Davide Elmo

My research is related to rock mechanics, rock engineering design and numerical modelling. I have intensively researched and published on the use of discrete fracture network engineering, interaction between surface and underground mining, mechanical behavior of hard rock pillars, slope stability analysis, and applications of synthetic rock mass modelling. I have developed a new branch of rock engineering called “behavioural rock engineering” to better understand the role that cognitive biases play in inductive forms of engineering design. I believe this topic is very important in the framework of adopting machine learning techniques when using input data that are largely qualitative in nature. By linking engineering to philosophy, we can explain the role of knowns and unknowns in engineering design, and we can consider the epistemological and ethical considerations of transferring uncertainty and cognitive biases from input to output in numerical results and computer applications. When applied, the findings of my research support more efficient rock engineering design and improve operational safety. To date, I have collaborated on more than 130 technical papers (peer-reviewed journals and conference proceedings).

• University of Exeter (UK), 2007, Ph.D
• University of Portsmouth (UK), 2001, B.Eng.

• MINE 303 Rock Mechanics Fundamentals
• MINE 403 Rock Mechanics Design
• MINE 505 Advanced Topics Rock Engineering
• MINE 485 Block Caving Systems (Cavability and Fragmentation)

• Schlotfeldt, P., D. Elmo and B. Panton. 2017. Overhanging rock slope by design: an integrated approach using rock mass strength characterisation, large scale numerical modelling and limiting equilibrium methods. International Journal of Rock Mechanics and Geotechnical engineering DJRMGE_2017_103. Accepted. In Press.
• Nadolski S., M. Munkhchuluun, B. Klein, D. Elmo and C. Hart. 2017. Cave Fragmentation in a Cave-to-Mill Context at the New Afton Mine Part I: Fragmentation and Hang-Up Frequency Prediction. Mining Technology (TIMM A). In Press.
• Mitelman A., D. Elmo and D. Stead. 2016. Development of a Spring Analogue Approach for the Study of Pillars and Shafts. International Journal of Mining Science and Technology. in Press.
• Gao F., D. Stead and D. Elmo. 2016. Numerical simulation of microstructure of brittle rock using a grain-breakable distinct element grain-based model. Computers and Geotechnics 78:203-217.
• Mitelman A. and D. Elmo. 2015. Analysis of Tunnel Support Design to Withstand Spalling Induced by Blasting. Journal of Tunnelling and Underground Space Technology. Vol. 51, pp. 354-361.
• Havaej M., J. Coggan, D. Stead and D. Elmo. 2015. A combined remote sensing-numerical modelling approach to the stability analysis of Delabole Slate Quarry, Cornwall, UK. Rock Mechanics and Rock Engineering. Volume 49, Issue 4, pp 1227-1245.
• Hamdi P., D. Stead and D. Elmo. 2015. Characterizing the influence of stress-induced microcracks on the laboratory strength and fracture development in brittle rocks using a finite/discrete element method-micro discrete fracture network FDEM-?DFN Approach. Journal of Rock mechanics and Geotechnical Engineering. Vol 7, 509-625.
• Panton B., D. Elmo, D. Stead and P. Schlotfeldt. 2015. A Discrete Fracture Network Approach for the Design of Rock Foundation Anchorage. Mining Technology Journal. Vol. 124(3), pp. 150-162.
• Nadolski S., B. Klein, D. Elmo and M. Scoble. 2015. Cave-to-Mill: A Mine-to-Mill approach for block cave mines. Mining Technology Journal. Vol. 124(1), pp. 47-55.
• Zhang Y., D. Stead and D. Elmo. 2015. Characterization of strength and damage of hard rock pillars using a synthetic rock mass method. Computers and Geotechnics Vol. 6, pp. 56-72.
• Mitelman A. and D. Elmo. 2014. Modelling of blast induced damage in tunnels using a hybrid finite-discrete numerical approach. Journal of Rock mechanics and Geotechnical Engineering. Volume 6(6), pp. 565-573.
• Elmo D., S. Rogers, D. Stead and E. Eberhardt. 2014. A Discrete fracture network approach to characterise rock mass fragmentation and implications for geomechanical upscaling. Mining Technology Journal. Vol. 123(3), pp. 149-161 (Second most viewed paper since November 2015).