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Soft Computing in Civil Engineering
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Volume Volume 2 (2018)
Volume Volume 1 (2017)
Charhate, S., Subhedar, M., Adsul, N. (2018). Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network. Soft Computing in Civil Engineering, 2(3), 27-38. doi: 10.22115/scce.2018.112140.1041
Shrikant Charhate; Mansi Subhedar; Nilam Adsul. "Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network". Soft Computing in Civil Engineering, 2, 3, 2018, 27-38. doi: 10.22115/scce.2018.112140.1041
Charhate, S., Subhedar, M., Adsul, N. (2018). 'Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network', Soft Computing in Civil Engineering, 2(3), pp. 27-38. doi: 10.22115/scce.2018.112140.1041
Charhate, S., Subhedar, M., Adsul, N. Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network. Soft Computing in Civil Engineering, 2018; 2(3): 27-38. doi: 10.22115/scce.2018.112140.1041

Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network

Article 3, Volume 2, Issue 3 - Serial Number 5, Summer 2018, Page 27-38  XML PDF (714.51 K)
Document Type: Regular Article
DOI: 10.22115/scce.2018.112140.1041
Authors
Shrikant Charhate 1; Mansi Subhedar2; Nilam Adsul3
1Professor and Dean Department of Civil Engineering Pillai HOC College of Engineering and Technology, Rasayani
2Faculty of Technology
3Department of Civil Engineering Pillai HOC College of Engineering & Technology
Receive Date: 27 December 2017,  Revise Date: 05 March 2018,  Accept Date: 18 March 2018 
Abstract
The selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability & compressive strength are the two significant parameters that need special attention. The aim of this study is to predict the slump along with 7-days & 28-days compressive strength based on the data collected from various RMC plants. There are many studies reported in general to address this issue time to time over a long period. However, considering the worldwide use of a huge quantity of concrete for various infrastructure projects, there is a scope for the study that leads to most accurate estimate. Here, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and M70 grade of concrete. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7-days and 28-days compressive strength. A variety of experiments was carried out that suggests ANN performs better and yields more accurate prediction compared to MLR model for both slump & compressive strength.

Highlights
Keywords
Slump; Compressive strength; Multiple linear regression; Artificial Neural Network
Main Subjects
Artificial Neural Networks (ANN)
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