A simple genetic algorithm for calibration of stochastic rock discontinuity networks

Jimenez Rodriguez, Rafael and Jurado Piña, Rafael (2012). A simple genetic algorithm for calibration of stochastic rock discontinuity networks. "Rock Mechanics and Rock Engineering", v. 45 (n. 4); pp. 461-473. ISSN 0723-2632. https://doi.org/10.1007/s00603-012-0226-1.

Description

Title: A simple genetic algorithm for calibration of stochastic rock discontinuity networks
Author/s:
  • Jimenez Rodriguez, Rafael
  • Jurado Piña, Rafael
Item Type: Article
Título de Revista/Publicación: Rock Mechanics and Rock Engineering
Date: July 2012
ISSN: 0723-2632
Volume: 45
Subjects:
Faculty: E.T.S.I. Caminos, Canales y Puertos (UPM)
Department: Ingeniería y Morfología del Terreno
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Este artículo propone un método para llevar a cabo la calibración de las familias de discontinuidades en macizos rocosos. We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications.

More information

Item ID: 15251
DC Identifier: http://oa.upm.es/15251/
OAI Identifier: oai:oa.upm.es:15251
DOI: 10.1007/s00603-012-0226-1
Official URL: http://link.springer.com/article/10.1007%2Fs00603-012-0226-1
Deposited by: Memoria Investigacion
Deposited on: 05 Nov 2013 10:56
Last Modified: 21 Apr 2016 15:17
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