Modeling the compression index for fine soils using an intelligent method

Seyed Mahmood Kashefipour, Mehdi Daryaee

Hydraulic Structures Department, Water Sciences Engineering Faculty, Shahid Chamran University Ahwaz, Iran

Construction of buildings and different structures leads to soil consolidation, hence, causes soil settlement. Soil settlement depends on numerous factors, for instance, pressure deformation, depletion of pore water, and so forth.

One way to calculate soil settlement, is utilizing compression index, which is obtained through consolidation test. Obtaining this index through consolidation test is too time-consuming; thus, researchers have attempted to relate compression index to soil physical parameters such as plasticity limit, liquid limit, void ratio, and relative density, which all could be simply measured; therefore, there is great deal of empirical relations in this regard.

In this study, the correlation coefficients between the physical characteristics of fine soil and compression index were investigated using the Artificial Neural Network (ANN). A few but common empirical equations describing the relationship of compression index with other soil properties were evaluated along with the developed ANN model in this study.

The results have indicated that among the considered empirical relations, the Rendon-Herrero formula performed better in calculating the compression index.

By comparison, the ANN calculates the compression index more accurately and with less error than the Rendon-Herrero formula.
Full Article Available at: Volume 5, Number 5, November 2014 – JBES
Journal Name: Journal of Biodiversity and Environmental Sciences (JBES)