Prediction of bond strength of lightweight concretes by using artificial neural networks

被引：0
|
作者：
Sancak, Emre [1 ]
机构：
[1] Suleyman Demirel Univ, Construct Educ Dept, Tech Educ Fac, TR-32260 Isparta, Turkey
来源：
SCIENTIFIC RESEARCH AND ESSAYS | 2009年 / 4卷 / 04期
关键词：
Artificial neural networks; structural lightweight concrete; pumice; bond strength; CEMENT;
D O I：
暂无
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In the scope of this study, concrete samples planned to be used as load-bearing concrete were produced by using pumice aggregate and silica fume. Cement was replaced by silica fume, as the mineral additive, by 5 and 10% of its weight. First of all, fresh concrete properties of the produced samples were evaluated. Then, compressive strength tests were conducted on the 28(th) and 90(th) days. In addition, pull-out tests were carried out on cubic samples of 150 mm(3) on the 90(th) day so as to detect the reinforcing steel-concrete bond strength. The data obtained at the end of the tests were used as input to the Artificial Neural Networks (ANN) method to predict bond strength values. Bond strength values predicted via the ANN method were found to be close to the bond strength values obtained via tests. In conclusion, it can be quite beneficial to predict the bond strength of normal and lightweight concrete via the ANN method by using a high number of parameters as input. Thus, it will be possible to detect the reinforcing steel-concrete bond strength in a faster and reliable manner and by doing less laboratory work.
引用
收藏
页码：256 / 266
页数：11
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