Modelling of the Strength of High Performance Concrete using Machine Learning Models
Sangram Biplab Manabendra Thakur
, Paresh Biswal
Fly ash (FA), Ground Granulated Blast Furnace Slag (GGBS), compressive strength, machine learning models
Many research work has shown that High-performance concrete may be a profoundly complex fabric, which makes modelling its behaviour a very troublesome assignment. This paper is pointed at illustrating the conceivable outcomes of adapting the leading conceivable machine learning model to anticipate the compressive strength of high-performance concrete. We are using a data set where the input components are as follows Cement -- kg per m3, Blast Furnace Slag -- kg per m3, Fly Ash -- kg per m3, Water -- kg per m3, SP's -- kg per m3, Coarse Aggregate -- kg per m3, Fine Aggregate -- kg per m3, Age -- Day (1~365). Whereas my output components will be Concrete compressive strength -- MPa. The present study leads to the following conclusion Bootstrap Random Forest classification model performance is better than other machine learning algorithms. Subsequently utilizing the machine learning model will not as it were offer assistance in foreseeing the strength but moreover will be valuable in making a prediction of materials required eventually it'll offer assistance in lessening the wastage of material
"Modelling of the Strength of High Performance Concrete using Machine Learning Models", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 5, page no.174 - 179, May-2021, Available :https://ijsdr.org/papers/IJSDR2105032.pdf
Volume 6
Issue 5,
May-2021
Pages : 174 - 179
Paper Reg. ID: IJSDR_193304
Published Paper Id: IJSDR2105032
Downloads: 000347187
Research Area: Engineering
Country: Rayagada, Odisha, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
