Regression Model for Resilient Modulus of Subgrade Soils in Shanghai

2011 ◽  
Vol 374-377 ◽  
pp. 1796-1799
Author(s):  
Hong Xi Liu ◽  
Liang Zhou
Keyword(s):  
Regression Model ◽  
Prediction Models ◽  
Resilient Modulus ◽  
Soil Samples ◽  
Soil Types ◽  
Model Parameters ◽  
Cohesive Soils ◽  
Subgrade Soils ◽  
The Past ◽  
Effective Design

Subgrade resilient modulus (MR) is very important for effective design of pavements. Several methods to estimate the resilient modulus were suggested in the past years. The main objective of this paper was to validate the correlation of MR with other physical properties of the subgrade soils. Cohesive soils representing major soil types in Shanghai were selected. The resilient modulus tests were conducted with UTM. Additional analysis was then performed to develop correlations between the model parameters and other soil properties. To verify the prediction models independently, laboratory MR tests were conducted on new subgrade soils. It was observed that the predicted MR values compared well with the laboratory measured values for the soil samples.

Model for predicting resilient modulus of unsaturated subgrade soil using soil-water characteristic curve

2015 ◽  
Vol 52 (10) ◽  
pp. 1605-1619 ◽  
Author(s):  
Zhong Han ◽  
Sai K. Vanapalli
Keyword(s):  
Moisture Content ◽  
Soil Water ◽  
Resilient Modulus ◽  
Characteristic Curve ◽  
Optimum Moisture Content ◽  
Model Parameters ◽  
Soil Water Characteristic Curve ◽  
Subgrade Soils ◽  
Fine Grained ◽  
Proposed Model

Soil suction (ψ) is one of the key factors that influence the resilient modulus (MR) of pavement subgrade soils. There are several models available in the literature for predicting the MR–ψ correlations. However, the various model parameters required in the existing models are generally determined by performing regression analysis on extensive experimental data of the MR–ψ relationships, which are cumbersome, expensive, and time-consuming to obtain. In this paper, a model is proposed to predict the variation of the MR with respect to the ψ for compacted fine-grained subgrade soils. The information of (i) the MR values at optimum moisture content condition (MROPT) and saturation condition (MRSAT), which are typically determined for use in pavement design practice; (ii) the ψ values at optimum moisture content condition (ψOPT); and (iii) the soil-water characteristic curve (SWCC) is required for using this model. The proposed model is validated by providing comparisons between the measured and predicted MR–ψ relationships for 11 different compacted fine-grained subgrade soils that were tested following various protocols (a total of 16 sets of data, including 210 testing results). The proposed model was found to be suitable for predicting the variation of the MR with respect to the ψ for all the subgrade soils using a single-valued model parameter ξ, which was found to be equal to 2.0. The proposed model is promising for use in practice, as it only requires conventional soil properties and alleviates the need for experimental determination of the MR–ψ relationships.

Regression Model for Resilient Modulus of Subgrade Soils

1999 ◽  
Vol 1687 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Louay N. Mohammad ◽  
Baoshan Huang ◽  
Anand J. Puppala ◽  
Aaron Allen
Keyword(s):  
Regression Model ◽  
Resilient Modulus ◽  
Subgrade Soils

Experimental Study of Dynamic Resilient Modulus of Cohesive Soils in Henan

2014 ◽  
Vol 971-973 ◽  
pp. 2092-2095
Author(s):  
Yu Peng Wang ◽  
Liang Zhou
Keyword(s):  
Experimental Study ◽  
Resilient Modulus ◽  
Henan Province ◽  
Additional Analysis ◽  
Cohesive Soils ◽  
Test Results ◽  
Stress Strain ◽  
Strain Behavior ◽  
The Past ◽  
Subgrade Soil

Subgrade soil is very important materials to support highways. Resilient modulus (MR) has been used for characterizing stress-strain behavior of base or subbase subjected to repeated traffic loadings. Several methods to estimate the resilient modulus were suggested in the past years. The main objective of this study was to test the resilient modulus in the laboratory. The Subgrade soil was selected in Henan province. Resilient modulus tests were conducted with UTM. Additional analysis was performed to discuss the factors of the test results.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
T Heseltine ◽  
SW Murray ◽  
RL Jones ◽  
M Fisher ◽  
B Ruzsics
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Coronary Artery Calcium ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Cvd Risk ◽  
Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

CPT-based liquefaction case histories compiled from three earthquakes in Canterbury, New Zealand

2021 ◽  
pp. 875529302199636
Author(s):  
Mertcan Geyin ◽  
Brett W Maurer ◽  
Brendon A Bradley ◽  
Russell A Green ◽  
Sjoerd van Ballegooy
Keyword(s):  
New Zealand ◽  
Case History ◽  
Prediction Models ◽  
Free Field ◽  
Case Histories ◽  
Cone Penetration ◽  
History Data ◽  
The Past ◽  
Surface Manifestation ◽  
The Many

Earthquakes occurring over the past decade in the Canterbury region of New Zealand have resulted in liquefaction case-history data of unprecedented quantity. This provides the profession with a unique opportunity to advance the prediction of liquefaction occurrence and consequences. Toward that end, this article presents a curated dataset containing ∼15,000 cone-penetration-test-based liquefaction case histories compiled from three earthquakes in Canterbury. The compiled, post-processed data are presented in a dense array structure, allowing researchers to easily access and analyze a wealth of information pertinent to free-field liquefaction response (i.e. triggering and surface manifestation). Research opportunities using these data include, but are not limited to, the training or testing of new and existing liquefaction-prediction models. The many methods used to obtain and process the case-history data are detailed herein, as is the structure of the compiled digital file. Finally, recommendations for analyzing the data are outlined, including nuances and limitations that users should carefully consider.

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