Design of foundations for large-scale civil works naturally involves soil characterization over considerable volumes. The 3D-interpretation of properties where only scarce geotechnical data is available is crucial for deriving effective and safe engineering decisions. Because of the ever-increasing cost of site investigation, it is neither practical nor economical to acquire geotechnical data at each point of interest for a complete definition of soils behavior. This situation makes it necessary to explore spatial-variability modeling alternatives that can manage limited geo-information. In this paper, a dynamic-neural procedure is developed for describing spatial relations between a set of geo-parameters easy-to-obtain. Once the network is finished, this topology is used to expand the small initial set of values into millions of computer-generated measurements. The massive database is incorporated into a Virtual Reality engine that facilitates the intuitively visual understanding of geo-information, permits to present all relevant data in a comprehensible format for decision making and provides a way to reduce very complex and diverse data sets into the essential elements without loss of data quality.
[1]
J. Carlos Santamarina,et al.
Strain-Rate Effects in Mexico City Soil
,
2009
.
[2]
Marián Boguñá,et al.
Extracting the multiscale backbone of complex weighted networks
,
2009,
Proceedings of the National Academy of Sciences.
[3]
J. E. Aguayo Camargo,et al.
Neotectónica y facies sedimentarias cuaternarias en el suroeste del Golfo de México, dentro del marco tectono-estratigráfico regional evolutivo del Sur de México
,
2005
.
[4]
Yee Whye Teh,et al.
A Fast Learning Algorithm for Deep Belief Nets
,
2006,
Neural Computation.
[5]
Sepp Hochreiter,et al.
Learning to Learn Using Gradient Descent
,
2001,
ICANN.
[6]
Simon Haykin,et al.
Neural Networks and Learning Machines
,
2010
.
[7]
Michael Egmont-Petersen,et al.
Image processing with neural networks - a review
,
2002,
Pattern Recognit..