A hybrid artificial neural network-based identification system for fine-grained composites

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F21%3APU141955" target="_blank" >RIV/00216305:26110/21:PU141955 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.techno-press.org/content/?page=article&journal=cac&volume=28&num=4&ordernum=3" target="_blank" >http://www.techno-press.org/content/?page=article&journal=cac&volume=28&num=4&ordernum=3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.12989/cac.2021.28.4.369" target="_blank" >10.12989/cac.2021.28.4.369</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid artificial neural network-based identification system for fine-grained composites

Popis výsledku v původním jazyce

Recent interest in the development of innovative building materials has brought about the need for a detailed assessment of their mechanical fracture properties. The parameters for these need to be acquired, and one of the possible ways of doing so is to obtain them indirectly based on a combination of fracture testing and inverse analysis. The paper describes a method for the identification of selected parameters of mortars and other fine grained brittle matrix composites. The cornerstone of the method is the use of an artificial neural network, which is utilized as a surrogate model of the inverse relation between the measured specimen response parameters and the sought material parameters. Due to the potentially wide range of composite mixtures and hence the wide range of experimental responses likely to be gained from individual specimens, an ensemble of artificial neural networks was created. It allows the entire range of variants to be covered and provides resulting parameter values with sufficient precision. Such a system is also easy to expand if a composite with properties outside the current range is tested. The capabilities of the proposed identification system are demonstrated on two selected types of fine grained composites with different specimen responses. The first group of specimens was made of composite based on alkali activated slag with standardized and natural sand investigated within the time interval of 3 to 330 days of aging. The second tested composite contained alkali activated fly ash matrix, and the effect of the addition of natural fibers on fracture response was investigated.

Název v anglickém jazyce

A hybrid artificial neural network-based identification system for fine-grained composites

Popis výsledku anglicky

Recent interest in the development of innovative building materials has brought about the need for a detailed assessment of their mechanical fracture properties. The parameters for these need to be acquired, and one of the possible ways of doing so is to obtain them indirectly based on a combination of fracture testing and inverse analysis. The paper describes a method for the identification of selected parameters of mortars and other fine grained brittle matrix composites. The cornerstone of the method is the use of an artificial neural network, which is utilized as a surrogate model of the inverse relation between the measured specimen response parameters and the sought material parameters. Due to the potentially wide range of composite mixtures and hence the wide range of experimental responses likely to be gained from individual specimens, an ensemble of artificial neural networks was created. It allows the entire range of variants to be covered and provides resulting parameter values with sufficient precision. Such a system is also easy to expand if a composite with properties outside the current range is tested. The capabilities of the proposed identification system are demonstrated on two selected types of fine grained composites with different specimen responses. The first group of specimens was made of composite based on alkali activated slag with standardized and natural sand investigated within the time interval of 3 to 330 days of aging. The second tested composite contained alkali activated fly ash matrix, and the effect of the addition of natural fibers on fracture response was investigated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GA19-09491S" target="_blank" >GA19-09491S: Víceúrovňové stanovení lomově-mechanických parametrů pro simulaci betonových konstrukcí (MUFRAS)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Computers and Concrete

ISSN

1598-8198

e-ISSN

1598-818X

Svazek periodika

28

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

10

Strana od-do

369-378

Kód UT WoS článku

000711669900003

EID výsledku v databázi Scopus

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