Design and Implementation of ANN Based SCC with GGBS using Auromix 400
Mohanraj A1, Karthiga N2, Durgadevi S3, Dhivyabharathi S4, Senthilkumar V5

1Mr. A. Mohanraj*, Department of Civil, Bannariamman Institute of Technology, Erode, India.
2Mrs. N. Karthiga, Department of Civil, Bannariamman Institute of Technology, Erode, India.
3Mrs. S. Durdadevi, Department of Civil, Bannariamman Institute of Technology, Erode, India.
4Mrs. S. Dhivyabharathi, Department of Civil, Bannariamman Institute of Technology, Erode, India.
5Dr. V. Senthilkumar, Department of Civil, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India. 

Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 776-781 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3867079220/2020©BEIESP | DOI: 10.35940/ijrte.B3867.079220
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Abstract: Concrete is the main building material. When concrete becomes hard, it gives strength to the structure. Many times it is a difficult task to pour the concrete into the formwork and compact it perfectly. This has been overcome by using Self-Compacting Concrete (SCC). Such a concrete is one of the advanced building materials in the field of construction industry. Unlike the other type of concrete, this kind of concrete compact’s effectively under its own weight. There is no need of any external vibration or compaction procedure to minimal the concrete in formwork. It can easily flow in every corners of the formwork without blocking. This project deals with SCC in which, the binary material used is Ground Granulated Blast Furnace Slag (GGBS) as mineral admixture at various percentage of replacement. To reduce the measure of water used in concrete, Auromix-400 is used as Super Plasticizer at a constant dosage. Several tests were carried out to study the behavior of fresh and hardened concrete. Test for fresh concrete includes slump flow, V Funnel test. Similarly, the properties of concrete were also determined by conducting compression and Spit tensile test. At the same time the simulation model was also developed to test the proposed system using the artificial neural network (ANN) protocol. The ANN model is built on six objects with multiple output-multiple. Single Output Type – In the second method, the artificial neural network model is a single input neural network that is built on top of multiple inputs – where multiple inputs has been predicted separately based on various types of neural function – Secondly, the ANN model is built on multiple inputs. The results indicate the superiority of the neural network method in terms of the accuracy of publication prediction results.
Keywords: SCC, ANN, Workability, Compressive strength, Split tensile.