Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study
In this study, the effect of high temperature on the flexural and compressive strength of mortars containing waste PET aggregates was investigated experimentally. The mortar samples prepared in 5 different concentrations with a total of 2.5%, 5%, 10%, 20% and 30% PET aggregate substitution were heat...
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| Veröffentlicht in: | Arabian journal for science and engineering (2011) 2021-05, Vol.46 (5), p.4867-4881 |
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| container_title | Arabian journal for science and engineering (2011) |
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| creator | Çolak, Andaç Batur Akçaözoğlu, Kubilay Akçaözoğlu, Semiha Beller, Gülhan |
| description | In this study, the effect of high temperature on the flexural and compressive strength of mortars containing waste PET aggregates was investigated experimentally. The mortar samples prepared in 5 different concentrations with a total of 2.5%, 5%, 10%, 20% and 30% PET aggregate substitution were heated up to 100, 150, 200, 250, 300 and 400 °C. After waiting for 1, 2 and 3 h at these temperatures, flexural and compressive strength tests were performed. It was observed that flexural strength and compressive strength values decreased with increasing temperature and PET aggregate amounts in all mixtures. An artificial neural network was designed to estimate flexural and compressive strength values using experimental data. It has been observed that the developed artificial neural network can predict flexural and compressive strengths with an average error of − 0.51%. |
| doi_str_mv | 10.1007/s13369-020-05280-1 |
| format | Article |
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The mortar samples prepared in 5 different concentrations with a total of 2.5%, 5%, 10%, 20% and 30% PET aggregate substitution were heated up to 100, 150, 200, 250, 300 and 400 °C. After waiting for 1, 2 and 3 h at these temperatures, flexural and compressive strength tests were performed. It was observed that flexural strength and compressive strength values decreased with increasing temperature and PET aggregate amounts in all mixtures. An artificial neural network was designed to estimate flexural and compressive strength values using experimental data. It has been observed that the developed artificial neural network can predict flexural and compressive strengths with an average error of − 0.51%.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s13369-020-05280-1</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9297-8134</orcidid></addata></record> |
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| subjects | Aggregates Artificial intelligence Artificial neural networks Compressive strength Engineering Flexural strength High temperature effects Humanities and Social Sciences Mechanical properties Mortars (material) multidisciplinary Neural networks Research Article-Civil Engineering Science |
| title | Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study |
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