Predictive models for the engineering properties of some Quaternary clay deposits in the U.S.S.R

1991 ◽  
Vol 7 (1) ◽  
pp. 687-694
Author(s):  
E. I. Tkachuk
Keyword(s):  
Predictive Models ◽  
Physical Property ◽  
Engineering Properties ◽  
Behaviour Pattern ◽  
Model Parameters ◽  
Deformation Characteristics ◽  
Predictive Values ◽  
Rock Types ◽  
Clay Deposits ◽  
Type Form

AbstractThe influence of indicators of composition, induration state and physical property on strength (durability) and deformation characteristics of genetically dissimilar Quaternaryclay rocks has been investigated. About 200 samples describing specific territorial geological-genetic rock types and including from 18 to 980 values for each of between 12 to 60 property indicators are analyzed. The type, form and closeness of relationships are considered together with the quantitative behaviour pattern of the most important property variations. From this the most informative indirect indicators are determined, property behaviour Statistical models are obtained and the predictive values of the model parameters are analyzed.

scholarly journals Predictive models to determine the behavior of plastic and liquid limit of Lateratic soil for Raod construction at Egbema: Imo state of Nigeria

2012 ◽  
Vol 2 (1) ◽  
pp. 25 ◽  
Author(s):  
Solomon Ndubuisi Eluozo ◽  
C. Nwaobakata
Keyword(s):  
Predictive Models ◽  
Design Parameter ◽  
Performance Testing ◽  
Road Construction ◽  
Liquid Limit ◽  
Engineering Properties ◽  
Lateritic Soil ◽  
Design Parameters ◽  
Predictive Values ◽  
Laboratory Results

Predictive values to determine the behaviour of plastic and liquid of lateritic soil for road design and construction has been developed, this two parameters has a relationship in term of soil classification through their laboratory results, both parameters sample were subjected to through laboratory analysis for several locations, the laboratory results of both parameters were plotted to determine there behavious and limits, the results generated equations at various locations, the equations were resolved and it generated theoretical values, the model values displayed there relationship on the classification of soil for road construction. The implementation of a laboratory testing proceedure is to classify subgrade material and assess sustain properties and moisture sensitivity (heavy, collapse, softening) that can influence long-term pavement performance. Testing implementation consists of classification testing, these are (i.e., gradation analysis, Atterberg Limits and sulfate tests). Generated predictive models producing theoretical values, are engineering properties of soil testing for various parameters (i.e., swell/consolidation, R-value, unconfined compressive strength, California Bearing Ratio, and Resilient Modulus Tests). This are also design parameters that should generate predictive models that can be applied as design parameter in the study area, the study is imperative because predictive models generated and validated has ascertain proof of the workability of the models as design parameter in design of flexible pavements in the study location.

Joint inversion of potential-fields data over the DO-27 kimberlite pipe using a Gaussian mixture model prior

2020 ◽  
Vol 8 (4) ◽  
pp. SS47-SS62
Author(s):  
Thibaut Astic ◽  
Dominique Fournier ◽  
Douglas W. Oldenburg
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Joint Inversion ◽  
Gravity Data ◽  
Physical Property ◽  
Gaussian Mixture ◽  
Kimberlite Pipe ◽  
Magnetic Data ◽  
High Porosity ◽  
Rock Types

We have carried out petrophysically and geologically guided inversions (PGIs) to jointly invert airborne and ground-based gravity data and airborne magnetic data to recover a quasi-geology model of the DO-27 kimberlite pipe in the Tli Kwi Cho (also referred to as TKC) cluster. DO-27 is composed of three main kimberlite rock types in contact with each other and embedded in a granitic host rock covered by a thin layer of glacial till. The pyroclastic kimberlite (PK), which is diamondiferous, and the volcanoclastic kimberlite (VK) have anomalously low density, due to their high porosity, and weak magnetic susceptibility. They are indistinguishable from each other based upon their potential-field responses. The hypabyssal kimberlite (HK), which is not diamondiferous, has been identified as highly magnetic and remanent. Quantitative petrophysical signatures for each rock unit are obtained from sample measurements, such as the increasing density of the PK/VK unit with depth and the remanent magnetization of the HK unit, and are represented as a Gaussian mixture model (GMM). This GMM guides the PGI toward generating a 3D quasi-geology model with physical properties that satisfies the geophysical data sets and the petrophysical signatures. Density and magnetization models recovered individually yield volumes that have physical property combinations that do not conform to any known petrophysical characteristics of the rocks in the area. A multiphysics PGI addresses this problem by using the GMM as a coupling term, but it puts a volume of the PK/VK unit at a location that is incompatible with geologic information from drillholes. To conform to that geologic knowledge, a fourth unit is introduced, PK-minor, which is petrophysically and geographically distinct from the main PK/VK unit. This inversion produces a quasi-geology model that presents good structural locations of the diamondiferous PK unit and can be used to provide a resource estimate or decide the locations of future drillholes.

A Hierarchical Model Validation of Predictive Models for Engineering Product Development

2009 ◽  
Author(s):  
Byeng D. Youn ◽  
Byung C. Jung ◽  
Zhimin Xi ◽  
Sang Bum Kim
Keyword(s):  
Model Validation ◽  
Computer Model ◽  
Hierarchical Model ◽  
Predictive Models ◽  
Likelihood Function ◽  
Hierarchical Level ◽  
Model Parameters ◽  
Bottom Up ◽  
Hierarchy Level ◽  
Statistical Calibration

As the role of predictive models has increased, the fidelity of computational results has been of great concern to engineering decision makers. Often our limited understanding of complex systems leads to building inappropriate predictive models. To address a growing concern about the fidelity of the predictive models, this paper proposes a hierarchical model validation procedure with two validation activities: (1) validation planning (top-down) and (2) validation execution (bottom-up). In the validation planning, engineers define either the physics-of-failure (PoF) mechanisms or the system performances of interest. Then, the engineering system is decomposed into subsystems or components of which computer models are partially valid in terms of PoF mechanisms or system performances of interest. Validation planning will identify vital tests and predictive models along with both known and unknown model parameter(s). The validation execution takes a bottom-up approach, improving the fidelity of the computer model at any hierarchical level using a statistical calibration technique. This technique compares the observed test results with the predicted results from the computer model. A likelihood function is used for the comparison metric. In the statistical calibration, an optimization technique is employed to maximize the likelihood function while determining the unknown model parameters. As the predictive model at a lower hierarchy level becomes valid, the valid model is fused into a model at a higher hierarchy level. The validation execution is then continued for the model at the higher hierarchy level. A cellular phone is used to demonstrate the hierarchical validation of predictive models presented in this paper.

Reservoir Physical Property and Controlling Factors for the Lower Youshashan Formation in Youshashan-Dawusi Area, Southwestern Qaidam Basin

2014 ◽  
Vol 675-677 ◽  
pp. 1363-1367 ◽  
Author(s):  
Guo Min Chen ◽  
Quan Wen Liu ◽  
Min Quan Xia ◽  
Xiang Sheng Bao
Keyword(s):  
Qaidam Basin ◽  
Sedimentary Environment ◽  
Physical Property ◽  
Controlling Factors ◽  
Reservoir Rock ◽  
High Porosity ◽  
Delta Front ◽  
Thin Sections ◽  
Rock Types ◽  
Porosity And Permeability

The core data, casting thin sections and scanning electron microscopy are used to study the clastic reservoir characteristics and controlling factors of reservoir growth. It indicated that the main reservoir rock types are lithic arkose, Feld spathic sandstone, and a small amount of feldspar lithic sandstone, and with compositional maturity and low to middle structural maturity. Moreover, the primary reservoir space types are mainly intergranular pores, secondary are secondary pores, and reservoir types belong to the medium-high porosity and permeability, and the average porosity and permeability of lower Youshashan formation are 17.70% and 112.5×10-3μm2 separately. Furthermore, the reservoir body is mainly sand body result from deposits of distributary channel and mouth bar of which belong to the braided delta front, and the planar physical property tends to be better reservoir to worse reservoir from northwest to southeast. Finally, mainly factors to control the distribution of reservoir physical property, are the sedimentary environment and lithology, were worked out.

Static and Dynamic Performance Analysis and Improvement of Pillar in Vertical Drilling Machine

2011 ◽  
Vol 48-49 ◽  
pp. 1106-1111
Author(s):  
Wei Huang ◽  
Jun Qiu ◽  
Xiao Hui Zhu
Keyword(s):  
Finite Element ◽  
Natural Frequencies ◽  
Dynamic Performance ◽  
Structural Deformation ◽  
Model Parameters ◽  
Drilling Machine ◽  
Deformation Characteristics ◽  
Pillar Structure ◽  
Dynamic Performance Analysis ◽  
Element Theory

Based on finite element theory, pillar structure of the model parameters was established, and analyzed its static and dynamic performance. At the same time, the structural deformation of pillar load and deformation characteristics were analyzed. The choosing shape method was used to get the improvements in pillar. The Improved models were done modal analysis, and got their natural frequencies.

scholarly journals Prediction of violent crime on discharge from secure psychiatric hospitals: A clinical prediction rule (FoVOx)

2018 ◽  
Vol 47 ◽  
pp. 88-93 ◽  
Author(s):  
A. Wolf ◽  
T.R. Fanshawe ◽  
A. Sariaslan ◽  
R. Cornish ◽  
H. Larsson ◽  
...  
Keyword(s):  
Violent Crime ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Prediction Rule ◽  
Psychiatric Hospitals ◽  
Violence Risk ◽  
Post Discharge ◽  
Specific Patient ◽  
Predictive Values

AbstractBackgroundCurrent approaches to assess violence risk in secure hospitals are resource intensive, limited by accuracy and authorship bias and may have reached a performance ceiling. This study seeks to develop scalable predictive models for violent offending following discharge from secure psychiatric hospitals.MethodsWe identified all patients discharged from secure hospitals in Sweden between January 1, 1992 and December 31, 2013. Using multiple Cox regression, pre-specified criminal, sociodemographic, and clinical risk factors were included in a model that was tested for discrimination and calibration in the prediction of violent crime at 12 and 24 months post-discharge. Risk cut-offs were pre-specified at 5% (low vs. medium) and 20% (medium vs. high).ResultsWe identified 2248 patients with 2933 discharges into community settings. We developed a 12-item model with good measures of calibration and discrimination (area under the curve = 0.77 at 12 and 24 months). At 24 months post-discharge, using the 5% cut-off, sensitivity was 96% and specificity was 21%. Positive and negative predictive values were 19% and 97%, respectively. Using the 20% cut-off, sensitivity was 55%, specificity 83% and the positive and negative predictive values were 37% and 91%, respectively. The model was used to develop a free online tool (FoVOx).InterpretationWe have developed a prediction score in a Swedish cohort of patients discharged from secure hospitals that can assist in clinical decision-making. Scalable predictive models for violence risk are possible in specific patient groups and can free up clinical time for treatment and management. Further evaluation in other countries is needed.FundingWellcome Trust (202836/Z/16/Z) and the Swedish Research Council. The funding sources had no involvement in writing of the manuscript or decision to submit or in data collection, analysis or interpretation or any aspect pertinent to the study.

Research on Cataclastic Rock Bolting Control

2015 ◽  
Vol 730 ◽  
pp. 117-121
Author(s):  
Pei Xin Dong ◽  
Zhi Qiang Yang ◽  
Qian Gao ◽  
Hai Zhi Ma ◽  
Xiao Guang Wu ◽  
...  
Keyword(s):  
Mining Area ◽  
Difficult Problem ◽  
Engineering Properties ◽  
Deformation Characteristics ◽  
Joint Development ◽  
Rock Bolting ◽  
Rock Structure ◽  
Cataclastic Rock ◽  
Supporting Effect ◽  
The Stability

Due to the complexity of cataclastic rock structure, joint development, cataclastic rock bolting is a difficult problem to solve. According to roadway engineering properties and deformation characteristics of liu qiao one ore two level six mining area-380 transport cross section, choose one supporting form, adopt the method of numerical simulation to analysis its supporting effect, to ensure the stability of surrounding rock and the safe running of the roadway.

scholarly journals CHARACTERIZATION OF UJOGBA CLAY DEPOSITS IN EDO STATE, NIGERIA FOR REFRACTORY APPLICATIONS

2016 ◽  
Vol 12 (2) ◽  
pp. 71-82
Author(s):  
Z Elakhame ◽  
S. A. Bello ◽  
J. O. Agunsoye ◽  
I. Patric ◽  
O. Otitoju
Keyword(s):  
Chemical Composition ◽  
Physical Property ◽  
Mineralogical Composition ◽  
Linear Shrinkage ◽  
Crushing Strength ◽  
Clay Deposits ◽  
Refractory Property ◽  
Cold Crushing Strength ◽  
Edo State

 Ujogba clay deposits in Esan Land, Edo State of Nigeria were characterized for refractory and other applications. The characteristics investigated were mineralogical composition, chemical composition, linear shrinkage, apparent porosity, bulk density, cold crushing strength and density. The physical property tests were carried out in accordance with ASTM C71. Results obtained showed that the Ujogba clays are largely kaolinite and chemically composed of mainly silica (68.93 %) and alumina (24.61 %). These oxides appreciated to 71.04 % (silica) and 19.63 % (alumina) in the fired product; yet the ratio of alumina to silica remained unchanged. The physical property such as refractory property examination confirmed that the clays belong to the fireclay class and are useful for refractory and ceramic applications. Kathmandu UniversityJournal of Science, Engineering and TechnologyVol. 12, No. 2, 2016, page: 71-82  

scholarly journals Predicting microwave-induced relative volatility change by a novel dimensionless number

2020 ◽  
Author(s):  
Zhenyu Zhao ◽  
Hong Li ◽  
Guanlun Sun ◽  
Xinhui Tang ◽  
Haoyu Wu ◽  
...  
Keyword(s):  
Liquid Solution ◽  
Quantitative Modeling ◽  
Dimensionless Number ◽  
Model Parameters ◽  
Relative Volatility ◽  
Predictive Values ◽  
Quantitative Correlation ◽  
Properties Of Materials ◽  
Experimental Values ◽  
Further Development

Microwave (MW) can selectively interact with polar molecules in homogeneous mixture liquid solution and therefore change their relative volatility, which has been previously observed in experimental results for various binary mixtures. However, the lack of theoretical analysis and quantitative modeling hinders the further development of novel MW-assisted separation techniques. Hence, this study derived a novel dimensionless number ZMW based on the assumption of molecular irradiation to explore the effect of the dielectric and thermodynamic properties of materials as well as MW field intensity on the microwave-induced relative volatility change (MIRVC). Furthermore, a quantitative correlation between MIRVC and ZMW was established, whose model parameters were determined by fitting experimental data under MW irradiation. The correlation was also utilized to predict MIRVC in the previously published literature and the error range between predictive values and experimental values was within ± 6%, indicating great validity of the proposed quantitative correlation.

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