1,720,952 research outputs found
An improved semi-analytical method for 3D slope reliability assessments
No full textAn improved semi-analytical method for calculating the reliability of 3D slopes with spatially varying shear strength parameters is proposed. The response of an existing semi-analytical method has been compared with that of the computationally more intensive, but more general, random finite element method (RFEM), demonstrating that the simpler method underestimates the failure probability. An alternative relationship for the expected failure length and two correction factors are proposed, which modify the original formulation of the simpler method. The proposed approach gives substantially improved results that compare favourably with those obtained by RFEM, and therefore provides a more accurate simplified solution.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineerin
3D slope stability analysis with spatially variable and cross-correlated shear strength parameters
No full textThe paper investigates the stability of slopes with spatially variable and cross-correlated shear strength parameters in 3D. The influence of various cross-correlation coefficients between these parameters on the probability of 3D slope failure has been considered for different levels of anisotropy of the heterogeneity in the shear strength. Specifically, 3D random fields of cohesion and friction angle were generated using the Local Average Subdivision method, and these were correlated with each other by various degrees. The fields were then linked to finite element analyses within a Monte Carlo framework. The results indicate that a positive cross-correlation between the parameters reduces the slope reliability, whereas a negative cross-correlation between the parameters increases the reliability.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineeringGeoscience and Engineerin
Geotechnical uncertainties and reliability-based assessments of dykes
No full textThis thesis utilises the random finite element method (RFEM) to provide practical guidance and tools for geotechnical engineers to account for the influence of soil spatial variability. This has involved: (a) practical insight and guidance on the choice of characteristic soil property values and scales of fluctuation; (b) a robust approach to reliability assessment and design that obviates the need for explicit calculation of characteristic values; and (c) the benchmarking and improving of simpler analysis tools.Geo-engineerin
On characteristic values for calculating factors of safety for dyke stability
No full textVarious simplified approaches are used to calculate the characteristic values of shear strength properties, which have then been used in deterministic stability analyses of a dyke cross-section. The calculated factors of safety are compared with the 5-percentile ‘system response’ of the dyke cross-section, calculated using the more exhaustive random finite-element method (RFEM), which is consistent with the requirements of Eurocode 7. The simplified methods accounting for variance reduction due to averaging of property values mostly give factors of safety within 10% of the RFEM solution, whereas the factor of safety based on the 5-percentile material properties is significantly over-conservative.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineerin
Reliability-Based Partial Factors Considering Spatial Variability of Strength Parameters
No full textThe 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.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineerin
Effect of uncertainties in geometry, inter-layer boundary and shear strength properties on the probabilistic stability of a 3D embankment slope
No full textThis paper investigates the influence of three forms of uncertainty on the probabilistic stability of an idealised 3D embankment slope. These are: 1D spatial variability in the external geometry of the slope along its length, 2D spatial variability in the depth of the boundary between the embankment material and the foundation layer, and 3D spatial variability in the shear strength properties of the slope and foundation materials. The relative influence of each uncertainty has been investigated using the random finite element method, based on statistics consistent with a Dutch regional dyke. The results indicate that, for such a structure, the soil spatial variability has a much greater influence than uncertainties relating to embankment geometry and inter-layer boundary. In particular, it is demonstrated that the spatial correlation of material properties along the length of the embankment has a greater influence on the probabilistic characteristics of the embankment slope stability and failure consequence than the spatial correlation of properties perpendicular to it. A worst case scale of fluctuation for the material properties is identified.Geo-engineerin
Probabilistic classification of soils based on Local Average Subdivision method and CPT data
No full textA random field generator based on Local Average Subdivision (LAS) method is proposed in this study in order to achieve probabilistic soil classification and quantify the uncertainty of the generated most probable geological cross section. CPT data and Robertson’s soil classification chart (1990) are adopted to classify the soil. The sole application of LAS makes the random field unconditional, which has been improved to conditional random field generator by using Kriging interpolation. Both unconditional and conditional generator are tested in an illustrative example and the results indicate that the improvement from unconditional to conditional random field reduces the uncertainty of the most probable result of classifications and the classifications in the unconditional random field will converge if there are enough realizations. Additionally, the conditional random field generator is further applied in a case with three conducted CPTs, which build up a domain with very large scale of fluctuations. It’s found that the uncertainty of the generated most probable result of classifications is pretty low so it’s speculated that the proposed generator can be best applied in a large scale of fluctuation scenario. Another finding in the case study is that the proposed random field generator can be used to verify the reliability of conducted CPTs.Additional ThesisGeo-Engineerin
Influence of Residual Dyke Strength on Dyke Reliability Using the Random Material Point Method
No full textThe material point method (MPM) is used to model both rotational and horizontal sliding failure mechanisms of dykes under external (water) loading. To model the different failure mechanisms, an external hydrostatic water pressure has been applied on the canal side of the dyke by applying a newly developed boundary condition. The boundary condition detects the material boundary and distributes the applied load to the nodes of the background mesh. The definition of dyke failure has also been investigated using MPM. In conventional dyke assessment, using (for example) the finite element method (FEM), the dyke is considered to have failed as soon as an initial failure occurs. However, the dyke may still be able to resist the flow of water, and this continuing ability to resist water flow is known as residual dyke strength. By taking account of residual dyke strength, for example with MPM as shown in this paper, the computed reliability can increase compared to conventional assessment, as in some cases total dyke failure does not occur after an initial slope failure. Finally, spatial variability is considered using the random material point method (RMPM), which combines random fields with MPM in a Monte Carlo framework. When considering spatial variability, a significant gain in reliability due to residual dyke strength has been observed, but further investigation is required to fully understand the effect of spatial variability on residual dyke strength. In order to simplify this preliminary investigation, the adopted soil properties in this paper have not been based on actual soils used in dyke construction; the results are only intended to indicate the capabilities of RMPM and develop hypotheses on the effect of residual dyke strength.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Geo-engineerin
Probabilistic identification of soil stratigraphy using CPT data
No full textThe deterministic approach for interpreting CPT soil profiles poses the serious limitation of not taking data uncertainty into account. Therefore, a Bayesian model was developed by Wang et al. (2013) that, for a given CPT profile, determines the most probable number of soil layers and most probable soil layer thicknesses by simulating and comparing multiple ‘model classes’ with different complexities. In this study, this proposed model is implemented into the Python coding environment after which the functionality is verified by conducting a case study on a 23 푚 CPT profile from the Groningen area (NE Netherlands). For the given CPT profile, the model distinguishes 6 separate soil layers from which the position and thickness are in agreement with the deterministic analysis and the available borehole data. However, the case study suggests that the model fails to correctly identify the most probable soil types for CPT measurements within the vicinity of the edges of the Robertson chart. This is most-likely related to a “cut-off”-effect of the joint Gaussian distribution describing the uncertainty of a single datapoint. A subsequent study on the integration of the statistical parameters within the model is therefore required. Additionally, the code includes several optimizing strategies, but remains time consuming for very complex model classes. Further optimization is suggested to achieve greater model precision and efficiency.https://github.com/guidodezeeuw/Bayesian Github with codeAdditional ThesisGeo-EngineeringCivil Engineerin
Machine learning for prediction of undrained shear strength from cone penetration test data
No full textThe need of shear strength measurements of soil in the design phase of geotechnical engineering is almost indispensable. Many methods have been applied to estimate the shear strength of soil, including various laboratory test, in-situ test and analytical methods. As an in-situ test method, cone penetration test (CPT) is a powerful and cost-effective tool for the investigation of subsoil conditions. CPT data is usually complemented by the laboratory test data for verification. The laboratory-based studies of subsoil, however, can be not only a complex but also tedious and expensive task for large projects involving large amount of data. Therefore, new approaches for estimating the soil shear strength are demanded. Having demonstrated superior predictive ability for many material properties compared to traditional methods, machine learning methods have been increasingly popular and widely used. This thesis focus on the prediction of soil undrained shear strength through cone penetration test data. The major objectives of this master thesis include testing how machine learning could help us lower the need for laboratory test data. At first, the research starts with a literature review of various methods used to evaluate the soil shear strength. Comparing to the machine learning methods, the laboratory and in-situ test methods are relatively more time-consuming, costly and labour-intensive. And the analytical methods are considered lacking in precision. Then the training dataset which consists of 526 samples is introduced. In each sample, there are four input variables obtained from cone penetration test, namely the effective stress (σ′v ), cone tip resistance (qt − σv), effective cone tip resistance (qt − u2) and the excess pore pressure (u2 − u0). The undrained shear strength obtained from laboratory test is taken as the output variable. Next, the training dataset is fed to five machine learning techniques, namely the artificial neural network, support vector machine, Gaussian process regression, random forest and XGBoost, to train models. The hyperparameters are tuned with k-fold and group k-fold cross-validation strategies in the validation process. After that, the testing dataset which consists of 20 samples is established. Cone penetration test data that are in close vicinity to the location of the samples are processed by Gaussian process regression to obtain representative cone penetration test data at the sample location, which is taken as the inputs in the testing dataset. The undrained shear strengths of the samples are measured by Consolidated-Undrained shear test and are taken as the outputs of the testing dataset. Finally, the five machine learning models are tested on the testing dataset. The crossvalidation results, together with the prediction results of the models on the training and testing dataset are evaluated, gathered and compared by various statistic metrics to show the relative performance of the models. XGBoost appears to be the most accurate of all the tested algorithms on this dataset. And Gaussian process regression is chosen as the second option due to its ability to capture uncertainties. The robustness of these two models are then validated from a statistical point of view by applying Monte Carlo analysis. The importance of the input parameters in this study is evaluated by applying random forest for the sensitivity analysis. The results from random forest indicate that the excess pore pressure and the cone tip resistance - total vertical stress are the most influential inputs to the undrained shear strengthGeo-Engineerin
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