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A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis
Abstract The creep strain is proportional to the logarithm of the time under load, and is proportional to the stress and the temperature. At higher temperatures the creep rate falls off less rapidly with time, and the creep strain is proportional to a fractional power of time, with exponent increasing as the temperature increases and reaching a value ∼1/3 at temperatures, of about 0.5 Tm. At these temperatures the creep increases with stress according to a power greater than unity and possibly exponentially increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) such as by experimental methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. In the present investigation, Artificial Neural Network (ANN) technique and Co-active neuro-fuzzy inference system (CANFIS) backed Genetic algorithm technique have been used for the prediction of creep strain and energy of Jog (B), and a comparative study has made between the two models.
A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis
Abstract The creep strain is proportional to the logarithm of the time under load, and is proportional to the stress and the temperature. At higher temperatures the creep rate falls off less rapidly with time, and the creep strain is proportional to a fractional power of time, with exponent increasing as the temperature increases and reaching a value ∼1/3 at temperatures, of about 0.5 Tm. At these temperatures the creep increases with stress according to a power greater than unity and possibly exponentially increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) such as by experimental methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. In the present investigation, Artificial Neural Network (ANN) technique and Co-active neuro-fuzzy inference system (CANFIS) backed Genetic algorithm technique have been used for the prediction of creep strain and energy of Jog (B), and a comparative study has made between the two models.
A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis
Singh, T. N. (author) / Verma, A. K. (author) / Sharma, P. K. (author)
2006
Article (Journal)
Electronic Resource
English
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
| British Library Online Contents | 2007
Analytical method of evaluating deformational properties of rock
| Springer Verlag | 1969
Analytical method of evaluating deformational properties of rock
| Online Contents | 1969
| Engineering Index Backfile | 1958