Simple Index Tests for Assessing the Recompression Index of Fine-Grained Soils

Kootahi, Karim

doi:10.1061/(asce)gt.1943-5606.0001624

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Similarly, the C s data (FI-CLAY/14/856) is marked with greater scatter compared to C c and CR. Nevertheless, the model suggested by Kootahi (2017) fits rather well with the data, and the bias factor b is approximately 1 (Table 2). On contrary, the relationship between PI and C s was characterised by larger scatter (see Figure S3 in the supplement); accordingly, calibration yielded very high δ values.…”

Section: Comparison Of Existing Transformation Models To Fi-clay/14/856mentioning

confidence: 74%

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Löfman

Korkiala-Tanttu

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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The compression index obtained from an oedometer test is often used to estimate the settlements of clayey subsoil, but compressibility parameters are rarely available during the preliminary geotechnical design phase. Various empirical correlations linking compressibility to other properties such as water content have been proposed. However, as Scandinavian clays are soft and exhibit greater compressibility, the existing transformation models for compressibility can be biased when applied to Finnish clays. This paper compiles a partial multivariate database of Finnish clayey soils and demonstrates that the existing transformation models tend to underestimate the compressibility of Finnish clays. The new transformation models are constructed by means of a 2-degree polynomial regression applied to the natural logarithms of the soil properties. Finally, the transformation uncertainties are quantified via the standard deviation of errors and the coefficient of variation. The best predictors for the compressibility of Finnish clayey soils were found to be the void ratio and water content. When the void ratio was combined with a secondary predictor, such as the ratio between undrained shear strength and preconsolidation pressure or plastic limit, the transformation uncertainty decreased notably.

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Section: Comparison Of Existing Transformation Models To Fi-clay/14/856mentioning

confidence: 74%

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Löfman

Korkiala-Tanttu

2021

Georisk: Assessment and Management of Risk for Engineered Syste

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“…In this study, we paid more attention to the uncertainty prevailing at a particular location (local uncertainty), rather than the uncertainty assessed at many locations simultaneously (multiple-point or spatial uncertainty). 19 However, currently most correlations whether based on simple algebraic, statistical methods or advanced ML methods, neglected uncertainty induced by measurement scatter (data from different laboratories/ resources) and limited number of samples, [20][21][22] meaning their predictions were deterministic without uncertainty evaluation. 23 Because direct application of their predicted results in engineering practice may pose risks, correlations that take uncertainty into account through ML are worth studying.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time‐dependent behavior and its application

Zhang

Jin

Yin

2021

Num Anal Meth Geomechanics

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Uncertainty is a commonplace and significant issue in geotechnical engineering. Unlike conventional statistical and machine learning methods, this study presents a novel approach to correlating soil properties that takes uncertainty into account using an artificial neural network with Monte Carlo dropout (ANN_MCD). An uncertainty model for two important soil properties, creep index C α, and hydraulic conductivity k, that control the long-term performance of geotechnical structures is proposed in a function of three soil physical properties using ANN_MCD. Evaluation of the accuracy, uncertainty, and monotonicity of the predicted results for both C α and k reveals the excellent performance of the proposed model, which is used to simulate the long-term settling and excess pore pressure of an embankment on soft clays. The predicted results show good agreement with observations, within a 95% confidence interval. All results indicate that the proposed ANN_MCD-based modelling approach can be used to rapidly correlate soil properties with an uncertainty evaluation and can be further combined with numerical modelling to analyze an engineering-scale problem and conduct risk assessment.

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“…Estimation of soil physical and mechanical properties is an inevitable step in geotechnical design for achieving the trade-off between the cost and engineering safety, such as the estimation of bearing capacity and long-term deformation., and Site-specific laboratory testing is the most direct way of obtaining soil properties. For a certain type of soils, empirical or semiempirical correlations have been developed for use in predicting soil properties [1][2][3][4][5][6][7]. However, these conventional regression-based correlations have limited prediction capabilities and cannot uncover the essential interconnections between input variables and the studied soil properties, hindering the development of a general prediction model and the use of existing data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison

Zhang

Yin

Jin

2021

Arch Computat Methods Eng

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Machine learning (ML) holds significant potential for predicting soil properties in geotechnical design but at the same time poses challenges, including those of how to easily examine the performance of an algorithm and how to select an optimal algorithm. This study first comprehensively reviewed the application of ML algorithms in modelling soil properties for geotechnical design. The algorithms were categorized into several groups based on their principles, and the main characteristics of these ML algorithms were summarized. After that six representative algorithms are further detailed and selected for the creation of a ML-based tool with which to easily build ML-based models. Interestingly, automatic determination of the optimal configurations of ML algorithms is developed, with an evaluation of model accuracy, application of the developed ML model to the new data and investigation of relationships between the input variables and soil properties. Furthermore, a novel ranking index is proposed for the model comparison and selection, which evaluates a ML-based model from five aspects.Soil maximum dry density is selected as an example to allow examination of the performance of different ML algorithms, the applicability of the tool and the model ranking index to determining an optimal model.

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Simple Index Tests for Assessing the Recompression Index of Fine-Grained Soils

Cited by 5 publications

References 6 publications

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Transformation models for the compressibility properties of Finnish clays using a multivariate database

Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time‐dependent behavior and its application

Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison

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