12/22/2023 0 Comments Usaf deep core tunnel boring machineLong short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed Monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. Eventually, comparing the ML outcomes and the TBM Perform, the 5-fold cross-validation was taken into consideration. Strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alphaĪngle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. A simple ranking technique, as well as some performance indices, were calculated for each developed model. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass.
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