Machine learning for optimized buildings morphosis - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Machine learning for optimized buildings morphosis

Résumé

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.
Fichier principal
Vignette du fichier
Raboudi_BenSaci_2020_MachineLearning_AMC.pdf (377.46 Ko) Télécharger le fichier
Paper 10 - Machine learning for optimized buildings morphosis.mp4 (23.99 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03258021 , version 1 (11-06-2021)

Licence

Identifiants

Citer

Khaoula Raboudi, Abdelkader Ben Saci. Machine learning for optimized buildings morphosis. DTUC '20: Digital Tools & Uses Congress, ACM, Oct 2020, Virtual Event Tunisia, France. pp.1-5, ⟨10.1145/3423603.3424057⟩. ⟨hal-03258021⟩
62 Consultations
240 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More