Improving face decisions in tunnelling by machine learning‐based MWD analysis

Abstract

AbstractIn Norwegian drill and blast tunnelling, contracts stipulate collecting Measurement While Drilling (MWD) data from all drillholes. The MWD approach is an objective way of collecting, processing and visualising advance drilling data that have been successfully used in making face decisions for many years in Norwegian tunnelling. MWD data collection consists of equipping drill rigs with sensors recording different drilling parameters, with subsequent near real‐time data processing for access by on‐site personnel and face engineers in decision‐making process. A deficiency in the MWD approach is still the subjective data interpretation necessary to translate visualised data into actual face decisions. Digital scepticism and a lack of digital knowledge are other obstacles in automating the process from data interpretation to decisions. Thus, MWD data are sometimes only used for nice visualisations and as‐built documentation. This study proposes machine learning (ML)‐based methods to characterise the rock mass from sensor data using data from five twin‐tube tunnels in a Norwegian highway project. Results show that the deep learning‐based method – convolutional neural network – is capable of translating complex patterns in MWD data to functional rock mass characterisation, thereby aiding face engineers with data analysis. The study is, to our knowledge, the first known attempt to use deep learning‐based computer vision techniques to interpret MWD data framed as images.