- Tytuł:
- Soft computing based prediction of friction angle of clay
- Autorzy:
-
Dutta, R. K.
Gnananandarao, T.
Ladol, S. - Powiązania:
- https://bibliotekanauki.pl/articles/1818506.pdf
- Data publikacji:
- 2020
- Wydawca:
- Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
- Tematy:
-
artificial neural network
sensitivity analysis
M5P model tree
multiregression analysis
friction angle of clay
sztuczna sieć neuronowa
analiza wrażliwości
drzewo modelu M5P
analiza wielokrotnej regresji - Opis:
- Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.
- Źródło:
-
Archives of Materials Science and Engineering; 2020, 104, 2; 58--68
1897-2764 - Pojawia się w:
- Archives of Materials Science and Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł