Developing of neuro-swarm system to estimate the undrained shear strength of soil by CPT data

Abstract

In this study, a novel hybrid metaheuristic model was developed to forecast the undrained soil shear (USS) property from cone penetration test (CPT) data (data from bore log sample from 70 different sites in Louisiana). This algorithm produced with the integration of grey wolf optimization (GWO) and multilayer perceptron neural network (MLP), named GWO - MLP, where different numbers of hidden layers were tested (1 to 4). The duty of optimization algorithm was to determine the optimal number of neurons in each hidden layer. To this objective, the system comprised five inputs entitled sleeve friction, cone tip persistence, liquid limit, plastic limitation, too much weight, and USS as outcome. The developed models for forecasting the USS of soil show the proposed best models have R2 at 0.9134 and 0.9236 in the training and predicting stage. Although the total ranking score of GWO-MLP2 and GWO-MLP4 is equal, the OBJ value shows that GWO-MLP4 has better performance than GWO-MLP2. In this case, considering the time of model running and a greater number of hidden layers suggests that GWO-MLP2 could be most appropriate. Therefore, the GWO-MLP3 model outperforms other GWO-MLP networks in the training and testing phase.