Assessment of circular-bored twin tunnel (CBTT) performance using soft computing methods

Circular-bored tunnel (CBTT) machines are used as an alternative to drill and for blasting in rock and conventional "hand mining" in soil. The penetration rate (PR) prediction is a difficult issue plus the related analysis between CBTT and rock mass. Circular tunnels are high proposed for general projects. Experimental and analytical models in analyzing CBTT penetration ratio could not provide a reliable and valid prediction to civil projects and mining because of the nonlinear problems, related risks and high costs. This research provides a novel study of using three algorithms as support vector machine (SVM), grey wolf optimizer (GWO) and k -nearest neighbor (KNN) to predict the PR of CBTT. For this purpose, doing empirical analysis could be time consuming, lead to high cost, inaccuracy of results, and high error percentages. Accordingly, this study, by doing numerical analysis, has tried to accurately predict the PR of CBTT in terms of uniaxial compressive strength and rock quality designation were used as input data. It was followed by the development of SVM, KNN, and GWO to obtain the best predictive model. Accordingly, SVM with the best RMSE value of (0.183), R 2 value (0.914) and MAE value of (0.139) was selected as the best model among other two ones in this research, meaning that SVM could predict the PR of CBTT with more accuracy and reliably. Thus, SVM model identified uniaxial compressive strength (0.2) as a significant parameter in CBTT prediction and penetration ratio.