A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

Engineering with Computers - Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant...     

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

     

TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Neural Computing and Applications - A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in...     

A novel TS Fuzzy-GMDH model optimized by PSO to determine the deformation values of rock material

Neural Computing and Applications - Since determining the rock deformation directly in the laboratory is costly and time consuming, it is important to reliably determine/estimate this parameter...