TY - JOUR
T1 - Why the future of rock mass classification systems requires revisiting their empirical past
AU - Yang, Beverly
AU - Mitelman, Amichai
AU - Elmo, Davide
AU - Stead, Doug
N1 - Publisher Copyright:
© 2021 The Author(s). Published by The Geological Society of London. All rights reserved.
PY - 2022/2
Y1 - 2022/2
N2 - Despite recent efforts, digitization in rock engineering still suffers from the difficulty in standardizing and statistically analysing databases that are created by a process of quantification of qualitative assessments. Indeed, neither digitization nor digitalization have to date been used to drive changes to the principles upon which, for example, the geotechnical data-collection process is founded, some of which have not changed in several decades. There is an empirical knowledge gap that cannot be bridged by the use of technology alone. In this context, this paper presents the results of what the authors call a rediscovery of rock mass classification systems, and a critical review of their definitions and limitations in helping engineers to integrate these methods and digital acquisition systems. This discussion has significant implications for the use of technology as a tool to directly determine rock mass classification ratings and for the application of machine learning to address rock engineering problems.
AB - Despite recent efforts, digitization in rock engineering still suffers from the difficulty in standardizing and statistically analysing databases that are created by a process of quantification of qualitative assessments. Indeed, neither digitization nor digitalization have to date been used to drive changes to the principles upon which, for example, the geotechnical data-collection process is founded, some of which have not changed in several decades. There is an empirical knowledge gap that cannot be bridged by the use of technology alone. In this context, this paper presents the results of what the authors call a rediscovery of rock mass classification systems, and a critical review of their definitions and limitations in helping engineers to integrate these methods and digital acquisition systems. This discussion has significant implications for the use of technology as a tool to directly determine rock mass classification ratings and for the application of machine learning to address rock engineering problems.
UR - http://www.scopus.com/inward/record.url?scp=85123070215&partnerID=8YFLogxK
U2 - 10.1144/qjegh2021-039
DO - 10.1144/qjegh2021-039
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
AN - SCOPUS:85123070215
SN - 1470-9236
VL - 55
JO - Quarterly Journal of Engineering Geology and Hydrogeology
JF - Quarterly Journal of Engineering Geology and Hydrogeology
IS - 1
M1 - qjegh2021-039
ER -