Aref Maksoud, Hayder Basel Al-Beer, Aseel Ali Hussien, Samir Dirar, Emad Mushtaha, Moohammed Wasim Yahia


Introduction: With the rapid development in computational design, both architectural design and representation processes have witnessed a revolutionary change from the analog to the digital medium, opening new doors for adaptability in the architectural design process by leveraging nature concepts in design. The computational design approach starts with the mathematical model definition based on numerical relations and equations, thus, replacing the standard visual representation. Purpose of the study: We aimed to integrate computational design technologies to create self-learning buildings that could adapt to environmental challenges and adjust accordingly by collecting data from the surrounding environment via the implementation of sensors. Methods: We started with extensive research on state-of-the-art computational design in architecture, followed by the design implementation and the implementation of the architectural design of a building. The design followed a parametric approach to design and strategies. An algorithm was developed with Grasshopper Scripting to generate documents that mimic the growth process of cellular bone structures and adapt that form to a selected project site. To ensure that the generated form is adaptable, we performed multiple analyses, such as sunlight, radiation, and shadow analysis, before selecting the form and finishing its development. The results show that an environmentally responsive form that extends from the surrounding environment is characterized by high levels of adaptability. Results: In the course of the study, the effectiveness of computational design technologies in architecture was established.


computational design; Grasshopper; parametric design; architecture; adaptive design.

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Abdalla, S.B.; Rashid, M.; Yahia, M.W.; Mushtaha, E.; Opoku, A.; Sukkar, A.; Maksoud, A.; Hamad, R. Comparative Analysis of Building Information Modeling (BIM) Patterns and Trends in the United Arab Emirates (UAE) with Developed Countries. Buildings 2023, 13, 695. doi: 10.3390/buildings13030695

Ahmed, S. N. (2021). Covid, AI, and robotics - a neurologist’s perspective. Frontiers in Robotics and AI, Vol. 8, 617426. DOI: 10.3389/frobt.2021.617426.

Al-Azzawi, T. and Al-Majidi, Z. (2021). Parametric architecture: the second international style. IOP Conference Series: Materials Science and Engineering, Vol. 1067, 012019. DOI: 10.1088/1757-899X/1067/1/012019.

Alexander, D. (2020). 5 ways artificial intelligence is changing architecture. [online] Available at: [Date accessed 10 December 2022].

Assaf, T. (2021). A frequency modulation-based taxel array: a bio-inspired architecture for large-scale artificial skin. Sensors, Vol. 21, Issue 15, 5112. DOI: 10.3390/s21155112.

Birangal, G., Admane, S. V., and Shinde, S. S. (2015). Energy efficiency approach to intelligent building. International Journal of Engineering Research, Vol. 4, Issue 7, pp. 389–393. DOI: 10.17950/ijer/v4s7/711.

Caetano, Inês. Santos, Luís. Leitão, António. (2020) Computational design in architecture: Defining parametric, generative, and algorithmic design, Frontiers of Architectural Research, Volume 9, Issue 2, 2020, Pages 287-300, ISSN 2095-2635,

Caetano, Inês. Leitão, António. (2020) Architecture meets computation: an overview of the evolution of computational design approaches in architecture, Architectural Science Review, 63:2, 165-174, DOI: 10.1080/00038628.2019.1680524

Chen, C., Hu, Y., Karuppiah, M., and Kumar, P. M. (2021). Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustainable Energy Technologies and Assessments, Vol. 47, 101358. DOI: 10.1016/j.seta.2021.101358.

Chen, D. A., Ross, B. E., and Klotz, L. E. (2015). Lessons from a coral reef: biomimicry for structural engineers. Journal of Structural Engineering, Vol. 141, Issue 4, 02514002. DOI: 10.1061/(ASCE)ST.1943-541X.0001216.

Choi, J.-M., Won, M.-C., and Lee, S.-J. (2010). 63906 Vision based self learning mobile robot using machine learning algorithms (Robotics and Mechatronics). In: The Proceedings of the Asian Conference on Multibody Dynamics, 2010, Vol. 2010.5, 63906. DOI: 10.1299/jsmeacmd.2010.5._63906-1_.

Chua, S. L. (2013). Behaviour recognition in smart homes. Journal of Ambient Intelligence and Smart Environments, Vol. 5, No. 1, p. 133. DOI: 10.3233/AIS-120193.

Cortiços, N. D. (2019). Self-learning and self-repairing technologies to establish autonomous building maintenance. MATEC Web of Conferences, Vol. 278, 04004. DOI: 10.1051/matecconf/201927804004.

Cotrufo, N., Saloux, E., Hardy, J. M., Candanedo, J. A., and Platon, R. (2020). A practical artificial intelligence-based approach for predictive control in commercial and institutional buildings. Energy and Buildings, Vol. 206, 109563. DOI: 10.1016/j.enbuild.2019.109563.

Cubukcuoglu, C., Ekici, B., Tasgetiren, M. F., and Sariyildiz, S. (2019). OPTIMUS: self-adaptive differential evolution with ensemble of mutation strategies for Grasshopper algorithmic modeling. Algorithms, Vol. 12, Issue 7, 141. DOI: 10.3390/a12070141.

Dimitropoulos, K., Daras, P., Manitsaris, S., Fol Leymarie, F., and Calinon, S. (2021). Editorial: artificial intelligence and human movement in industries and creation. Frontiers in Robotics and AI, Vol. 8, 712521. DOI: 10.3389/frobt.2021.712521.

Emaminejad, N. and Akhavian, R. (2022). Trustworthy AI and robotics: implications for the AEC industry. Automation in Construction, Vol. 139, 104298. DOI: 10.1016/j.autcon.2022.104298. (2020). Emirates GBC 2020 Green Building Market Brief. [online] Available at: [Date accessed 12 December 2022].

Estévez, A. T. and Navarro, D. (2017). Biomanufacturing the future: biodigital architecture & genetics. Procedia Manufacturing, Vol. 12, pp. 7–16. DOI: 10.1016/j.promfg.2017.08.002.

Hendy, A. M. (2020). The new design considerations in the residential buildings’ interiors at the post-Corona (COVID-19) era. Journal of Advanced Research in Dynamical and Control Systems, Vol. 12, Special Issue 8, pp. 444–458. DOI: 10.5373/JARDCS/V12SP8/20202544.

Hutson, M. (2017). How artificial intelligence could negotiate better deals for humans. [online] Available at: [Date accessed 12 December 2022].

Jaruga-Rozdolska, A. (2022). Architektura 4.0: proces projektowania wspierany przez sztuczną inteligencję Potencjał wykorzystania skryptu generatywnego MidJourney w procesie tworzenia koncepcji architektonicznej. Builder, Vol. 303, No. 10, pp. 66–69. DOI: 10.5604/01.3001.0015.9893.

Joshi, N. (2019). How AI is making buildings smart and intelligent. [online] Available at: [Date accessed 1 January 2023].

Kurtoglu, T., Campbell, M. I., and Linsey, J. S. (2009). An experimental study on the effects of a computational design tool on concept generation. Design Studies, Vol. 30, Issue 6, pp. 676–703. DOI: 10.1016/j.destud.2009.06.005.

Maksoud, A., Mushtaha, E., Al-Sadoon, Z., Sahall, H., and Toutou, A. (2022). Design of Islamic parametric elevation for interior, enclosed corridors to optimize daylighting and solar radiation exposure in a desert climate: a case study of the University of Sharjah, UAE. Buildings, Vol. 12, Issue 2, 161. DOI: 10.3390/buildings12020161.

Maksoud A., Mushtaha E., Chouman L., Al Jawad E., Samra S.A., Sukkar A., Yahia M.W.

Study on Daylighting Performance in the CFAD Studios at the University of Sharjah

(2022) Civil Engineering and Architecture, 10 (5), pp. 2134 - 2143, DOI: 10.13189/cea.2022.100532.

Mehra, S. and Sharma, R. (2019). Performance analysis of artificial intelligence based MPPT techniques for a solar system under changing environmental conditions. In: Proceedings of ICAEEC-2019, IIIT Allahabad, India, May 31 – June 1, 2019. DOI: 10.2139/ssrn.3573604.

Mushtaha, Emad; Alsyouf, Imad; Hamad, Rawan; Elmualim, Abbas; Maksoud, Aref ;Yahia Moohammed Wasim (2022). Developing design guidelines for university campus in hot climate using Quality Function Deployment (QFD): the case of the University of Sharjah, UAE, Architectural Engineering and Design Management, 18:5, 593-613, DOI: 10.1080/17452007.2022.2041386

Naboni, R. and Kunic, A. (2017). Design and additive manufacturing of lattice-based cellular solids at building scale. Blucher Design Proceedings, Vol. 3, No. 12, pp. 369–375. DOI: 10.5151/sigradi2017-058.

Nisztuk M, Myszkowski PB. Usability of contemporary tools for the computational design of architectural objects: Review, features evaluation and reflection. International Journal of Architectural Computing. 2018;16(1):58-84. doi:10.1177/1478077117738919

Oberste-Ufer, K. (2019). 7 ways artificial intelligence is revolutionizing architecture. [online] Available at: [1 January 2023].

Oxman, R. (2017). Thinking difference: theories and models of parametric design thinking. Design Studies, Vol. 52, pp. 4–39. DOI: 10.1016/j.destud.2017.06.001.

Pala, Z. and Özkan, O. (2020). Artificial intelligence helps protect smart homes against thieves. DUJE (Dicle University Journal of Engineering), Vol. 11, Issue 3, pp. 945–952. DOI: 10.24012/dumf.700311.

Philips, M. (2020). The present and future of AI in design (with infographic). [online] Available at:,adjustments%20based%20on%20that%20data [4 January 2023].

Rocha, H. R. O., Honorato, I. H., Fiorotti, R., Celeste, W. C., Silvestre, L. J., and Silva, J. A. L. (2021). An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes. Applied Energy, Vol. 282, Part A, 116145. DOI: 10.1016/j.apenergy.2020.116145.

Roudsari, M. S., Pak, M., and Viola, A. (2013). Ladybug: a parametric environmental plugin for Grasshopper to help designers create an environmentally-conscious design. In: Proceedings of Building Simulation 2013: 13th Conference of IBPSA, Chambéry, France, August 26–28, 2013, pp. 3128–3135. DOI: 10.26868/25222708.2013.2499.

Shishina, D. and Sergeev, P. (2019). Revit|Dynamo: designing objects of complex forms. toolkit and process automation features. Architecture and Engineering, Vol. 4, Issue 3, pp. 30–38. DOI: 10.23968/2500-0055-2019-4-3-30-38.

Wang, H. (2011) Real-time data-based fault diagnosis system. Advanced Materials Research, Vol. 189–193, pp. 2621–2624. DOI: 10.4028/

Wollerton, M. (2018). Nest Learning Thermostat (3rd Gen) review: same great Nest, now with a temperature sensor. [online] Available at: [Date accessed 12 December 2022].

Zhao, Y., Li, T., Zhang, X., and Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: advantages, challenges and the future. Renewable and Sustainable Energy Reviews, Vol. 109, pp. 85–101. DOI: 10.1016/j.rser.2019.04.021.


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