Reza Tarinejad

TL;DR: This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimized (MFO) for estimation of the TBM penetration rate (PR).

TL;DR: The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models.

TL;DR: A modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement is introduced to reveal the capability level of this hybrid model in predicting pile settlement.

TL;DR: The developed soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile were developed and it was observed that the developed models are able to provide higher prediction performance in the design of Qult of the piles.

TL;DR: The results of this study can be used for optimum design of blasting pattern parameters for flyrock prediction through the use of random forest technique and the Bayesian network technique.