In Situ Skin Friction Capacity Modeling with Advanced Neuro-Fuzzy Optimized by Metaheuristic Algorithms

Abstract

Developing new optimization algorithms and data mining has improved traditional engineering structural analysis models (meaning basically swarm-based solutions). Additionally, an accurate quantification of in situ friction capacity (ISFC) of driven piles is of paramount importance in design/construction of geotechnical infrastructures. A number of studies have underscored the use of models developed via artificial neural networks (ANNs) in anticipation of the bearing capacity of driven piles. Nonetheless, the main drawbacks of implementing the techniques relying on artificial neural networks are their slow convergence rate and reliable testing outputs. The current research focused on establishing an accurate/reliable predictive network of ISFC. Therefore, an adaptive neuro-fuzzy inference system (ANFIS) coupled with Harris hawk optimization (HHO), salp swarm algorithm (SSA), teaching-learning-based optimization (TLBO), and water-cycle algorithm (WCA) is employed. The findings revealed that the four models could accurately assimilate the correlation of ISFC to the referenced parameters. The values of the root mean square error (RMSE) realized in the prediction phase were 8.2844, 7.4746, 6.6572, and 6.8528 for the HHO-ANFIS, SSA-ANFIS, TLBO-ANFIS, and WCA-ANFIS, respectively. The results depicted WCA-ANFIS as more accurate than the three other algorithms at the testing and training phase, and could probably be utilized as a substitute for laboratory/classical methods.

Keywords

• driven piles
• in situ friction capacity
• adaptive neuro-fuzzy inference system
• optimization schemes
• hybridization