Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques.

IF 7.7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Yakubu Sani Wudil, Amin Al-Fakih, Mohammed A Al-Osta, M A Gondal
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引用次数: 0

Abstract

In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO2-FP) of fly ash geopolymer concrete. The approach employs adaptive boosting to enhance decision tree regression (DTR) and support vector regression (SVR), resulting in a robust predictive framework. The models used key material features, including fly ash concentration, fine and coarse aggregates, superplasticizer, curing temperature, and alkali activator levels. These features were tested across three configurations (Combo-1, Combo-2, Combo-3) to determine optimal predictor combinations, with Combo-3 consistently yielding the highest predictive accuracy. The performance of the developed models was assessed based on standard metric indicators like mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), and correlation coefficient between the predicted and actual CO2-FP. Results demonstrated that the Adaboost-DTR model with Combo-3 configuration achieved the best performance metrics during testing (CC = 0.9665; NSE = 0.9343), outperforming both standalone and other ensemble models. The findings underscore the value of feature selection and boosting techniques in accurately estimating CO2 emissions for sustainable construction applications. This research offers remarkable benefits for policymakers and industry stakeholders aiming to optimize concrete compositions for environmental sustainability. The results support future integration with IoT systems to enable real-time CO2 monitoring in construction materials. Finally, this study establishes a foundation for developing efficient CO2-FP emission management tools.

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来源期刊
Environmental Research
Environmental Research 环境科学-公共卫生、环境卫生与职业卫生
CiteScore
12.60
自引率
8.40%
发文量
2480
审稿时长
4.7 months
期刊介绍: The Environmental Research journal presents a broad range of interdisciplinary research, focused on addressing worldwide environmental concerns and featuring innovative findings. Our publication strives to explore relevant anthropogenic issues across various environmental sectors, showcasing practical applications in real-life settings.
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