Consolidation Model Research Articles

Machine learning-aided selection of CPT-based transformation models using field monitoring data from a specific project

AbstractTransformation models have been widely used in geotechnical engineering to relate data from lab or field tests (e.g., cone penetration tests, CPT) to design parameters required in geotechnical analysis and design. Proper selection of transformation models is crucial but challenging for accurate prediction of geotechnical responses (e.g., reclamation-induced settlement) in practice. This study proposes a general machine learning framework that accommodates a wide variety of existing CPT-based transformation models and uses field monitoring data (e.g., settlement data observed from a specific project) to select suitable transformation models for improving prediction of spatiotemporally varying reclamation-induced settlement. The proposed approach takes advantage of sparse dictionary learning (SDL) and achieves prediction of settlement by a linear weighted sum of dictionary atoms that are constructed using outputs from finite element models (FEM) of reclamation-induced consolidation. Input parameters of the FEM models are determined using existing transformation models in literature. A transformation model database that relates multiple soil consolidation parameters with CPT data is also compiled for consolidation analysis and dictionary construction in SDL. The proposed approach is illustrated using a real reclamation project in Hong Kong. Results show that the proposed approach provides an effective and transparent vehicle to leverage existing abundant transformation models, identify appropriate transformation models using field monitoring data, and improve prediction of spatiotemporally varying reclamation-induced settlement, with greatly reduced prediction uncertainty. The transformation model selection and settlement prediction are also improved continuously as more field monitoring data are obtained.

Large strain consolidation model for very soft soils with prefabricated horizontal drains considering nonlinear compression and creep

Large strain consolidation model for very soft soils with prefabricated horizontal drains considering nonlinear compression and creep

Development of a simple test to quantify the consolidation properties of liquefied granular materials

AbstractLiquefaction can have devastating consequences by causing increased mobility of debris flows, tailings dam breaches, and settlement following seismic shaking. Observations on the consolidation behaviour of liquefied soils in 1-g or centrifuge shake table tests have permitted significant advancements in analytical and numerical methods to predict the rate and magnitude of consolidation settlement. However, advanced consolidation models introduce material parameters which are currently difficult to define quickly and at low cost. The objective of this paper is to demonstrate the development of a simple, low-cost test that can be used to quickly estimate the spatially and temporally varying coefficient of consolidation, which controls post-liquefaction settlements. Using the proposed simple setup, experiments were conducted on four uniformly graded sands of varying grain size and on one well-graded mixture. A range of analytical and numerical approaches from literature were assessed for their ability to back-analyse the observed pore pressure dissipation and settlement. Estimated values for the coefficient of consolidation were comparable between models and estimates of surface settlement matched well the experimental results. The simplicity of the proposed test combined with its low-cost and mobile nature, raise significant possibilities for quick estimations of the evolution of the coefficient of consolidation post-liquefaction.

Large strain consolidation analysis of dredged sludge treated with multilayer PHDs combined with vacuum pressure

Multilayer PHDs combined with vacuum pressure for the treatment of dredged sludge yards are effective in avoiding the bending effect of drainage bodies during consolidation. However, the existing theory of self-weight consolidation cannot reflect the actual consolidation properties of dredged sludge treated with multilayer PHDs combined with vacuum pressure. In this study, the nonlinear large-strain consolidation model of dredged sludge treated with multilayer PHDs combined with vacuum pressure was established, considering the nonlinear compressibility and permeability of dredged sludge, self-weight stress of dredged sludge and the variation of dredged sludge height with time. The reliability of the model is verified by comparison with existing consolidation theory, indoor test, and field test data. By researching the effect of the PHDs laying ratios, the height of each dredged sludge layer, and the stacking process time on the consolidation rate of dredge sludge, it is found that if the total height of the treated dredged sludge is 9.0 m and the 5.0 m high yard is designed with double-layer PHDs, the optimal PHDs laying ratios is 20%, the optimal location of the 2nd layer PHDs is at half of the total height of dredged sludge and the optimal stacking time is 140 days.

Compressional rheology model for vacuum filtration-consolidation of sludge in geotextile tubes

Recently, the disposal of high-water-content dredged sludge through geotextile tubes with vacuum-assisted prefabricated horizontal drains (PHD) has gained growing popularity for its convenience and efficiency. However, existing simulations for this typical coupled filtration-consolidation process using conventional consolidation models exhibit deficiencies due to the absence of effective stress and invalidity of Darcy’s law in the particle-wandering filtration phase. In this study, based on the compressional rheology theory, a two-dimensional coupled filtration-consolidation model constituted by the compressive yield stress Py(ϕ) and hindered setting factor r(ϕ) is developed to elucidate the solid-solid and solid-fluid interactions during slurry dewatering. A novel approach for measuring consistent Py(ϕ) and r(ϕ) relationships is proposed. The numerical solution is derived utilizing the alternating direction implicit (ADI) difference method and verified against a classical one-dimensional compressional rheology model and a field trial. Further analysis suggests that constitutive parameters, e.g., gel point, affect the dewatering efficiency by determining the relative degree of soil disorder, obstruction to particle movement, and vacuum transmission effect, while design parameters, e.g., PHD spacing and tube height, impact the magnitude and radiation range of vacuum pressure to influence the overall efficiency.

NEW ECONOMETRIC MODEL OF SUSTAINABLE ECONOMIC DEVELOPMENT QUANTIFYING THE IMPACT OF THE IMPLEMENTATION OF SDG9 IN THE EUROPEAN CONTEXT

In the context of European integration, sustainable economic development in the European Union is a strategic priority. In recent years, the European Union has made progress towards sustainable development objectives, reflecting a commitment to economic growth that respects the principles of environmental protection, social equity and economic efficiency. The European Union plays an important role in shaping sustainable development policies and initiatives through legislation, directives and financial funds aimed at both protecting the environment and stimulating innovation and competitiveness. We aim to analyse the level of innovative sustainability of the European economy in relation to the European dimension of innovative sustainability using an econometric model. The methods used consist of a literature survey, database consolidation and econometric modelling. The econometric model shows that environmental taxes and raw material consumption have a negative impact on GDP per capita in the short run, while technological innovation, measured by the number of patents and R&D personnel, contributes significantly to economic growth and sustainable development, underlining the need for long-term investment in human capital and green technologies to support the competitiveness and sustainability of European economies. The results of the study will lead to the formulation of European public policies on sustainable economic development regarding the implementation of Sustainable Development Goal 9.

Predicting hardwood porosity domains: Toward cascading computer-vision wood identification models

Prior work on computer-vision wood identification (CVWID) for North American hardwoods yielded two independent deep learning models – a 22-class model for diffuse-porous woods and a 17-class model for ring-porous woods – but did not address semi-ring-porous woods nor provide a CVWID solution for an unknown specimen without a human first determining which model to deploy. As untrained human operators would lack the anatomical proficiency to differentiate among porosity domains, it is necessary to develop a consolidated model that can identify diffuse-, ring-, and semi-ring-porous woods. Previous research suggests that prediction accuracy might decrease as class number grows. A potential strategy to reduce the number of classes a CVWID system must consider at a time is to hierarchically deploy a cascade of models. In pursuit of a unified model that can cover North American hardwoods of all porosity types, this study compared the accuracies of a consolidated 39-class (ring- + diffuse-porous) model and a consolidated 42-class (ring- + diffuse- + semi-ring-porous) model with a two-tiered, cascading model scheme whereby images are first differentiated into three porosity domain classes and then again into only those taxonomic classes with that porosity. The results showed that the cascading model scheme can mitigate the accuracy reductions incurred by the 42-class model and nearly eliminate the occurrence of cross-domain misidentifications.

Machine-learning based prediction of obstructive coronary artery disease using integrated submodules of clinical information, chest x-ray, and electrocardiography

Abstract Background The early diagnosis of obstructive coronary artery disease (CAD) is critical. However, delays in diagnosis may occur due to subsequent high-cost tests, contributing to substantial medical expenses. A more insightful interpretation of fundamental diagnostic tools, such as chest x-ray (CXR) and electrocardiography (ECG), could mitigate medical costs and facilitate a timely diagnosis. Purpose This study explores the application of machine learning to interpret each modality and create a consolidated prediction model for significant CAD, aiming to construct a diagnostic model based on primary tests, including CXR, ECG, and clinical information. Methods Clinical information-ECG-CXR paired data were generated from 19,140 patients, with CAD confirmed through coronary angiography (CAG). Machine learning (ML) was employed to develop submodules for each modality, and various integration methods were explored. The area under the curve (AUC) served as the outcome metric for predicting obstructive CAD. Results The development set comprised data from 17,976 patients, with CAG-confirmed obstructive CAD observed in approximately 60% of patients. The obstructive CAD group exhibited characteristics such as advanced age, male predominance, a higher prevalence of chest pain, and more risk factors. The ML model, incorporating clinical information, CXR, and ECG with submodules, demonstrated the optimal prediction of obstructive CAD (AUC 0.722). This model significantly outperformed the model without submodules (0.722 vs. 0.638, p<0.0001) in obstructive CAD prediction. Conclusions Through the integration of submodules encompassing clinical information and basic tests, leveraging a high-quality database, the integrated ML algorithm offers improved prediction capabilities for CAG-confirmed obstructive CAD. This underscores the potential role of machine learning in clinical decision-making based on diverse modalities with distinct characteristics.

Coupled filtration–consolidation model for vacuum-preloaded soft ground

The widespread utilisation of vacuum-assisted prefabricated vertical drains for managing clayey soft ground has led to the development of numerous consolidation models. However, these models have limitations when describing the filtration behaviour of soil under conditions of high water content, without the formation of a particle network. To address this issue effectively, in this work, based on the compressional rheology theory, a two-dimensional axisymmetric model incorporating the compressive yield stress Py(ϕ) and a hindered setting factor r(ϕ) was developed to couple the filtration and consolidation of soil under vacuum preloading. A novel approach for determining the unified ϕ–Py–r relationships was introduced. The equation governing such fluid/solid and solid/solid interactions was solved using the alternative direction implicit method, and the numerical solutions were validated against the one-dimensional filtration cases, three-dimensional laboratory model tests and large-scale field trials. Further parametric analysis suggests that the radius of the representative unit and r(ϕ) exclusively affect the dewatering rate of the clayey slurry, while the gel point and Py(ϕ) influence both the dewatering rate and the final deformation.

Coupling effects on class C predictions of soft soil settlements

AbstractIn uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deformation and pore water pressure dissipation in the back analysis of soft soil settlements. It further evaluates the suitability of both coupled and uncoupled constitutive models with different types of monitoring data, providing practical guidance for selecting consolidation models and achieving reliable long-term predictions. The one-dimensional governing equations for soft soil consolidation, incorporating prefabricated vertical drains and creep deformation, are first reviewed. A case study of a trial embankment in Ballina, New South Wales, Australia, is then used to demonstrate the impact of coupling effects and monitoring data on settlement predictions. The results show that considering coupling effects not only improves long-term settlement predictions but also reduces uncertainties in the updated soil parameters, especially when both settlement and pore water pressure data are used.

A Strain-Controlled Finite Strain Model for CRD Consolidation of Saturated Clays Considering Non-Linear Compression and Permeability Relationships

Consolidation is the combined phenomenon of the compression and groundwater seepage of clay. Accurate evaluation of the consolidation characteristic is essential for the design, construction, and long-term stability of geotechnical structures. In this study, a strain-controlled non-linear finite strain model for constant rate-of-deformation (CRD) consolidation was developed for quickly and reliably predicting the consolidation behavior of clay soils. The model can account for any form of non-linear compression and permeability relationships, thus considering variations in the coefficient of consolidation. Being strain-controlled, it overcomes the limitations of stress-controlled models which require complex numerical iteration. The validity and accuracy of this model were verified through rigorous comparisons with both numerical simulations and experimental data. For normally consolidated soils, a non-linear e-lgσ′compression model was used instead of a linear compression model. For overconsolidated soils, the Harris function compression model was determined to be recommended to overcome the discontinuities in total stress and pore pressure caused by the traditional piecewise e-lgσ′ model. It was also found that determining the steady state of consolidation for normally consolidated soils should use the non-linear method, while the linear method is suggested to be adopted for overconsolidated soils.

Understanding the evolution of competing institutional logics in the marketization of care: A stage model analysis of Australia's National Disability Insurance Scheme

This study explores the marketization of healthcare through a stage model analysis, focusing on Australia's National Disability Insurance Scheme (NDIS). By employing mixed methods, including sentiment and frequency analysis as well as qualitative content analysis of policy documents and media coverage, we trace the NDIS's evolution and the interplay of competing social welfare and market logics over time. Our findings underline that the evolution and interplay between competing institutional logics follow a stage model of institutional change, detailing pre-emergence, orientation, contestation, consolidation, and normalization phases. Additionally, we observe a shift in dominant institutional logics across different stages, demonstrating the critical role of media and public sentiment in shaping discourse about the marketization of care, which intertwines with policy decision-making. Our findings emphasize the importance of adaptive engagement and communication strategies by policymakers to avoid marginalizing vulnerable groups as institutional logics evolve, especially in the latter stages of the process when a dominant logic has emerged. The study highlights the complex dynamics of institutional change and offers insights for both researchers and practitioners in the healthcare sector, shedding light on the coevolution of competing logics in the policy development and implementation process.

Weak Galerkin finite element method with the total pressure variable for Biot's consolidation model

In this work, we develop a weak Galerkin method for the three-field Biot's consolidation model. The key idea is to consider the total pressure variable. We employ the stable pair of weak Galerkin finite elements to discretize the displacement and total pressure, and use totally discontinuous weak functions to approximate pressure in a semi-discrete scheme. Then, we give the fully discrete scheme based on the backward Euler method in time. Furthermore, we prove the well-posedness of the numerical schemes and derive the optimal error estimates for three variables in their nature norms. Our theoretical results are independent of the Lamé constant λ and the storage coefficient c0. Finally, some experiments that employ different polynomial degrees and polygonal meshes are presented to demonstrate the efficiency and stability of the weak Galerkin method.

Modeling of Drain Consolidation in the Quick Triaxial Test and Its Analytical Solution

ABSTRACTSand columns have been widely used to accelerate drainage and then improving the mechanical properties of soft soil foundations. The sand column has also been introduced into the triaxial test by researchers, in the center of the cylindrical specimen, to greatly accelerate drainage and consolidation process. The objective of this paper is to evaluate the consolidation properties of the triaxial cylindrical specimen considering the presence of a sand column, and then to propose a consolidation model that simulates the consolidation process of the triaxial test. The consolidation equations were derived considering the drainage of the specimen with a sand column composed of both vertical and double‐radial flows. Then the analytical solution of the model was obtained based on specific initial and boundary conditions. The comparison between the consolidation model and the laboratory tests yielded highly consistent. The case study demonstrated that the proposed consolidation model accurately simulates the evolution of average pore pressure and degree of consolidation in triaxial specimens containing a sand column. The studies on the consolidation parameters showed that there were different effects on the drainage rate for the diameter of specimen, the permeability coefficients of specimen and sand column, as well as the radius of the sand column.

Performance Analysis of Pile Group Installation in Saturated Clay

In offshore pile engineering, the installation of jacked piles generates compaction effects within soil, thus further affecting previously installed adjacent piles. This study proposes a three-dimensional numerical model for pile group installation, soil consolidation, and loading analysis. Subsequently, the effect of pile spacing and pile length-to-diameter ratio on the deformation, internal forces, and vertical bearing capacity of adjacent piles are investigated. The results indicate that with an increase in pile center distance, the peak lateral displacement of the adjacent piles decreases, whereas the peak vertical displacement increases. As the pile length-to-diameter ratio increases, the peak vertical and lateral displacements of the adjacent piles are enhanced. In addition, the peak axial force of the adjacent piles initially decreases and then increases with the penetration depth of the subsequent pile, whereas the peak bending moment initially increases and then decreases. The vertical bearing capacity of the subsequent pile is significantly superior to that of the adjacent piles. Therefore, the effects of pile installation on adjacent piles should be included in pile engineering. The impact of the subsequent pile installation on the bearing capacity of adjacent piles can be significantly reduced by increasing the pile center distance and pile length-to-diameter ratio. The findings provide useful guidance for pile group engineering.

Adversarial Variational Autoencoders to Extend and Improve Generative Model

Generative artificial intelligence (GenAI) has been advancing with many notable achievements like ChatGPT and Bard. The deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to the strong role of deep neural networks (DNNs) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims at synaptic plasticity of the human neuron network, fosters the generation ability of DGM, which produces surprising results with the support of statistical flexibility. Two popular approaches in DGM are the variational autoencoder (VAE) and generative adversarial network (GAN). Both VAE and GAN have their own strong points although they share and imply the underlying theory of statistics as well as significant complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. This research unifies VAE and GAN into a consistent and consolidated model called the adversarial variational autoencoder (AVA) in which the VAE and GAN complement each other; for instance, the VAE is a good data generator by encoding data via the excellent ideology of Kullback–Leibler divergence and the GAN is a significantly important method to assess the reliability of data as to whether it is real or fake. In other words, the AVA aims to improve the accuracy of generative models; besides, the AVA extends the function of simple generative models. In methodology, this research focuses on the combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

Modeling the START transition in the budding yeast cell cycle.

Budding yeast, Saccharomyces cerevisiae, is widely used as a model organism to study the genetics underlying eukaryotic cellular processes and growth critical to cancer development, such as cell division and cell cycle progression. The budding yeast cell cycle is also one of the best-studied dynamical systems owing to its thoroughly resolved genetics. However, the dynamics underlying the crucial cell cycle decision point called the START transition, at which the cell commits to a new round of DNA replication and cell division, are under-studied. The START machinery involves a central cyclin-dependent kinase; cyclins responsible for starting the transition, bud formation, and initiating DNA synthesis; and their transcriptional regulators. However, evidence has shown that the mechanism is more complicated than a simple irreversible transition switch. Activating a key transcription regulator SBF requires the phosphorylation of its inhibitor, Whi5, or an SBF/MBF monomeric component, Swi6, but not necessarily both. Also, the timing and mechanism of the inhibitor Whi5's nuclear export, while important, are not critical for the timing and execution of START. Therefore, there is a need for a consolidated model for the budding yeast START transition, reconciling regulatory and spatial dynamics. We built a detailed mathematical model (START-BYCC) for the START transition in the budding yeast cell cycle based on established molecular interactions and experimental phenotypes. START-BYCC recapitulates the underlying dynamics and correctly emulates key phenotypic traits of ~150 known START mutants, including regulation of size control, localization of inhibitor/transcription factor complexes, and the nutritional effects on size control. Such a detailed mechanistic understanding of the underlying dynamics gets us closer towards deconvoluting the aberrant cellular development in cancer.

Impact of structural dependence on the formation of multi-subject collaborative network for comprehensive land consolidation

Rural decline is a common challenge facing the world, and it is imperative to revitalize the world's countryside. As an essential platform and means to promote rural revitalization, comprehensive land consolidation (CLC) is characterized by diverse governance subjects and a multi-subject collaborative network has been formed in its implementation process. Prior research primarily investigates the role of exogenous mechanisms in shaping multi-subject collaborative relationships, often overlooking endogenous mechanisms, namely the endogenous structural dependence of the multi-subject collaborative network itself. In this study, the research dimensions and levels of the CLC multi-subject collaborative network were combed out, and the influence mechanism of structural dependence on the formation of this network was analyzed. Taking the CLC pilot projects in 11 counties (cities and districts) in Hubei Province, China, as study cases, this study empirically examined the impact of structural dependence on forming the CLC multi-subject collaborative network using the exponential random graph model. The results show that the formation of the CLC multi-subject collaborative network was significantly influenced by endogenous mechanisms, such as reciprocal and triangular structures, and exhibited prominent path-dependent characteristics. In addition, structural dependence significantly weakened the influence effect of exogenous mechanisms, and ignoring endogenous mechanisms led to a decrease in the interpretability and credibility of exogenous mechanisms. Based on these findings, this study puts forward corresponding policy recommendations. This study provides theoretical insights and decision-making references for promoting effective collaboration among multiple subjects and enhancing the effectiveness of CLC. It also provides China’s experience and reference for other developing countries to establish a multi-subject collaboration model for land consolidation.

Analytical modeling and experiments on soft soil consolidation with vacuum preloading‐airbag pressurization

AbstractThis study aims to provide substantial theoretical support for employing vacuum preloading‐airbag pressurization (VP‐AP) to enhance soft soil foundations. By integrating the airbags and prefabricated vertical drains (PVDs) within the analytical framework, the consolidation models are developed to accommodate double smear zones and well resistance. Analytical solutions for various airbag pressurization scenarios are derived under the equal‐strain assumption. A model test is subsequently conducted to investigate the settlement process of soft soil using the VP‐AP method. The validity of the proposed theoretical model is corroborated through comparison with existing analytical solutions and experimental data. The accuracy of the predictive model is further substantiated by the model test results. Lastly, this research offers an in‐depth analysis of soil consolidation behavior under diverse influencing factors. The findings indicate that the VP‐AP method demonstrates superior efficacy over conventional vacuum‐surcharge preloading when the airbag pressure matches the external load pressure. Additionally, an increase in the radius of airbags enhances the soil consolidation efficiency. The analytical model presented in this study elucidates the consolidation mechanisms of the VP‐AP method, with experimental research confirming the efficacy of this approach and the predictive model established herein.

Large strain consolidation of soft soils with partially penetrating PVDs: Analytical solutions incorporating variable well resistance

The large-strain geometric assumptions and nonlinear compressibility and permeability have significant effects on the consolidation of soft soils with high compressibility. However, analytical solutions for large-strain nonlinear consolidation of soft soils with partially penetrating PVDs have been rarely reported in the literature. A double logarithmic model is adopted to describe the nonlinear compressibility and permeability of soft soils with high compressibility, and a large-strain consolidation model for soft soils with partially penetrating PVDs under the condition that the excess pore water pressure at the interface between the improved and unimproved layers is equal is established based on Gibson’s large-strain consolidation theory. The analytical solution for the large-strain nonlinear consolidation model for soft soils with partially penetrating PVDs is obtained. The reliability of the analytical solution obtained in this study is verified by comparing it with the existing solutions under different conditions, and the maximum deviation between the two methods does not exceed 5 %. On this basis, consolidation behaviors of soft soils with partially penetrating PVDs under different conditions were analyzed by extensive calculations. Finally, the proposed analytical solution for the large strain consolidation model is applied to the settlement calculation of the Bachiem Highway Project, which further demonstrates the applicability of the consolidation model.