Modeling of Concrete Slump and Compressive Strength using ANN
M. Deepak1, A. Gopalan2, R. Akshay Raj3, S.Shanmugi4, P.Usha5
1M. Deepak, Assistant Professor, Department of Civil Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
2A. Gopalan, Professor, Department of Civil Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
3R. Akshay Raj, U.G Student, Department of Civil Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
4S.Shanmugi, U.G Student, Department of Civil Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
5P.Usha, U.G Student, Department of Civil Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript Published on 22 March 2019 | PP: 497-503 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3471018319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artificial Neural Network (ANN) is a subdivision of Artificial Intelligence are extensively used to answer a complex civil engineering concerns. The following paper would predict the compressive strength and slump, having several mixtures with 28 days. ANN model with 7 different parameters that comprises: Slag (SL), Fly Ash (FL), Fine Aggregate (FA), Coarse Aggregate (CA), Super Plasticizers (SP), Cement (C), Water (W) respectively as input while concrete slump and while compressive strength as output. The same inputs are provided and are developed as another model. The slump and compressive strength of concrete are determined by ANN through its machine learning which is identified by validation, testing and training results. This kind of strength conjecture will help the concrete factories that manufactures the concrete, which when used in concrete will result in definite strength.
Keywords: Back Propagation Algorithm, Slump, Compressive Strength, Artificial Neural Network.
Scope of the Article: Computational Techniques in Civil Engineering