Comparison of methods of prediction of compressive strength of concrete using multiple linear regression in microsoft excel and artificial neural networks in RStudio

Compressive strength (CS) of concrete is a key quality factor that is being monitored continuously in all construction projects which use huge quantity of concrete throughout the world. Engineers need to be open minded with the attitude of lifelonglearning to upskill with the ever-evolving softwarea...

Full description

Saved in:
Bibliographic Details
Main Authors: Hussain, Mohammed, Yedukondalu, G., Raju, Y. Kamala, Prasad, V. Kamakshi
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Blast furnace practice
Blast furnace slags
Compressive strength
Fly ash
Neural networks
Q factors
Regression analysis
Slag
Sustainable development
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Compressive strength (CS) of concrete is a key quality factor that is being monitored continuously in all construction projects which use huge quantity of concrete throughout the world. Engineers need to be open minded with the attitude of lifelonglearning to upskill with the ever-evolving softwareand hardware technologies. Seven day and twenty-eight-day compressive strengths of concrete samples with varied amounts of cement, blast furnace slag, fly ash, water, super plasticizer, coarse aggregate, and fine aggregate are studied. Multiple Linear Regression (MLR) models are fitted for this data in Microsoft Excel. Artificial Neural Networks (ANNs) in RStudio are developed for this data. The performances of both methods are compared. This paper takes care of Goal 12 of United Nations Sustainable Development of ensuring sustainable consumption and production patterns as environmentally degrading materials (flyash and blast furnace slag) are used.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0058070