Artificial Intelligence:

Leading Architecture on a Futuristic Field Trip!

Many positive outcomes have resulted from architects’ use of AI. One benefit is that it allows architects to replicate and evaluate alternative design options quickly. It has led to better decisionmaking and more creative design solutions, as Soliman et al. (2019) discovered. Mainly in residential and commercial building design, where customization is vital, a tailored design solution bears the tremendous appeal that AI may deliver (Kloeckner 2018).

Artificial intelligence is also used in design decision-making by analysing large datasets. As a result, architects gain valuable insights into user preferences, market trends, and historical design data. This analytical approach empowers architects to make design decisions with greater discernment and ensures that their creations align harmoniously with the wide range of user needs and preferences (Karan et al., 2020; Gilner et al., 2019).

Various aspects of sustainability, such as energy efficiency, daylight utilization, thermal comfort, and the wise use of materials, are evaluated and improved with the help of artificial intelligence simulations. Designs that prioritize environmental sustainability and sustainability, in general, may be created using cutting-edge technology. Because of this, we can reduce our energy use, save costs, and lessen our footprint on the planet (Merabet et al., 2021). AI-enabled intelligent building systems as discussed by Panchalingam and Chan (2021) allow real-time monitoring and analysis of building performance data.

Architects can use this method to identify areas that require improvement and make changes to optimize energy efficiency and improve occupant comfort.

Artificial intelligence systems facilitate the cooperation of multidisciplinary groups with distinct areas of expertise by providing a central hub for the sharing and iterative improvement of designs. This occurrence leads to better communication and a more unified, collaborative design process (Saba et al., 2021). Risk mitigation involves the utilization of artificial intelligence to thoroughly analyse historical project data in order to identify and predict potential risks and challenges that may arise. According to Kayis et al. (2007), using AI architects can proactively address important issues and effectively prevent project delays.

Cost estimation tools employ sophisticated artificial intelligence algorithms to analyse complex design parameters and extensive historical cost data with meticulous accuracy. The projected project costs are reliable and precise. Therefore, it facilitates efficient financial resource management for architects (Mislick & Nussbaum, 2015). According to Wan et al (2020) the computing powers of AI tools are so advanced that they can accurately conduct complicated computations and simulations. Consequently, they cut down on design mistakes and the need for expensive fixes. Realistic visual representations of designs are made possible by virtual and augmented reality technologies powered by artificial intelligence, which helps customers and stakeholders better grasp the result.

The importance of resource efficiency in the construction industry cannot be overstated. Artificial intelligence (AI) has become a significant asset in tackling this concern. Using AI enables us to maximize material efficiency, streamline construction procedures, and improve the allocation of resources. This process, in turn, significantly reduces waste and fosters the adoption of more sustainable construction practices (Waltersmann, 2021). In case of renovations, artificial intelligence is crucial in analysing relevant data on existing structures. The analytical process outlined here serves as a guiding framework for renovation and retrofit projects, ensuring that any upgrades implemented align seamlessly with the building’s performance and specific needs (Cisterna, 2021).

According to Cardoso and Ferreira (2020), predictive maintenance involves using artificial intelligence to systematically monitor and analyse the different systems and equipment within a building. Conducting a thorough analysis allows us to identify and predict maintenance needs, ensuring optimal functionality is achieved and costly breakdowns are avoided. Lastly, artificial intelligence algorithms enable the perpetual acquisition of knowledge from each design endeavour, accumulating a repository of wisdom and discernment that can be effectively applied to subsequent projects, thereby facilitating continuous improvement and refinement (Kontogiannis &

Selfridge 1995).

Embracing AI applications in architecture allows professionals to tap into their creative abilities, producing visually captivating designs that excel in functionality, sustainability, and adaptability. It involves providing buildings with a specialized solution that addresses the requirements of inhabitants and the environment.

Footnotes

B., Kayis., G., Arndt., Mingwei, Zhou., S., Amornsawadwatana. (2007). A Risk Mitigation Methodology for New Product and Process Design in Concurrent Engineering Projects. CIRP Annals, 56(1):167-170. doi: 10.1016/J.CIRP.2007.05.040

Djamel, Saba., Youcef, Sahli., Abdelkader, Hadidi. (2021). The Role of Artificial Intelligence in Company’s Decision Making. 287-314. doi: 10.1007/978-3-030-52067-0_13

Diego, Cisterna., Simon, Seibel., Svenja, Oprach., Shervin, Haghsheno. (2021). Artificial Intelligence for the Construction Industry - A Statistical Descriptive Analysis of Drivers and Barriers. 283-295. doi: 10.1007/978-3-030-87687-6_27

Diogo, Cardoso., Luís, Carlos, de, Souza, Ferreira. (2020). Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools. Applied Sciences, 11(1):18-. doi: 10.3390/ APP11010018

Ebrahim, Karan., Mahdi, Safa., Min, Jae, Suh. (2020). Use of Artificial Intelligence in a Regulated Design Environment – A Beam Design Example. 16-25. doi: 10.1007/978-3-030-512958_2

Ewa, Gilner., Adam, Galuszka., Tomasz, Grychowski. (2019). Application of Artificial Intelligence in Sustainable Building Design - Optimisation Methods. 81-86. doi: 10.1109/ MMAR.2019.8864698

Ghezlane, Halhoul, Merabet., Ghezlane, Halhoul, Merabet., Mohamed, Essaaidi., Mohamed, Ben, Haddou., Basheer, Qolomany., Junaid, Qadir., Muhammad, Anan., Ala, Al-Fuqaha., Ala, Al-Fuqaha., Mohamed, Riduan, Abid., Driss, Benhaddou. (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligenceassisted techniques. Renewable & Sustainable Energy Reviews, 144:110969-. doi: 10.1016/J.RSER.2021.110969

Gregory, K., Mislick., Daniel, A., Nussbaum. (2015). Cost Estimation: Methods and Tools.

Jiafu, Wan., Jianqi, Liu., Lingxia, Liao. (2020). Guest Editorial: Special Issue on “Advanced Artificial Intelligence for Industrial Internet of Things”. Journal of Internet Technology, 21(5):14771478.

Kristof, Kloeckner., John, Davis., Nicholas, C., M., Fuller., Giovanni, Lanfranchi., Stefan, Pappe., Amit, Paradkar., Larisa, Shwartz., Maheswaran, Surendra., Dorothea, Wiesmann. (2018). AI for Solution Design. 57-73. doi: 10.1007/978-3-319-94048-9_4

Kostas, Kontogiannis., Peter, G., Selfridge. (1995). Workshop report: The two-day workshop on Research Issues in the Intersection between Software Engineering and Artificial Intelligence (held in conjunction with ICSE-16). 2(1):87-97. doi: 10.1007/BF00873410

Lara, Waltersmann., Steffen, Kiemel., Julian, Stuhlsatz., Alexander, Sauer., Robert, Miehe. (2021). Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review. Sustainability, 13(12):6689-. doi: 10.3390/SU13126689

Seungsu, Paek., Namhyoung, Kim. (2021). Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education. Sustainability, 13(14):7941-. doi: 10.3390/SU13147941

Sara, Saad, Soliman., Dina, Taha., Zeyad, Tarek, El, Sayad. (2019). Architectural education in the digital age: Computer applications: Between academia and practice. alexandria engineering journal, 58(2):809-818. doi: 10.1016/J.AEJ.2019.05.016

Rav, Panchalingam., Ka, Ching, Chan. (2021). A state-ofthe-art review on artificial intelligence for Smart Buildings. Intelligent Buildings International, 13(4):203-226. doi: 10.1080/17508975.2019.1613219

Sharika Tasnim

Tasnim maintains a lifelong passion for architecture, having been involved with diverse professional and learning experiences across the U.S., U.K., and other countries. She is a Committee Member at the Royal Institute of British Architects (RIBA).