Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods - Arivalagan Pugazhendhi; Ha Manh Bui (Advances in Engineering and Intelligence Systems)
Empirically, soil compaction is an important aspect in the selection of materials for earth constructions. Due to time constraints and attention to completion resources, it is necessary to develop models to forecast compaction parameters (maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily measured index properties. The main purpose of this study is to scrutinize the applicability of using the hybrid adaptive neuro-fuzzy inference system (ANFIS) models for predicting the γdmax and ωopt related to the standard proctor compaction test of lateritic soils. Results present that both models have a reasonable performance in predicting the γdmax and ωopt with R2 larger than 0.9038 and 0.9692 for the training data, representing the acceptable correlation between measured and forecasted γdmax and ωopt. Regarding developed models, the ANFIS model optimized with whale optimization algorithm (WOA) has the best performance than imperialist competitive algorithm (ICA) model in both training and testing phases for predicting γdmax and ωopt.