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.
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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 |
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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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