both element test simulations and site response analyses, the fabric effects stemming from both the inherent and induced anisotropies can significantly influence the liquefaction resistance of sands.

Retrogressive failures occur in slopes consisting of sensitive materials such as snow or quick clay. They can be triggered by a small disturbance at the slope toe, but can cause propagated failure spreading miles away. Understanding the physical mechanism and predicting the retrogressive failure process are particularly important. Previous studies have discussed the failure criteria, the soil properties or the method of numerical modeling of retrogressive slope failure. However, little attention has been paid to the microscopic failure mechanism, especially relating to various possible failure patterns. In this study, multiscale modeling is incorporated to study the physical mechanism of different retrogressive failure patterns, including earth flow, flowslide and spread failure, within a unified framework. Utilizing multiscale analysis, we found that earth flow failure is related to the shear failure of granular materials. In contrast, the development of macroscopic shear bands is accompanied by tensile failure. As shear and tension failures are typical failure mechanisms of frictional and cohesive materials, it is deduced that friction and cohesion effects play key roles in different retrogressive failure patterns. Therefore, the distributions of attractive and repulsive contact forces are explored and a novel parameter η is proposed to quantify the interplay between friction and cohesion. Further analysis proves that η can capture the effect of friction and cohesion and distinguish different retrogressive failure patterns. Finally, a spectrum of retrogressive failures for a granular slope is established, in which the failure mechanism is explained by the changeable dominant effect, that is, frictional or cohesive in soil.

This research focuses on investigating the relative performance of a range of machine learning algorithms, namely the artificial neural network, support vector machine, Gaussian process regression, random forest, and XGBoost, for predicting the undrained shear strength from cone penetration test data. This is to assess how machine learning could help us lower the need for laboratory test data. The training dataset compiles 526 data from 12 regions and the testing dataset consists of 20 data from a polder located close to Leiden in the Netherlands. In addition, k-fold and group k-fold cross-validation strategies are both applied to validate the models. The poor performance of the models during group k-fold cross-validation suggests that, while machine learning techniques can perform well when site-specific data are included during training, they struggle to generalize without site-specific data. This highlights the difficulty of capturing soil heterogeneity and suggests that either machine learning methods should be trained on specific sites for which some data are already available, or much larger training datasets are needed.

The stability of six regional dyke cross-sections in the Netherlands was re-assessed using the random finite element method (RFEM), which explicitly accounts for the spatial variability of strength parameters. The RFEM assessments of the cross-sections were shown to result in significantly narrower response distributions than those obtained by ignoring the spatial variability, and therefore would result in more economical designs. Given the complexity of RFEM for applications in daily engineering practice, the results obtained from the re-assessments of the six dyke cross-sections were used to propose partial factors that can be used in practice to achieve the desired reliability levels for regional dykes. When applied in a conventional semi-probabilistic assessment of a dyke cross-section, these partial factors would result in the same level of reliability as would have been obtained by carrying out an RFEM analysis of the same cross-section.

Nonlinear effective stress site response analyses (SRAs) are commonly used to estimate dynamic soil behaviour, seismic wave propagation through the soil medium, and resulting ground motions. These analyses can be used to identify potential hazards (e.g., landslides, settlements, liquefaction) and to estimate dynamic loads on superstructures in areas that are prone to natural or induced earthquakes, which can help with disaster planning and risk mitigation efforts. In this study, the influence of fabric anisotropy, which is induced during the soil formation process, on the response of sand deposits has been assessed through one-dimensional site response and response spectrum analyses. First, a novel anisotropic critical state theory (ACST) based semi-micromechanical constitutive model accounting for the effect of fabric anisotropy has been incorporated into a fully coupled dynamic code employing the u-p formulation. Then, the initial fabric anisotropy has quantitatively been changed to imitate different anisotropic formations observed in natural deposits. The proposed numerical procedure shows that fabric effects stemming from the anisotropic nature of sands can significantly influence the dynamic behaviour of sand deposits, leading to significant variations in ground motions and therefore resulting in diverse spectral accelerations at the ground surface.

Free-field site response analysis is a standard technique used to predict soil deposit dynamic response and liquefaction susceptibility. Such analyses are typically carried out by implementing periodic boundaries to guarantee the same speed of the dynamic waves travelling across them. However, when using random fields to consider the impact of soil spatial variability there is the possibility of an inconsistency with periodic boundaries. This is due to the generation of non-identical properties at the lateral boundaries on using traditional random fields. To overcome this inconsistency, this paper proposes periodic random fields to model spatial variability by matching the periodicity at the boundaries. To investigate the significance of using the proposed approach, a heterogeneous soil deposit subjected to earthquake loading is analysed using the random finite element method. The results show that, for certain values of the horizontal scale of fluctuation, ensuring consistency at the lateral boundaries could result in less conservative predictions of the extent of the liquefied areas.