Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU147019" target="_blank" >RIV/00216305:26110/23:PU147019 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1996-1944/16/3/1010" target="_blank" >https://www.mdpi.com/1996-1944/16/3/1010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma16031010" target="_blank" >10.3390/ma16031010</a>

Result language

angličtina

Original language name

Classification of Thermally Degraded Concrete by Acoustic Resonance Method and Image Analysis via Machine Learning

Original language description

The study of the resistance of plain concrete to high temperatures is a current topic across the field of civil engineering diagnostics. It is a type of damage that affects all components in a complex way, and there are many ways to describe and diagnose this degradation process and the resulting condition of the concrete. With regard to resistance to high temperatures, phenomena such as explosive spalling or partial creep of the material may occur. The resulting condition of thermally degraded concrete can be assessed by a number of destructive and nondestructive methods based on either physical or chemical principles. The aim of this paper is to present a comparison of nondestructive testing of selected concrete mixtures and the subsequent classification of the condition after thermal degradation. In this sense, a classification model based on supervised machine learning principles is proposed, in which the thermal degradation of the selected test specimens are known classes. The whole test set was divided into five mixtures, each with seven temperature classes in 200 °C steps from 200 °C up to 1200 °C. The output of the paper is a comparison of the different settings of the classification model and validation algorithm in relation to the observed parameters and the resulting model accuracy. The classification is done by using parameters obtained by the acoustic NDT Impact-Echo method and image-processing tools.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

<a href="/en/project/GA22-02098S" target="_blank" >GA22-02098S: Experimental analysis of the shrinkage, creep and cracking mechanism of the materials based on the alkali-activated slag</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

—

Volume of the periodical

16

Issue of the periodical within the volume

3

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

„“-„“

UT code for WoS article

000929756700001

EID of the result in the Scopus database

2-s2.0-85148022804