Design of concrete mixtures and prediction of their compressive strength using machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F25%3A10260328" target="_blank" >RIV/61989100:27120/25:10260328 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.e3s-conferences.org/articles/e3sconf/abs/2025/41/e3sconf_ys2025_01026/e3sconf_ys2025_01026.html" target="_blank" >https://www.e3s-conferences.org/articles/e3sconf/abs/2025/41/e3sconf_ys2025_01026/e3sconf_ys2025_01026.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1051/e3sconf/202564101026" target="_blank" >10.1051/e3sconf/202564101026</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of concrete mixtures and prediction of their compressive strength using machine learning

Popis výsledku v původním jazyce

The use of machine learning and neural networks in predicting the compressive strength of concrete promises to significantly improve the accuracy and reliability of models for the design and optimization of concrete mixtures. With rapid advances in this field, computational models will be able to handle even larger amounts of experimental data, increasing their ability to capture the complex relationships between input parameters and the mechanical properties of concrete. With the development of new neural network architectures and machine learning algorithms, it will be possible to create highly adaptive predictive models that can better respond to variability in concrete composition and production conditions, leading to more efficient and sustainable design in the construction industry. The submitted paper deals with the design of concrete mixtures and prediction of their compressive strength based on the compressive strength results of mixtures of known composition from other experiments using machine learning. Practical validation of the developed regression model will be carried out by testing the machine-designed mixtures for compressive strength after 28 days. © 2025 The Authors, published by EDP Sciences.

Název v anglickém jazyce

Design of concrete mixtures and prediction of their compressive strength using machine learning

Popis výsledku anglicky

The use of machine learning and neural networks in predicting the compressive strength of concrete promises to significantly improve the accuracy and reliability of models for the design and optimization of concrete mixtures. With rapid advances in this field, computational models will be able to handle even larger amounts of experimental data, increasing their ability to capture the complex relationships between input parameters and the mechanical properties of concrete. With the development of new neural network architectures and machine learning algorithms, it will be possible to create highly adaptive predictive models that can better respond to variability in concrete composition and production conditions, leading to more efficient and sustainable design in the construction industry. The submitted paper deals with the design of concrete mixtures and prediction of their compressive strength based on the compressive strength results of mixtures of known composition from other experiments using machine learning. Practical validation of the developed regression model will be carried out by testing the machine-designed mixtures for compressive strength after 28 days. © 2025 The Authors, published by EDP Sciences.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20100 - Civil engineering

Projekt

<a href="/cs/project/EH22_008%2F0004631" target="_blank" >EH22_008/0004631: Materiály a technologie pro udržitelný rozvoj</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

E3S Web of Conferences. Volume 641

ISBN

—

ISSN

2267-1242

e-ISSN

2267-1242

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

EDP Sciences

Místo vydání

Les Ulis

Místo konání akce

Vysoké Tatry

Datum konání akce

26. 3. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—