Artificial Intelligence for Sustainable Building Materials
DOI:
https://doi.org/10.62051/ijcsit.v6n1.03Keywords:
Artificial Intelligence, Sustainable Construction, Green Building Materials, Machine Learning, ChatGPT, Bio-inspired DesignAbstract
This paper reviews the role of artificial intelligence (AI), with a focus on machine learning (ML) and tools like ChatGPT, in advancing sustainable building materials. As the construction industry seeks to reduce its environmental footprint, AI offers innovative methods for discovering low-carbon alternatives, optimizing material composition, and designing bio-inspired structures. A narrative literature review was conducted, synthesizing peer-reviewed publications from 2014 to 2024. The findings are categorized into three main areas: low-carbon material discovery, ML-based material optimization, and generative AI for bio-inspired design. Results show that AI technologies significantly contribute to improving sustainability and performance while identifying key barriers such as high computational costs, limited interdisciplinary collaboration, and slow industry adoption. The paper concludes that while AI shows strong potential, further research and institutional support are essential for widespread implementation.
Downloads
References
[1] C. Debrah, A. P. Chan, and A. Darko, "Artificial intelligence in green building," Automation in Construction, vol. 137, 104192, 2022.
[2] D. Adio-Moses and O. S. Asaolu, "Artificial intelligence for sustainable development of intelligent buildings," in Proc. 9th CIDB Postgraduate Conf., Cape Town, South Africa: Univ. of Cape Town, 2016, pp. 1–10.
[3] Y. Xiang, Y. Chen, J. Xu, and Z. Chen, "Research on sustainability evaluation of green building engineering based on artificial intelligence and energy consumption," Energy Reports, vol. 8, pp. 11378–11391, 2022.
[4] B. Manzoor, I. Othman, S. Durdyev, S. Ismail, and M. H. Wahab, "Influence of artificial intelligence in civil engineering toward sustainable development—a systematic literature review," Applied System Innovation, vol. 4, no. 3, p. 52, 2021.
[5] D. Rodríguez-Gracia, M. M. Capobianco-Uriarte, E. Terán-Yépez, J. A. Piedra-Fernández, L. Iribarne, and R. Ayala, "Review of artificial intelligence techniques in green/smart buildings," Sustainable Computing: Informatics and Systems, vol. 38, 100861, 2023.
[6] K. T. Ateş, C. Şahin, Y. Kuvvetli, B. A. Küren, and A. Uysal, "Sustainable production in cement via artificial intelligence based decision support system: Case study," Case Studies in Construction Materials, vol. 15, e00628, 2021.
[7] N. Rane, S. Choudhary, and J. Rane, "Acceptance of artificial intelligence: key factors, challenges, and implementation strategies," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4842167
[8] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence and machine learning in business-to-business (B2B) sales and marketing: a review," Int. J. Data Sci. Big Data Analytics, vol. 4, no. 1, pp. 17–33, 2024.
[9] M. N. Amin, W. Ahmad, K. Khan, and A. Ahmad, "A comprehensive review of types, properties, treatment methods and application of plant fibers in construction and building materials," Materials, vol. 15, no. 12, 4362, 2022.
[10] F. Monika, H. Prayuda, M. D. Cahyati, E. N. Augustin, H. A. Rahman, and A. D. Prasintasari, "Engineering properties of concrete made with coal bottom ash as sustainable construction materials," Civil Engineering Journal, vol. 8, no. 1, pp. 181–194, 2022.
[11] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence and machine learning for resilient and sustainable logistics and supply chain management," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4847087
[12] M. Jones, A. Mautner, S. Luenco, A. Bismarck, and S. John, "Engineered mycelium composite construction materials from fungal biorefineries: A critical review," Materials & Design, vol. 187, 108397, 2020.
[13] S. Gounder, A. Hasan, A. Shrestha, and A. Elmualim, "Barriers to the use of sustainable materials in Australian building projects," Engineering, Construction and Architectural Management, vol. 30, no. 1, pp. 189–209, 2023.
[14] L. Tupenaite, L. Kanapeckiene, J. Naimaviciene, A. Kaklauskas, and T. Gecys, "Timber construction as a solution to climate change: A systematic literature review," Buildings, vol. 13, no. 4, 976, 2023.
[15] N. Hossaini and K. Hewage, "Sustainable materials selection for Canadian construction industry: An emergy-based life cycle analysis (Em-LCA) of conventional and LEED suggested construction materials," J. Sustainable Development, vol. 5, no. 1, pp. 2–15, 2012.
[16] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence, machine learning, and deep learning for sentiment analysis in business to enhance customer experience, loyalty, and satisfaction," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4846145
[17] Y. Goh, S. P. Yap, and T. Y. Tong, "Bamboo: the emerging renewable material for sustainable construction," in Encyclopedia of Renewable and Sustainable Materials, vol. 2, pp. 365–376, 2020.
[18] J. Abed, S. Rayburg, J. Rodwell, and M. Neave, "A review of the performance and benefits of mass timber as an alternative to concrete and steel for improving the sustainability of structures," Sustainability, vol. 14, no. 9, 5570, 2022.
[19] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence and machine learning in business intelligence, finance, and e-commerce: A review," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4843988
[20] A. Korjenic, S. Klarić, A. Hadžić, and S. Korjenic, "Sheep wool as a construction material for energy efficiency improvement," Energies, vol. 8, no. 6, pp. 5765–5781, 2015.
[21] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence driven approaches to strengthening environmental, social, and governance (ESG) criteria in sustainable business practices: a review," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4843215
[22] N. Rane, S. Choudhary, and J. Rane, "Artificial intelligence enhanced environmental, social, and governance (ESG) strategies for financial services and investment sectors," SSRN Electronic Journal, 2024. [Online]. Available: http://dx.doi.org/10.2139/ssrn.4842635
[23] S. Bianchi, S. Rossi, and R. Puglisi, "Enhancing mechanical and bioinspired materials through generative AI approaches," Next Materials, vol. 6, 100275, Jan. 2025. [Online]. Available from: https://doi.org/10.1016/j.nxmate.2024.100275
[24] Zhu, J., Wen, C., & Li, R. (2024). Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model. Axioms, 13(4), 218. https://doi.org/10.3390/axioms13040218
[25] Zhu, J., Li, R., Chen, Z., & Zhang, T. (2025). Does ownership structure influence the financial performance of Chinese listed companies? An Analysis of ESG Practices and Accounting-Based Outcomes. International Journal of Financial Studies, 13(2), 48. https://doi.org/10.3390/ijfs13020048
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Computer Science and Information Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
