A novel heuristic and tunicate centered ANFIS and RCCRD optimization for soil nailing using a numerical approach

Abstract

To assess performance along with stability, numerical simulations of Soil Nail Walls (SNWs) are regularly executed in practice. In this, failure is the major problem. To resolve the failure problem, prevailing techniques utilize different mechanisms. However, accuracy enhancement is still required. To enhance the performance, this proposed framework utilizes the Heuristic and Tunicate-centered Adaptive Neuro-Fuzzy Inference System (HT-ANFIS) algorithm-centric soil nailing. Here, the ground motion records are collected. Then, the Finite Element Model (FEM) is analyzed. Primarily, the parameters are extracted from the soil nailing modeling methodologies like Hardening Soil (HS), Hardening Soil small Strain (HSS), along with Mohr-Coulomb (MC) in this FEM. After that, these parameter values are given as input to the HT-ANFIS that predicts the Soil Nail (SN) modeling's Intensity Level (IL). The heuristic and Ranked Tunicate Optimization Algorithm (RTOA) methodologies are wielded in this HT-ANFIS. Hence, a better IL range is given by the HT-ANFIS from the IL range. By utilizing the Range Central Composite Rotable Design (RCCRD) mechanism, the optimal value is selected. The soil nailing is designed after the optimal value selection. In the experimental analysis, grounded on the performance metrics, the presented research work's performance is analyzed with the previous techniques. Also, the failure probability along with the model prediction is analyzed. Therefore, the analysis displays that in contrast to the conventional research approaches, the presented numerical modeling-centered soil nailing achieves better performance.