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Machine learning for optimized buildings morphosis

Abstract : The world is rapidly urbanizing, with an increasing number of new building constructions. This involves increasing the world's energy consumption and its associated greenhouse gas emissions. Computational tools are playing an increasing impact on the architectural design process. Recently, Machine learning (ML) has been applied to building design and has evinced its potential to improve building performance. This paper tries to review the use of ML for the building morphosis. We then forecast the use of machine learning for building optimized morphosis in the early design stage particularly for ensuring summer shading and winter solar access between neighbors.
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Contributor : Abdelkader Ben Saci Connect in order to contact the contributor
Submitted on : Friday, June 11, 2021 - 11:47:31 AM
Last modification on : Wednesday, November 3, 2021 - 5:58:26 AM
Long-term archiving on: : Sunday, September 12, 2021 - 7:26:09 PM


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Khaoula Raboudi, Abdelkader Ben Saci. Machine learning for optimized buildings morphosis. E. Reyes, G. Kembellec, F. Siala-Kallel, L. Sfaxi, M. Ghenima, I. Saleh. DTUC '20: Digital Tools & Uses Congress, Oct 2020, Virtual Event Tunisia, Proceedings of the 2nd International Conference, Association for Computing Machinery, pp.1-5, 2020, 978-1-4053-7753-9. ⟨10.1145/3423603.3424057⟩. ⟨hal-03258021⟩



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