Developing the Rule of Thumb for Evaluating Penetration Rate of TBM, Using Binary Classification

Using the tunnel boring machine (TBM) in tunneling projects contributes significantly to increased efficiency and reducing the time of project implementation in comparison with the classical methods. Since the scheduled deadline is a major issue in the mechanized tunneling project, factors that affect the performance of TBM must be deeply considered in the assessment of tunneling operations. In the implementation of the mechanized tunneling project, a key variable is to predict the penetration rate of TBM. The main aim of this study is to predict the penetration rate of TBM in a novelty framework based on binary classification. For this purpose, the two most effective artificial intelligence (AI) techniques, namely a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) and also the group method of data handling (GMDH) were applied, and a valuable database composed of 2838 was collected from the Kerman water conveyance tunnel project. The values of three parameters including the rotation speed, torque, and thrust force were measured that were considered as input data, and the values of penetration rate were measured as output data. Finally, the best-developed models were able to predict the binary classification of the TBM penetration rate with a testing accuracy of 92% and 91.6% for GMDH and GOA-SVM, respectively. In addition, the results obtained from the sensitivity analysis indicated that the rotation speed had the highest impact on the predicted penetration rate and torque and thrust force had the subsequent maximum impact in descending order, respectively.