Neural networks for defining spatial variation of rock properties in sparsely instrumented media

Reliable information of the three-dimensional distribution of rock mass properties improves the design of secure and cost-effective civil structures. In this paper, a recurrent neural network is presented as an alternative to predict the spatial variation of some index properties of rock in sparsely...

Full description

Saved in:
Bibliographic Details
Published in:Boletín de la Sociedad Geológica Mexicana 2016-01, Vol.68 (3), p.553-570
Main Authors: Benítez, Silvia Raquel García, Molina, Jorge Antonio López, Pedroza, Valentín Castellanos
Format: Article
Language:English
Subjects:
Artificial neural networks
ARTÍCULOS REGULARES / REGULAR ARTICLES
Civil engineering
Dams
Geoengineering
Geology
Geosciences, Multidisciplinary
Geotechnical engineering
Neurons
Property lines
Rocks
Spatial models
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reliable information of the three-dimensional distribution of rock mass properties improves the design of secure and cost-effective civil structures. In this paper, a recurrent neural network is presented as an alternative to predict the spatial variation of some index properties of rock in sparsely instrumented media. The neural technique, from statistical learning models, is used to approximate functions that can depend on a large number of inputs that are generally unknown. From a reasonably simple neuronal model of two inhomogeneous rock volumes, the limited measured information is extrapolated and the properties in the entire mass can be estimated. Comparisons betweenin situexplorationsversusthe 3D-neuronal definition confirm the potential of the proposed method for characterizing the properties of masses with inhomogeneous properties. Such a representation is useful for design of economic realistic numerical modelling of rock volumes, maximizing information while minimizing cost.
ISSN:1405-3322
1405-3322
DOI:10.18268/BSGM2016v68n3a10