Detail publikace

Application of Machine Learning for Prediction of Mechanical Properties of Mortars and Concretes

PRUDIL, M.

Originální název

Application of Machine Learning for Prediction of Mechanical Properties of Mortars and Concretes

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This paper deals with the application of machine learning (ML) in the field of concrete technology. Two databases of test mortars and concretes were created from selected academic theses, which include mechanical properties in relation to their composition. These databases were used to develop two ML models that predict the mechanical properties of mortars and concretes depending on their composition. The mortar test database contains a total of 242 mechanical property records and the concrete test database contains 111 records. The materials in the database are CEM I, CEM II and CEM III cements combined with additives such as ground granulated blast furnace slag, high temperature fly ash and micro-ground limestone.

Klíčová slova

Concrete technology, machine learning, mechanical properties, compressive strength, flexural strength

Autoři

PRUDIL, M.

Vydáno

7. 5. 2024

Nakladatel

ECON publishing s.r.o.

Místo

Brno

ISBN

978-80-86433-83-7

Kniha

Juniorstav 2024: Proceedings 26th International Scientific Conference Of Civil Engineering

ISSN

3029-5904

Periodikum

Juniorstav 2024

Stát

Česká republika

Strany počet

8

URL

https://dspace.vut.cz/server/api/core/bitstreams/309c6a7b-3f57-49a0-9112-e09d7699f002/content

BibTex

@inproceedings{BUT193490,
  author="Matěj {Prudil}",
  title="Application of Machine Learning for Prediction of Mechanical Properties of Mortars and Concretes",
  booktitle="Juniorstav 2024: Proceedings 26th International Scientific Conference Of Civil Engineering",
  year="2024",
  journal="Juniorstav 2024",
  pages="8",
  publisher="ECON publishing s.r.o.",
  address="Brno",
  doi="10.13164/juniorstav.2024.24085",
  isbn="978-80-86433-83-7",
  issn="3029-5904",
  url="https://dspace.vut.cz/server/api/core/bitstreams/309c6a7b-3f57-49a0-9112-e09d7699f002/content"
}