Research on Innovative Applications of AI in Sustainable Architecture: Blueprint for Future Building Technology

Jingwen He; Haoran Xu; Xinshi Li; Qian Meng

doi:10.2991/978-94-6463-726-7_39

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Research on Innovative Applications of AI in Sustainable Architecture: Blueprint for Future Building Technology

Authors

Jingwen He¹, Haoran Xu², Xinshi Li³, Qian Meng⁴^{, *}

¹Samfox School of Architecture, Washington University in St. Louis, St. Louis, CA, 94608, USA

²Graduate School of Architecture, Planning and Preservation, Columbia University, New York, 10027, USA

³Feliciano School of Business, Montclair State University, Montclair, NJ, 07043, USA

⁴School of Architecture and Design, University of Technology Sydney, Sydney, 2007, Australia

^*Corresponding author. Email: mengqian519@gmail.com

Corresponding Author

Qian Meng

Available Online 13 June 2025.

DOI: 10.2991/978-94-6463-726-7_39 How to use a DOI?
Keywords: Sustainable Construction; Artificial Intelligence; Energy Optimization; Dynamic Control
Abstract: With increasing concerns over climate change and resource scarcity, sustainable construction has become a key priority. The proposed model integrates a multi-layer AI structure with three modules: data collection, intelligent prediction, and dynamic control. IoT sensors first gather real-time environmental data, which the AI system uses to operate effectively. A deep learning network then predicts building energy demand, enabling dynamic energy adjustments using LSTM neural networks. Finally, reinforcement learning algorithms adaptively control energy and environmental systems, incorporating climate predictions, building thermal properties, and energy prices to maximize efficiency. The model dynamically adjusts energy consumption based on predicted demand and factors such as climate forecasts, building thermal properties, and energy prices. Experimental simulations confirm the model’s effectiveness in reducing energy use and emissions across various building scenarios.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2024 6th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2024)
Series: Atlantis Highlights in Engineering
Publication Date: 13 June 2025
ISBN: 978-94-6463-726-7
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-726-7_39 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Jingwen He
AU  - Haoran Xu
AU  - Xinshi Li
AU  - Qian Meng
PY  - 2025
DA  - 2025/06/13
TI  - Research on Innovative Applications of AI in Sustainable Architecture: Blueprint for Future Building Technology
BT  - Proceedings of the 2024 6th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2024)
PB  - Atlantis Press
SP  - 402
EP  - 408
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-726-7_39
DO  - 10.2991/978-94-6463-726-7_39
ID  - He2025
ER  -

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