Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using Supervised Machine Learning Methods

The use of fly ash (FA)-based geopolymer concrete as a low-carbon and eco-friendly substitute to Portland cement concrete has gained attention in recent years. However, accurately predicting its compressive strength remains a challenge due to the complex chemical and physical interactions involved i...

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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) 2024-04, Vol.49 (4), p.4889-4904
Main Authors: Khan, Arslan Qayyum, Naveed, Muhammad Huzaifa, Rasheed, Muhammad Dawood, Miao, Pengyong
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation networks
Chemical composition
Compressive strength
Concrete
Engineering
Fly ash
Geopolymers
Humanities and Social Sciences
Machine learning
Mean square errors
multidisciplinary
Neural networks
Permutations
Portland cements
Research Article-Civil Engineering
Root-mean-square errors
Science
Silicon dioxide
Supervised learning
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Summary:The use of fly ash (FA)-based geopolymer concrete as a low-carbon and eco-friendly substitute to Portland cement concrete has gained attention in recent years. However, accurately predicting its compressive strength remains a challenge due to the complex chemical and physical interactions involved in the geopolymerization process. In this research, three machine learning models, namely backpropagation neural network (BPNN), random forest regression (RFR), and k-nearest neighbors (KNN), were employed to predict the compressive strength of FA-based geopolymer concrete. The models were trained, validated, and tested using a dataset that considered the chemical composition, mix proportions, and pre-curing conditions of the concrete. The performance of each model was assessed utilizing various metrics, including the coefficient of determination ( R 2 ), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results indicated that the BPNN model gave the best results with an R 2 value of 0.948 relative to RFR and KNN with R 2 values of 0.927 and 0.911, respectively. The permutation feature importance (PFI) index revealed that the coarse aggregate content, SiO 2 content in FA, and NaOH concentration were found to have the greatest impact on the compressive strength of the FA-based geopolymer concrete.
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-023-08283-w