Predicting the geological condition beyond the tunnel excavation face using MSP monitoring data and LSTM algorithm

Abstract

The ground conditions beyond an excavation face, especially discontinuities in rock masses, have a significant influence on tunnel construction. However, the actual ground conditions observed during tunnel construction are often different from the ground conditions predicted in the geotechnical site explorations carried out in the design stage. Changes in ground conditions may require alterations in tunnel design, leading to substantial disruptions in the construction schedule and budget. In this regard, accurate ground evaluation prior to the design and construction stages are essential for successful tunnel construction projects. Machine learning models have been developed in order to evaluate the condition of rock discontinuities within 50 m of the tunnel face. Machine data (rotational pressure, feed pressure, and drilling (advance) speed) obtained from a large boring hole machine, called MSP, at an NATM construction site in a granite formation located in South Korea were logged, and the actual ground Discontinuity Score (DS) was appraised by analysing internal bore hole images taken after drilling. Then, the LSTM algorithm was applied to develop the machine learning model to determine DS based on the logged machine data. DS was most accurately predicted when the drilling speed was included in the input data, whereas those cases using only the rotational and feed pressure in the input data showed low prediction accuracy. Therefore, the drilling speed seems to have a higher correlation than hydraulic pressure with regard to ground conditions, including discontinuities. Once additional data is collected from various tunnel sites, the machine learning model could be further enhanced to become more robust and provide solutions to various engineering problems.

Safe small scale mine blasting operation: An application of soft computing techniques to predict blast-induced flyrock distance

“…LSTMs are designed to address the vanishing gradient/exploding gradient problem of traditional RNNs and can efficiently capture long-short term dependencies using memory cells and gates [33,34]. Previous utilization of LSTM models proved to outperform basic deep neural networks according to a number of recent studies [35,[36][37][38][39][40]. In this study, the LSTM model technique was developed for the prediction of blast induced fly-rock generated in dolomite small-scale mines.…”