TR214: Predicting Space Utilization of Buildings through Integrated and Automated Analysis of User Activities and Spaces

<a href="https://embed.stanford.edu/iframe/?url=https%3A%2F%2Fpurl.stanford.edu%2Ftm100bc3326" class="su-underline">Show Content</a>

Abstract/Contents

Abstract: A well-functioning building aligns the types and number of spaces with the activities of the building users. Throughout design, architects have to predict the utilization of spaces quickly and consistently. This study presents a knowledge-based space-use analysis (KSUA) method that integrates user activity and space information. Specific contributions enabling this method are a method for mapping user activities onto appropriate spaces and an ontology for representing user activities for use in space-use analysis. Tests with novice architects show that they can update predictions about space utilization 6.5 times faster with the KSUA method than with today’s method and do so much more consistently (the standard deviation of predictions across the novice architects was 68% less with the method). Tests also show that the performance of novice architects with the KSUA method outweighs the performance of expert architects without the method. Deployment of the method in practice should enable better designed buildings and more productive building users.

Type of resource	text
Date created	June 2013

Author	Kim, Tae Wan

Subject	CIFE
Subject	Center for Integrated Facility Engineering
Subject	Stanford University
Subject	Charrette
Subject	Knowledge representation and reasoning
Subject	Planning
Subject	Space-use analysis
Subject	User activity
Subject	VDC
Subject	Virtual Design and Construction
Genre	Technical report

Location	https://purl.stanford.edu/tm100bc3326

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.

Preferred Citation: Kim, Tae Wan. (2013). TR214: Predicting Space Utilization of Buildings through Integrated and Automated Analysis of User Activities and Spaces. Stanford Digital Repository. Available at: http://purl.stanford.edu/tm100bc3326

CIFE Publications

Loading usage metrics...