Simulation of tri-axial stress redistribution effect in concrete under fatigue loading: lattice discrete model vs. microplane model
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Date
2024-10-29
Authors
Aguilar Rueda, Mario
Vořechovský, Miroslav
BAKTHEER, Abedulgader
Wan-Wendner, Roman
Vorel, Jan
Chudoba, Rostislav
0000-0002-3366-5557Advisor
Referee
Mark
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CIMNE
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Abstract
Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models, used for reliability analysis, based on the selection of possible future candidates in the input domain (i.e., random variables). In the present research, different methodologies for obtaining the training sample for a surrogate model were explored, considering sample size, distribution of the points, and identification of the failure region. The effects on the reliability of the slope stability under vertical loading based on the safety factors from Bishop’s simplified method were obtained. The results reinforce the importance of the characteristics of the training sample used for the application of surrogate models to describe limit states and their accuracy when employed for the computation of the reliability index.
Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models, used for reliability analysis, based on the selection of possible future candidates in the input domain (i.e., random variables). In the present research, different methodologies for obtaining the training sample for a surrogate model were explored, considering sample size, distribution of the points, and identification of the failure region. The effects on the reliability of the slope stability under vertical loading based on the safety factors from Bishop’s simplified method were obtained. The results reinforce the importance of the characteristics of the training sample used for the application of surrogate models to describe limit states and their accuracy when employed for the computation of the reliability index.
Adaptive sequential sampling provides a good technique to refine and increase the accuracy of surrogate models, used for reliability analysis, based on the selection of possible future candidates in the input domain (i.e., random variables). In the present research, different methodologies for obtaining the training sample for a surrogate model were explored, considering sample size, distribution of the points, and identification of the failure region. The effects on the reliability of the slope stability under vertical loading based on the safety factors from Bishop’s simplified method were obtained. The results reinforce the importance of the characteristics of the training sample used for the application of surrogate models to describe limit states and their accuracy when employed for the computation of the reliability index.
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en
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Defence
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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
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