Soft computing-based calibration of microplane M4 model parameters: Methodology and validation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F14%3A00214185" target="_blank" >RIV/68407700:21110/14:00214185 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.advengsoft.2014.01.013" target="_blank" >http://dx.doi.org/10.1016/j.advengsoft.2014.01.013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.advengsoft.2014.01.013" target="_blank" >10.1016/j.advengsoft.2014.01.013</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Soft computing-based calibration of microplane M4 model parameters: Methodology and validation

Popis výsledku v původním jazyce

Constitutive models for concrete based on the microplane concept have repeatedly proven their ability to well-reproduce non-linear response of concrete on material as well as structural scales. The major obstacle to a routine application of this class ofmodels is, however, the calibration of microplane-related constants from macroscopic data. The goal of this paper is twofold: (i) to introduce the basic ingredients of a robust inverse procedure for the determination of dominant parameters of the M4 model proposed by Bažant et al. (2000) based on cascade artificial neural networks trained by evolutionary algorithm and (ii) to validate the proposed methodology against a representative set of experimental data. The obtained results demonstrate that the soft computing-based method is capable of delivering the searched response with an accuracy comparable to the values obtained by expert users.

Název v anglickém jazyce

Soft computing-based calibration of microplane M4 model parameters: Methodology and validation

Popis výsledku anglicky

Constitutive models for concrete based on the microplane concept have repeatedly proven their ability to well-reproduce non-linear response of concrete on material as well as structural scales. The major obstacle to a routine application of this class ofmodels is, however, the calibration of microplane-related constants from macroscopic data. The goal of this paper is twofold: (i) to introduce the basic ingredients of a robust inverse procedure for the determination of dominant parameters of the M4 model proposed by Bažant et al. (2000) based on cascade artificial neural networks trained by evolutionary algorithm and (ii) to validate the proposed methodology against a representative set of experimental data. The obtained results demonstrate that the soft computing-based method is capable of delivering the searched response with an accuracy comparable to the values obtained by expert users.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JM - Inženýrské stavitelství

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in Engineering Software

ISSN

0965-9978

e-ISSN

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Svazek periodika

72

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

226-235

Kód UT WoS článku

000335707200024

EID výsledku v databázi Scopus

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