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Prediction Of Mechanical Properties Of Steel Fiber Reinforced Concrete Using Cnn

Submitted2021-09-20
Last Update2022-03-24
TitlePrediction Of Mechanical Properties Of Steel Fiber Reinforced Concrete Using Cnn
Author(s)Author #1
Author title:Assiatant Professor
Name: Kavya B R
Org: Adichunchanagiri Institute of Technology, Chikkamagaluru
Country: India
Email: br.kavya6@gmail.com

Author #2
Author title:Professor
Name: Sureshchandra H S
Org: Mysore Royal Institute of Technology, Srirangapattana
Country: India
Email: schandrapes@gmail.com

Author #3
Author title:Assiatant Professor
Name: Prashantha S J
Org: Adichunchanagiri Institute of Technology, Chikkamagaluru
Country: India
Email: prasi.sjp@gmail.com

Author #4
Author title:Associate Professor
Name: Shrikanth A S
Org: Adichunchanagiri Institute of Technology, Chikkamagaluru
Country: India
Email: shrikanthas@gmail.com

Other Author(s)
Contact AuthorAuthor #1
Alt Email:
Telephone: +918861519334
KeywordsSteel Fiber Reinforced Concrete, Deep Learning, Convolutional Neural Network, Strength Prediction
AbstractThe performance of Steel Fiber Reinforced Concrete (SFRC) is superior to that of conventional concrete. Due to its intricacy and limited available data, the development of a strength prediction model for SFRC is very difficult. To prevail over this constraint, research was carried out to build a deep learning algorithm for the prediction of flexural, split tensile and compressive strengths of SFRC. To accomplish this, a dataset was created by accumulating SFRC strengths through an extensive literature survey. Initially, the deep features of fine aggregate-cement ratio, coarse aggregate-cement ratio, water-cement ratio, fly ash-cement ratio, super plasticizer-cement ratio, length, diameter and dosage of fiber are learned through a convolutional neural network. Then softmax regression was used to develop a prediction model. The prediction model is trained and tested using 89 datasets with various mix ratios. From the results, we can conclude that the deep learning-based prediction model exhibits greater accuracy, greater efficiency and greater generalization capacity compared to that of the conventional neural network model.
Paperview paper 6207.pdf (556KB)

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