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Zigeng Fang

Machine learning applications in Facility Management

School Research Theme: Facility Management
Research supervisors: Prof Michael Pitt; Dr Sean Hanna
Start date: 27 October 2017

One of the common challenges faced by facility/asset professionals is that they were exposed in an “information-saturated” and “data-rich” facility management environment, but, their knowledge and tools over how to utilize this rich FM information are scattered and limited. The existing building asset data remains un-serviced and neglected during many of strategic asset management decision-making processes. Machine Learning technology is proved to be able to tackle with data-related problems in many industries that have a vast amount of labelled or unlabeled data.

My research explores how facility management industry can achieve better portfolio-based strategic asset management through applying Machine Learning Technologies in asset data in all of building life-cycle phases.