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Exploring machine learning algorithms for automating complex processes in building and landscape architecture
1 Department of Architecture, College of Environmental Sciences, Joseph Ayo Babalola University, Osun State, Nigeria.
2 Department of Architecture, Faculty of Environmental Studies, University of Nigeria, Nsukka, Enugu, Nigeria.
Review
International Journal of Science and Research Archive, 2024, 13(02), 3611-3620
Article DOI: 10.30574/ijsra.2024.13.2.2603
Publication history:
Received on 13 November 2024; revised on 24 December 2024; accepted on 26 December 2024
Abstract:
This research examines the transformative impact of machine learning algorithms in building and landscape architecture automation. Through analysis of advanced deep learning and reinforcement learning systems, we demonstrate how these technologies enhance design optimization, environmental analysis, and sustainable landscape planning.
Our findings, based on case studies from Dubai and Barcelona, reveal significant improvements in building performance and resource management through machine learning automation. The Dubai International Financial Centre implementation demonstrated substantial energy consumption reduction through automated HVAC optimization while maintaining optimal comfort levels. At the Barcelona Botanical Gardens, the intelligent landscape irrigation system achieved 40% water conservation compared to traditional methods.
However, the research identifies critical challenges in data quality, algorithm reliability, and system integration. The successful implementation of architectural automation requires careful balance between computational capabilities and human expertise. Our analysis provides a framework for developing robust automation systems that maintain essential human oversight while leveraging machine learning's analytical power.
This study concludes that while machine learning significantly enhances architectural automation, its effective implementation depends on careful consideration of technical limitations and the maintenance of human judgment in critical decision-making processes.
Keywords:
Machine Learning; Architectural Automation; Design Optimization; Environmental Analysis; Landscape Architecture; Sustainable Design
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
