Prediction of Vertical Alignment of the MSP Borehole using Artificial Neural Network

Abstract

The multi-setting smart-investigation of the ground and pre-large hole boring method (MSP) has been widely used to secure a free face to effectively reduce the peak particle velocity (PPV) at tunnel construction. MSP generally involves drilling a 50-m borehole in the sub-horizontal direction using a 0.9-ton hammer bit at the end of MSP rod. As the borehole length increases, the hammer bit begins to vertically sag as a result of its heavy weight. If the alignment of the borehole diverges from its intended target, borehole reconstruction is inevitable, which leads to extensive time delays and extra costs. Though the borehole height is a crucial factor in determining whether reconstruction is required, there is currently no quantitative method to predict the vertical alignment of the borehole. We gathered 2,630 datasets from 13 tunnel construction sites where MSP had been applied, and developed a prediction model about the borehole height using artificial neural networks. In testing with 25% of those datasets, the mean absolute error was 0.008 m and the coefficient of determination between the measured and predicted values was 0.9998. The prediction model demonstrated good agreement with the actual measurements and can contribute to preventing unnecessary reconstruction events.