A Survey on the Relationships between Compression Index, Coefficient of Consolidation, and Atterberg Limits

Abstract

Correlations between compression index and atterberg limits found in the literature are very important for preliminary estimation. These equations are usually interpreted based on the R-square parameter and classified according to the conditions of the data (disturbed, undisturbed, remoulded, etc.). Although correlations reliable enough to eliminate oedometer tests are not yet fully available, these correlations can be useful in local calculations. In this study, correlations obtained from studies conducted after 2000 on the relationship of compression index and consolidation coefficient with atterberg limits and water content are mentioned and clearly shown. While the compression index equations are quite high in the literature, the equations produced with the consolidation coefficient are less in number. This is because consolidation calculations take a lot of time. Using 105 data obtained from researches in the literature, two equations were formed between the compression index, liquid limit and plasticity index. This study does not propose new equations, only relationships are generated using the Linear Regression method with data obtained from independent studies, based on the belief that the compression index has a stronger relationship with the liquid limit and plasticity index

Non-Linear Multivariate Analysis with Artificial Neural Network in Estimating Compression Index for Cohesive Soils of Northern Jakarta Coast

“…Although empirical correlations can provide a quick and inexpensive way to estimate soil parameters with simple tests, most of these correlations are derived from fitting data measurement made under specific site condition. This may cause large deviations when used at other sites (Dehghanian & Ipek, 2022) 3 ARTIFICIAL NEURAL NETWORK MODEL 3.1 Artificial Neural Networks (ANNs) ANNs are artificial adaptive systems that are inspired by the functioning processes of human brain and nervous system (Grossi & Buscema, 2008). ANNs provide strong solutions to problems in several areas, including classification, prediction, filtering, optimization, pattern recognition, and function approximation.…”