Neural modelling of compactibility characteristics of cohesionless soil

  • Maria J. Sulewska Faculty of Civil and Environmental Engineering, Bialystok University of Technology

Abstract

Compaction is the method of in-situ soil modification to improve its engineering properties. Two key compactibility parameters are: the maximum dry density ρd max and the corresponding optimum water content wopt. They are basic parameters for designing, constructing and controlling the compaction quality of earth structures (e.g. earth dams, highway embankments). Soil compactibility can be determined from the laboratory compactibility curve basing on Proctor's test. However, this test is destructive, time-consuming and expensive. To facilitate the determination of the cohesionless soil compactibility parameters, correlations between ρd max and wopt and the basic parameters characterizing soil grain-size distribution (CU, D10, D20, D30, D40, D50, D60, D70, D80, and D90) were developed. Artificial neural networks are applied to determine models with good prediction quality. The neural models have higher accuracy than the classic statistical models.

Keywords

geotechnical engineering, cohesionless soil, compactibility characteristics, Artificial Neural Network,

References

Published
Jan 25, 2017
How to Cite
SULEWSKA, Maria J.. Neural modelling of compactibility characteristics of cohesionless soil. Computer Assisted Methods in Engineering and Science, [S.l.], v. 17, n. 1, p. 27–40, jan. 2017. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/135>. Date accessed: 16 sep. 2021.
Section
Articles