RT info:eu-repo/semantics/conferenceObject
T1 Particle swarm optimisation-based support vector regression model to estimate the powder factor of explosives in groundwater tunnel driving
A1 Miguel García, Eduardo de
K1 Powder factor
K1 Lithology
K1 Blast
K1 Tunnel
AB In many parts of the world, especially in arid or semi-arid areas, they finddrinking water in the subsoil. One way to reach it, it is through small horizontaltunnels (Qanat) built on the mountain using explosives. Civil engineers must designprojects where they estimate the budget necessary to undertake the work, taking intoaccount the amount of explosives needed, number of blasts, duration of the civil workand powder factor among other data. However, there is not artificial intelligencebased models that help to forecast the amount of explosive needed to drill a tunnel.In this work, a hybrid regression model based on support vector machine (SVM)and particle swarm optimization (PSO) trained with real data (types of lithologies,geomechanical characteristics of the rocks and the amount of explosives used byengineers based on their previous experiences) obtained from a volcanic groundwatertunnel driving in the island of Tenerife (Spain), is proposed to predict the advance,the amount of explosives, the number of blasts and the powder factor in new tunnelsor expansion of existing ones. The results show that a new, simpler regression modelhas been obtained that reproduces the experimental data and it will reduce the effortof the engineers in the study of a new tunnel driving work.
PB Springer
SN 978-981-15-9892-0
YR 2020
FD 2020
LK http://riull.ull.es/xmlui/handle/915/35691
UL http://riull.ull.es/xmlui/handle/915/35691
LA es
DS Repositorio institucional de la Universidad de La Laguna
RD 21-may-2024