Assessment of Post-blast Damage Zones in Tunneling Operations Through MWD and Machine Learning

In this paper, Machine Learning techniques have been used to combine data monitored from the drill rig with scanner profiles of the excavated sections to develop an automatic geotechnical rock mass characterization model able to estimate potential zones of over- or under-break of the remaining rock mass in underground blasting. By comparison of scanner profiles of the excavated sections with the blasthole positions, an Excavated Mean Distance (EMD) between the contour blastholes and the excavated profile has been obtained, which may be considered as damage measure.