Publication: Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials
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Issued Date
2021
Resource Type
Language
eng
File Type
application/pdf
ISSN
19961944
Other identifier(s)
2-s2.0-85116491580
Rights Holder(s)
Scopus
Bibliographic Citation
Materials. Vol 14, No.19 (2021)
Suggested Citation
Ahmad W., Ahmad A., Ostrowski K.A., Aslam F., Joyklad P., Zajdel P. Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials. Materials. Vol 14, No.19 (2021). doi:10.3390/ma14195762 Retrieved from: https://hdl.handle.net/20.500.14740/8177
Author(s)
Abstract
The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2 ), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subject(s)
Adaptive boosting
Blast furnaces
Compressive strength
Decision trees
Errors
Fly ash
Forecasting
Gene expression
Mean square error
Sensitivity analysis
Slags
Supervised learning
Coefficient of determination
Compressive strength of concrete
Gene-expression programming
Machine learning approaches
Machine learning techniques
Mean absolute error
Performance
Properties of concretes
Supervised machine learning
Supplementary cementitious material
Concretes
Blast furnaces
Compressive strength
Decision trees
Errors
Fly ash
Forecasting
Gene expression
Mean square error
Sensitivity analysis
Slags
Supervised learning
Coefficient of determination
Compressive strength of concrete
Gene-expression programming
Machine learning approaches
Machine learning techniques
Mean absolute error
Performance
Properties of concretes
Supervised machine learning
Supplementary cementitious material
Concretes