Abstract
In this paper the possibility of using different regression models to predict the mechanical properties of limestone concrete after exposure to high temperatures, based on the results of non-destructive techniques, that could be easily used in-situ, is discussed. Extensive experimental work was carried out on limestone concrete mixtures, that differed in the water to cement (w/c) ratio, the type of cement and the quantity of superplasticizer added. After standard curing, the specimens were exposed to various high temperature levels, i.e., 200%, 400%, 600% or 800%. Before heating, the reference mechanical properties of the concrete were determined at ambient temperature. After the heating process, the specimens were cooled naturally to ambient temperature and tested using non-destructive techniques. Among the mechanical properties of the specimens after heating, known also as the residual mechanical properties, the residual modulus of elasticity, compressive and flexural strengths were determined. The results show that residual modulus of elasticity, compressive and flexural strengths can be reliably predicted using an artificial neural network approach based on ultrasonic pulse velocity, residual surface strength, some mixture parameters and maximal temperature reached in concrete during heating
Keywords
gradbeništvo;mehanske lastnosti;tlačna trdnost;umetne nevronske mreže;beton;požarno obnašanje;residual mechanical properties;compressive strength;artificial neural network;non-destructive testing techniques;fire behavior;concrete;
Data
Language: |
English |
Year of publishing: |
2020 |
Typology: |
1.01 - Original Scientific Article |
Organization: |
UL FGG - Faculty of Civil and Geodetic Engineering |
UDC: |
624 |
COBISS: |
29330179
|
ISSN: |
2287-531X |
Views: |
429 |
Downloads: |
54 |
Average score: |
0 (0 votes) |
Metadata: |
|
Other data
Secondary language: |
Slovenian |
Secondary keywords: |
gradbeništvo;mehanske lastnosti;tlačna trdnost;umetne nevronske mreže;beton;požarno obnašanje; |
Type (COBISS): |
Scientific work |
Embargo end date (OpenAIRE): |
0000-00-00 |
Pages: |
str. 247-256 |
Volume: |
ǂLetn. ǂ10 |
Issue: |
ǂšt. ǂ3 |
Chronology: |
2020 |
DOI: |
10.12989/acc.2020.10.3.247 |
ID: |
12048584 |