Advanced prediction of CO2 emissions and energy consumption in high-performance concrete manufacturing
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Xianghua Zhang
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Yajuan Li
Abstract
This study addresses the urgent need to reduce environmental impacts in the construction industry by improving the prediction of CO2 emissions and energy consumption during the production of High-Performance Concrete (HPC). To tackle this challenge, we employed three machine learning (ML) regression models: K-Nearest Neighbor Regression (KNNR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting Regression (XGBR) to examine the complex patterns influencing sustainability metrics in HPC manufacturing. Each model was further enhanced using the Fitness Dependent Optimizer (FDO) to increase predictive accuracy and decrease error rates. We evaluated model performance through training, validation, and testing phases, focusing on two objectives: CO2 emissions and energy usage. Among all the approaches tested, the KNNR and its hybrid version (KNFD) achieved the lowest Weighted Absolute Percentage Error (WAPE) of 0.002 for the energy prediction task. The XGBR and XGFD models followed close behind with WAPEs of 0.05 and 0.04, respectively, outperforming the GBR and GBFD models. Feature selection based on the F-statistic identified “cement” as the most crucial variable among the eight input features. These findings demonstrate the effectiveness of FDO-enhanced regression models in delivering accurate, low-error predictions, which are essential for sustainability-oriented initiatives in construction. This research establishes a solid foundation for data-driven strategies aimed at reducing environmental impacts in HPC production.
Funding source: the self-funded project of Xingtai Key Research and Development Plan
Award Identifier / Grant number: 2024ZC065
Acknowledgments
This work was supported by the self-funded project of Xingtai Key Research and Development Plan (2024ZC065).
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “ Xianghua ZHANG，Yajuan LI, and Bingjie WANG”. The first draft of the manuscript was written by “ Xianghua ZHANG” and all authors commented on previous versions of the manuscript. All authors have read and approved the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The authors do not have permissions to share data.
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