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Advances in Environmental Research Volume 13, Number 1, March 2024 , pages 051-69 DOI: https://doi.org/10.12989/aer.2024.13.1.051 |
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Addressing environmental concerns in concrete design through artificial intelligence: A sustainable approach |
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Sina Aminbakhsh, Ali Golsoorat Pahlaviani and Amin Tohidi
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| Abstract | ||
| This paper addresses the urgent need for sustainable infrastructure by examining the application of artificial intelligence in concrete design, with a specific emphasis on predicting uniaxial compressive strength (UCS) to mitigate environmental impacts. Concrete, a fundamental construction material, is a major contributor to environmental degradation due to its substantial carbon footprint. The study focuses on harnessing the potential of Artificial Neural Networks (ANN) to forecast UCS in conventional construction concrete, which is extensively utilized in global construction practices. This emphasis stems from the recognition of the significant environmental consequences associated with these concrete types, affecting both carbon footprints and ecosystems. The research methodology involved analyzing a dataset comprising 300 cubic concrete specimens with dimensions of 15 cm x 15 cm x 15 cm, split into training and testing sets at a ratio of 70:30. Various machine learning classifiers, including Support Vector Machine and Decision Tree, were employed for comparison alongside the ANN model. Results demonstrated that the ANN-based predictive model outperformed alternative classifiers, achieving high accuracy rates and minimal error values, thereby affirming its reliability in estimating UCS values. These findings highlight the potential of integrating AI technologies to enhance sustainability in construction practices and mitigate environmental impacts associated with concrete usage. By adopting innovative approaches such as ANN prediction models, the construction industry can contribute significantly to environmental preservation and sustainable development efforts. | ||
| Key Words | ||
| artificial intelligence; concrete; construction management; environmental impact; sustainable structures | ||
| Address | ||
| Sina Aminbakhsh and Ali Golsoorat Pahlaviani: Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1955847781, Iran Amin Tohidi: Department of Mining Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran | ||
