Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.

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