scholarly journals Influence of Degree of Compaction on Unsaturated Hydraulic Properties of a Compacted Completely Decomposed Granite

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Chen ◽  
Runqiang Tan ◽  
Zhongkui Chen ◽  
Yang Ping ◽  
Zhen Mei
Keyword(s):  
Hydraulic Properties ◽  
Prediction Models ◽  
Mercury Intrusion Porosimetry ◽  
Characteristic Curve ◽  
Southeastern China ◽  
Permeability Function ◽  
Unsaturated Permeability ◽  
Decomposed Granite ◽  
Engineering Projects ◽  
Completely Decomposed Granite

It is crucial to understand hydraulic properties, i.e., soil-water characteristic curve (SWCC) and unsaturated permeability function (UPF), of completely decomposed granite (CDG) for relevant engineering projects in southeastern China. Previous studies mainly focused on SWCCs of CDG, whereas UPFs of CDG have not yet been well understood. In this study, the effects of the degree of compaction (DOC) on SWCCs and UPFs of CDG were investigated based on experiments where suction range was from 0 to 500 kPa. The microstructure of soil specimens was then analyzed by mercury intrusion porosimetry (MIP). Furthermore, the UPFs of CDG under different values of DOC were calculated using four prediction models and compared with experimental data. Results showed that the pore volume of specimens at higher DOC was smaller than that at lower DOC, and there were more macropores observed in specimens at lower DOC. Meanwhile, it was found that increasing compaction effort produced negligible influence on the volume of micropores. When the suction was less than 100 kPa, the permeability was reduced with the increase in DOC, due to the decrease of macropore volume. However, the influence of DOC on SWCCs and UPFs became marginal when the suction exceeded 100 kPa. The Fredlund and Xing model provided the best prediction of UPF among the four models when suction was smaller than air entry value (AEV). It is suggested that these models could be improved to capture UPFs at higher suctions than AEV by considering suction-induced volume contraction.

scholarly journals Examination of the estimation of relative permeability for unsaturated soils

2015 ◽  
Vol 52 (12) ◽  
pp. 2077-2087 ◽  
Author(s):  
Feixia Zhang ◽  
D.G. Fredlund
Keyword(s):  
Lower Limit ◽  
Unsaturated Soils ◽  
Integral Formula ◽  
Characteristic Curve ◽  
Soil Conditions ◽  
Mathematical Algorithm ◽  
Soil Systems ◽  
Permeability Function ◽  
Unsaturated Permeability ◽  
Flexible Integration

The unsaturated permeability function is an important soil property function used in the numerical modeling of saturated–unsaturated soil systems. The permeability function is generally predicted by integrating along the soil-water characteristic curve (SWCC) starting at saturated soil conditions. The integration is based on a particular integral formula. The Fredlund–Xing–Huang permeability function is a flexible integration technique used for calculating the unsaturated permeability function. The original permeability theory published by Fredlund, Xing, and Huang in 1994 specified that the air-entry value (AEV), ψaev, be used as the lower limit of the integration when calculating the permeability function. However, as there was no analytical procedure available for the calculation of the AEV on the SWCC, it became common practice to start the integration procedure from a value near zero. The assumption was made that the error associated with starting the integration from an arbitrary low value was minimal. While this might be the case in some situations, the error can be quite substantial in other situations. This paper undertakes a study of the effect of the lower limit of integration on the calculation of the permeability function. Comparisons are made between starting the integration from various values below the AEV and starting the integration from the calculated AEV, ψaev. A mathematical algorithm is also proposed for the calculation of the AEV for integration purposes. The results show that the relative coefficient of permeability can be significantly underestimated when the lower limit of integration is smaller than the AEV. The recommendation is that the AEV always be used as the lower limit of integration in the Fredlund–Xing–Huang permeability equation.

scholarly journals New approach to improve soil-water characteristic curve to reduce variation in estimation of unsaturated permeability function

2016 ◽  
Vol 53 (4) ◽  
pp. 717-725 ◽  
Author(s):  
Arezoo Rahimi ◽  
Harianto Rahardjo
Keyword(s):  
Soil Water ◽  
Distribution Curve ◽  
Characteristic Curve ◽  
New Approach ◽  
Soil Water Characteristic Curve ◽  
Permeability Function ◽  
Wide Range ◽  
Unsaturated Permeability ◽  
Complete Measurement ◽  
Data Points

The unsaturated permeability function is often estimated from the soil-water characteristic curve (SWCC) of a soil. A complete SWCC measurement can improve the estimation of the unsaturated permeability function. In most laboratories, the SWCC can be measured up to a suction of 100 kPa using a Tempe cell. However, complete measurement of the SWCC is an expensive and time-consuming task. Therefore, this paper presents a new approach to estimate SWCC data points beyond 100 kPa suction to complement the SWCC measured up to a suction of 100 kPa. The new SWCC is then used to estimate the unsaturated permeability function. The proposed approach uses knowledge of the grain-size distribution curve and measured SWCC data at 100 kPa suction to estimate the SWCC data points beyond 100 kPa suction. To verify the proposed procedure, SWCC tests were conducted over a wide range of suctions for coarse kaolin and a triaxial permeameter system was used to directly measure unsaturated permeability of the coarse kaolin. The proposed procedure is found to reduce the variation between unsaturated permeability functions estimated by various estimation models.

Shear strength characteristics of two saprolitic soils

1996 ◽  
Vol 33 (4) ◽  
pp. 595-609 ◽  
Author(s):  
Julian K-M Gan ◽  
D G Fredlund
Keyword(s):  
Shear Strength ◽  
Characteristic Curve ◽  
Matric Suction ◽  
Coarse Grained ◽  
Triaxial Tests ◽  
Direct Shear ◽  
Strength Behavior ◽  
Decomposed Granite ◽  
Insight Into ◽  
Completely Decomposed Granite

The saturated and unsaturated shear strength behavior of an undisturbed, completely decomposed fine ash tuff and an undisturbed, completely decomposed granite from Hong Kong were studied using direct shear and triaxial tests. The completely decomposed fine ash tuff is a fine- to medium-grained saprolite. The completely decomposed granite is a coarse-grained saprolite. Results show that matric suction increases the shear strength of both soils. The extent of the increase is the shear strength with matric suction is related to the soil-water characteristic curve for the soil and to the amount of dilation during shear. The effect of matric suction on the shear strength was more pronounced for the fine- to medium-grained completely decomposed fine ash tuff than for the coarse-grained completely decomposed granite. These studies on the saprolitic soils provide insight into the understanding of the shear strength of unsaturated, coarse-grained soils. Key words: saprolites, shear strength, matric suction, triaxial, direct shear, coarse-grained soils.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

Shear strength and dilative characteristics of an unsaturated compacted completely decomposed granite soil

2010 ◽  
Vol 47 (10) ◽  
pp. 1112-1126 ◽  
Author(s):  
Md. Akhtar Hossain ◽  
Jian-Hua Yin
Keyword(s):  
Shear Strength ◽  
Normal Stress ◽  
Matric Suction ◽  
Water Retention Curve ◽  
Direct Shear ◽  
Normal Stresses ◽  
Soil Water Retention Curve ◽  
Strength Data ◽  
Decomposed Granite ◽  
Completely Decomposed Granite

Shear strength and dilative characteristics of a re-compacted completely decomposed granite (CDG) soil are studied by performing a series of single-stage consolidated drained direct shear tests under different matric suctions and net normal stresses. The axis-translation technique is applied to control the pore-water and pore-air pressures. A soil-water retention curve (SWRC) is obtained for the CDG soil from the equilibrium water content corresponding to each applied matric suction value for zero net normal stress using a modified direct shear apparatus. Shear strength increases with matric suction and net normal stress, and the failure envelope is observed to be linear. The apparent angle of internal friction and cohesion intercept increase with matric suction. A greater dilation angle is found at higher suctions with lower net normal stresses, while lower or zero dilation angles are observed under higher net normal stresses with lower suctions, also at a saturated condition. Experimental shear strength data are compared with the analytical shear strength results obtained from a previously modified model considering the SWRC, effective shear strength parameters, and analytical dilation angles. The experimental shear strength data are slightly higher than the analytical results under higher net normal stresses in a higher suction range.

scholarly journals A new approach to categorising continuous variables in prediction models: Proposal and validation

2015 ◽  
Vol 26 (6) ◽  
pp. 2586-2602 ◽  
Author(s):  
Irantzu Barrio ◽  
Inmaculada Arostegui ◽  
María-Xosé Rodríguez-Álvarez ◽  
José-María Quintana
Keyword(s):  
Prediction Models ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Real Data ◽  
Clinical Variable ◽  
Chronic Obstructive ◽  
Continuous Variables ◽  
Discriminative Ability ◽  
Data Set ◽  
Statistical Point

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points’ location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.

scholarly journals Algorithmic and data modeling: Will algorithmic modeling improve predictions of traits evaluated on ordinal scales?

2020 ◽  
Author(s):  
Zhanyou Xu ◽  
Andreomar Kurek ◽  
Steven B. Cannon ◽  
Williams D. Beavis
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Ridge Regression ◽  
Genomic Prediction ◽  
Ordinal Data ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Types

AbstractSelection of markers linked to alleles at quantitative trait loci (QTL) for tolerance to Iron Deficiency Chlorosis (IDC) has not been successful. Genomic selection has been advocated for continuous numeric traits such as yield and plant height. For ordinal data types such as IDC, genomic prediction models have not been systematically compared. The objectives of research reported in this manuscript were to evaluate the most commonly used genomic prediction method, ridge regression and it’s equivalent logistic ridge regression method, with algorithmic modeling methods including random forest, gradient boosting, support vector machine, K-nearest neighbors, Naïve Bayes, and artificial neural network using the usual comparator metric of prediction accuracy. In addition we compared the methods using metrics of greater importance for decisions about selecting and culling lines for use in variety development and genetic improvement projects. These metrics include specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. We found that Support Vector Machine provided the best specificity for culling IDC susceptible lines, while Random Forest GP models provided the best combined set of decision metrics for retaining IDC tolerant and culling IDC susceptible lines.

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