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Predicting the high temperature effect on mortar compressive strength by neural network
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  • Predicting the high temperature effect on mortar compressive strength by neural network
  • Predicting the high temperature effect on mortar compressive strength by neural network
저자명
Yuzer. N.,Akbas. B.,Kizilkanat. A.B.
간행물명
Computers & concrete
권/호정보
2011년|8권 5호|pp.491-510 (20 pages)
발행정보
테크노프레스
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Before deciding if structures exposed to high temperature are to be repaired or demolished, their final state should be carefully examined. Destructive and non-destructive testing methods are generally applied for this purpose. Compressive strength and color change in mortars are observed as a result of the effects of high temperature. In this study, ordinary and pozzolan-added mortar samples were produced using different aggregates, and exposed to 100, 200, 300, 600, 900 and $1200^{circ}C$. The samples were divided into two groups and cooled to room temperature in water and air separately. Compression tests were carried out on these samples, and the color change was evaluated by the Munsell Color System. The relationships between the change in compressive strength and color of mortars were determined by using a multi-layered feed-forward Neural Network model trained with the back-propagation algorithm. The results showed that providing accurate estimates of compressive strength by using the color components and ultrasonic pulse velocity design parameters were possible using the approach adopted in this study.