A Machine Learning Approach Towards Determining The Openness Of Urban Plaza

Abstract

The design of urban plaza is guided by the principle of D/H ratio where D denotes distance and H denotes building façade height which provides a quantitative measure of the enclosure. Plaza has been considered as an outdoor room and the buildings are the walls. But these urban walls are not continuous. Connecting roads, voids between buildings, the variation of building heights, and the omission of building on any side of the plaza affect openness. So, maintaining the same D/H ratio the sense of enclosure can be varied. This paper aims at determining the inter-relation of openness with distance and height for better understanding the idea of enclosure of urban plaza using machine learning algorithms. Machine learning can be used to determine the non-linear relationship between multiple variables. The variables D and H are set by the author where the perforation of the surrounding elevation varied, then respondents were asked to rate the degree of openness of the plazas based on their virtual journey using a head-mounted Virtual Reality (VR) display. Utilizing their responses an inter-relation among the parameters is determined by training up an artificial Neural Network (ANN) to predict the openness of any plaza. This can be used as a process of analyzing user experience of urban plazas.