scholarly journals Mix proportion design of liquid soil material suitable for abutment back filling

Author(s):  
Chongwei Huang ◽  
Meixuan Zhu ◽  
Yi Zhang ◽  
Kailun Hu
2007 ◽  
Vol 42 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Masahiro HOSODA ◽  
Yusuke YAMAMOTO ◽  
Kazumasa HARADA ◽  
Toshinari KORI ◽  
Masahiro FUKUSHI ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Young-Mi Oh ◽  
Paul V. Nelson ◽  
Dean L. Hesterberg ◽  
Carl E. Niedziela

A soil material high in crystalline Fe hydrous oxides and noncrystalline Al hydrous oxides collected from the Bw horizon of a Hemcross soil containing allophane from the state of Oregon was charged with phosphate-P at rates of 0, 2.2, and 6.5 mg·g−1, added to a soilless root medium at 5% and 10% by volume, and evaluated for its potential to supply phosphate at a low, stable concentration during 14 weeks of tomato (Solanum esculentumL.) seedling growth. Incorporation of the soil material improved pH stability, whether it was charged with phosphate or not. Bulk solution phosphate-P concentrations in the range of 0.13 to 0.34 mg·dm−3were associated with P deficiency. The only treatment that sustained an adequate bulk solution concentration of phosphate-P above 0.34 mg·dm−3for the 14 weeks of testing contained 10% soil material charged with 6.5 mg·g−1P, but initial dissolved P concentrations were too high (>5 mg·g−1phosphate-P) from the standpoint of phosphate leaching. The treatment amended with 10% soil material charged with 2.2 mg·g−1P maintained phosphate-P within an acceptable range of 0.4 to 2.3 mg·dm−3for 48 d in a medium receiving no postplant phosphate fertilization.


2020 ◽  
Vol 27 (1) ◽  
pp. 291-298
Author(s):  
Shoukai Chen ◽  
Yongqiwen Fu ◽  
Lei Guo ◽  
Shifeng Yang ◽  
Yajing Bie

AbstractA data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.


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