Compressive Strength Prediction of Roller-compacted Concrete Using Nondestructive Tests through Artificial Intelligence
- DOI
- 10.2991/aece-16.2017.76How to use a DOI?
- Keywords
- roller-compacted concrete; nondestructive test; Schmidt hammer; ultrasonic test; artificial intelligence
- Abstract
Roller-Compacted Concrete (RCC) is a type of concrete that has similar basic ingredients to conventional concrete but contains much less water and is known as non-slump concrete. Nondestructive testing (NDT) is a very good choice to evaluate the quality and strength of such concretes as RCC. These testes are easy, fast and efficient and are also of low cost. The aim of this study is estimation of compressive strength of RCC by offering suitable mathematical formulations based on results of nondestructive tests. For this purpose, many samples of RCC with and without fibers are made and tested by concrete breaking machines and nondestructive techniques including Schmidt hammer and ultrasonic tests. Finally, by the aid of Artificial Intelligence (AI) concepts, and using the values from nondestructive and destructive testes suitable relations were found. In practical projects by doing nondestructive tests and without performing destructive testes, the RCC compressive strength can be calculated from the proposed relations with high accuracy. If both Schmidt hammer and ultrasonic tests are used simultaneously, the accuracy increases.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Mohammad Ali HADIANFARD AU - Ali Reza NIKMOHAMMADI PY - 2016/12 DA - 2016/12 TI - Compressive Strength Prediction of Roller-compacted Concrete Using Nondestructive Tests through Artificial Intelligence BT - Proceedings of the 2016 International Conference on Architectural Engineering and Civil Engineering PB - Atlantis Press SP - 344 EP - 348 SN - 2352-5401 UR - https://doi.org/10.2991/aece-16.2017.76 DO - 10.2991/aece-16.2017.76 ID - HADIANFARD2016/12 ER -