A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms
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Volume 34, Issue 7
Abstract
As a promising environmentally friendly construction material that can be used to replace concrete, fly ash-based geopolymer (FABG) should meet the working strength requirement. However, the optimal mixture design of FABG could be difficult to obtain through experimental methods due to a variety of influential factors and their complex interrelationships. To address this problem and explore the influence patterns of those factors, this study developed an ensemble machine learning modeling method that integrated three algorithms: support vector regressor (SVR), random forest regressor (RFR) and extreme gradient boosting (XGBoost). A database containing 896 experimental instances was constructed by reviewing open resources. During the modeling, established estimators were tuned with a metaheuristic algorithm called differential evolution (DE). After analysis, the XGBoost model was determined as the strength prediction model of FABG, because it showed the best performance with the largest scores (0.97 and 0.91) without overfitting by the minimum mean absolute error (MAE) gap between the training and testing subsets. Additionally, a further understanding of how the factors affect the predicted values of the model was given by the SHapley Additive exPlanations (SHAP) theory. The results show that curing conditions had the biggest impact on the model output, followed by alkali-activator solution variables and the mole of sodium hydroxide. Therefore, the proposed method can accurately predict the strength of produced FABG and assist in understanding the influence patterns of various factors.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research work is financially supported by National Nature Science Foundation of China (52078181, 51778029). Experimental data used in machine-learning modeling in this article are retrieved from open literature. All data sources are acknowledged.
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Journal of Materials in Civil Engineering
Volume 34 • Issue 7 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Jul 13, 2021
Accepted: Oct 29, 2021
Published online: Apr 23, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 23, 2022
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Cited by
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