Non-Linear Multivariate Analysis with Artificial Neural Network in Estimating Compression Index for Cohesive Soils of Northern Jakarta Coast - M Addifa Yulman; Shandy Yudha Isa; Miftahurrohman; Aditya Hadyan Putra; Mulia, Andri; Elyada Eben Ezer; Darmadi, Kenandio (Indonesian Geotechnical Journal, 2024)
This study presents a novel application of artificial neural network (ANN) to develop a model for predicting compression index (Cc) of cohesive soils from their index properties. The model was trained using data from 347 undisturbed samples on a variety of cohesive soils from Northern Jakarta. It takes up to three variables as inputs: specific gravity (Gs), liquid limit (LL), and plastic limit (PL). The model was tested on a separate dataset of 117 samples and found to have a strong capability to predict Cc values when compared to some reference correlations. The ANN model has demonstrated good performance for each set by producing overall error of 29.6%, compared to 38.1% and 30.5% for the empirical formulas. This study shows that the application of ANN offers an essential advancement in this area, helping to overcome the limitation of conventional statistical correlation.