|
Title |
Study on Prediction of Compressive Strength of Concrete based on Aggregate Shape Features and Artificial Neural Network
|
|
Authors |
전준서(Jun-Seo Jeon) ; 김홍섭(Hong-Seop Kim) ; 김창혁(Chang-Hyuk Kim) |
|
DOI |
https://doi.org/10.11112/jksmi.2021.25.5.135 |
|
Abstract |
In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.
|