Advancing Pile-Bearing Capacity Prediction with Meta-Heuristic Enhanced Specific ANFIS strategys

In the wake of demands for efficiency, this investigation offers a novel strategy for the real-time prediction of PBC. This method is developed using specific ANFIS predictive schemes, sturdinessened by two robust meta-heuristic schemes: ARO and CryStal. The integration of these schemes enhances the accuracy of the prediction while simplifying the process. The following study presents three schemes: the ANCS, the ANAR, and the individual ANFIS strategy. Each model adds different aspects to the whole, synergistically enhancing overall precision in PBC prediction. This strategy signifies a radical improvement in PBC prediction strategies by introducing an effective and time-saving method with extensive geomechanical applications. Meta-heuristic schemes combined with specific ANFIS schemes show promising outcomes toward real-time PBC estimation in diverse geological scenarios. Notably, the statistical performance of the ANCS model is very impressive with an R^2 value of 0.999 for the whole database in a validation test and with a minimum RMSE of 49.24. Moreover, the ANCS model has displayed very good predictive and generalization performance compared to developed ANFIS and ANAR schemes, hence emphasizing its efficiency and applicability for real-world problems.