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Machine learning in facilities & asset management

Fang, Z; Pitt, M; Hanna, S; (2019) Machine learning in facilities & asset management. In: Proceedings of the 25th annual Pacific rim real estate society (PRRES) conference. PRRES: Melbourne, Australia. Green open access

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Abstract

In this article, we explore a machine learning approach that helps the Facility Management (FM) manager and FM data analyst to do the FM data clustering and classification automatically. Through experiment the popular machine learning algorithm, we examine how the current available Machine Learning (ML) & Natural Language Processing (NLP) technic can help solve the interoperability issue faced in the field of FM. The finding of this research indicates that: 1. The deep learning network is able to classify the building assets according to their group elements (level three of NRM’s Elemental Standard of Cost Analysis) with a high accuracy rate (by more than ninety percent accuracy);2. The Convolutional Neural Network (CNN) Classifier can achieve better accuracy performance than the junior building data analyst; 3. The Unsupervised Skip-gram Gradient Descent Model can cluster the words in the document into different groups; 4. The Unsupervised Skip-gram Gradient Descent Model can reveal the hidden relationship inside the FM data. For future researches and projects, this research enlightens the future direction of applying ML/NLP techniques in the field of FM.

Type: Proceedings paper
Title: Machine learning in facilities & asset management
Event: The 25th annual Pacific rim real estate society (PRRES) conference
Location: Melbourne, Australia
Dates: 14 January 2019 - 16 January 2019
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.prres.net/index.htm?http://www.prres.ne...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Facility management, Machine Learning, Natural Language Processing, Text Classification, Interoperability, RICS NRM: New Rules of Measurement.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett Sch of Const and Proj Mgt
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture
URI: https://discovery.ucl.ac.uk/id/eprint/10080660
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