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.