CBR Predictive Model Development from Soil Index and Compaction Properties in case of Fine-Grained Soils of Debre –Tabor City, Ethiopia

Author(s):  
F. G. Teklehaymanot ◽  
E. Alene
2021 ◽  
Vol 13 (14) ◽  
pp. 7737
Author(s):  
Amin Soltani ◽  
Mahdieh Azimi ◽  
Brendan C. O’Kelly

This study aims at modeling the compaction characteristics of fine-grained soils blended with sand-sized (0.075–4.75 mm) recycled tire-derived aggregates (TDAs). Model development and calibration were performed using a large and diverse database of 100 soil–TDA compaction tests (with the TDA-to-soil dry mass ratio ≤ 30%) assembled from the literature. Following a comprehensive statistical analysis, it is demonstrated that the optimum moisture content (OMC) and maximum dry unit weight (MDUW) for soil–TDA blends (across different soil types, TDA particle sizes and compaction energy levels) can be expressed as universal power functions of the OMC and MDUW of the unamended soil, along with the soil to soil–TDA specific gravity ratio. Employing the Bland–Altman analysis, the 95% upper and lower (water content) agreement limits between the predicted and measured OMC values were, respectively, obtained as +1.09% and −1.23%, both of which can be considered negligible for practical applications. For the MDUW predictions, these limits were calculated as +0.67 and −0.71 kN/m3, which (like the OMC) can be deemed acceptable for prediction purposes. Having established the OMC and MDUW of the unamended fine-grained soil, the empirical models proposed in this study offer a practical procedure towards predicting the compaction characteristics of the soil–TDA blends without the hurdles of performing separate laboratory compaction tests, and thus can be employed in practice for preliminary design assessments and/or soil–TDA optimization studies.


2021 ◽  
Author(s):  
Lizong Deng ◽  
Luming Chen ◽  
Tao Yang ◽  
Mi Liu ◽  
Shicheng Li ◽  
...  

UNSTRUCTURED In “Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study” (J Med Internet Res 2021;23(6):e26892) the authors noted one error. The institution name of affiliation “Suzhou Institute of Systems Medicine” was not correct. It should be corrected from “Suzhou Institute of Systems Medicine” to “Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College; Suzhou Institute of Systems Medicine”


2020 ◽  
Vol 51 (4) ◽  
pp. 648-665
Author(s):  
Min Wu ◽  
Qi Feng ◽  
Xiaohu Wen ◽  
Ravinesh C. Deo ◽  
Zhenliang Yin ◽  
...  

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.


2012 ◽  
Vol 33 (8) ◽  
pp. 723-739 ◽  
Author(s):  
Sebastian Polak ◽  
Barbara Wiśniowska ◽  
Anna Glinka ◽  
Kamil Fijorek ◽  
Aleksander Mendyk

2015 ◽  
Vol 137 (4) ◽  
pp. 2283-2283
Author(s):  
David P. Knobles ◽  
Steven A. Stotts ◽  
Robert A. Koch

2015 ◽  
Vol 15 (2) ◽  
pp. 283-288 ◽  
Author(s):  
M.F.M. Abushammal ◽  
N.E.A. Basri ◽  
M.K. Younes

2021 ◽  
Vol 11 (19) ◽  
pp. 9258
Author(s):  
Maria Ganopoulou ◽  
Ioannis Kangelidis ◽  
Georgios Sianos ◽  
Lefteris Angelis

Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development.


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