Machine learning and BIM visualization for maintenance issue classification and enhanced data collection. (October 2018)

• Record Type: Journal Article
• Authors: McArthur, J.J.; Shahbazi, Nima; Fok, Ricky; Raghubar, Christopher; Bortoluzzi, Brandon; An, Aijun
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 101
• Page End: 112
• Publication Date: 2018-10
• Subjects: Machine learning -- Building information modelling -- Visualization -- Facility management -- Predictive models -- Work orders; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.06.007
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Automatic classification of WOs enhances and structures occupant-generated data. Category prediction accuracy 57–86% for all problem categories and 80%+ for the top 3. Prediction accuracy up to 90% was achieved for the most frequent subcategories. Class prediction accuracies above 95% were consistently achieved for multiple classes. When the problem category is known, subcategory prediction accuracy increases above 90% Abstract: Occupant-generated work orders are recognized as a good potential data to support Facility Management (FM) activities, however they are unstructured and rarely contain the specific information engineers require to resolve the reported issues. Instead, this often requires multiple trips are often needed to identify the required trade, identify the problem and required parts/tools, and resolve. A key challenge is data quality: free-form (unstructured) text is collected that frequently lacks necessary detail for problem diagnosis. Machine Learning provides new opportunities within the FM domain to improve the quality of information collected through online work order reporting systems by automatically classifying WOs and prompting building occupants with appropriate FM team-developed questions in real time to gather the required specific information in structured form. This paper presents the development, comparison, and application of two sets of supervised machine learning models to perform this classification for WOs generated from occupant complaints. A set of ∼150, 000 historical WOs was used for model development and textual classification using with various term and itemset frequency approaches was tested. Classifier prediction accuracies ranged from 46.6% to 81.3% for classification by detailed subcategory; this increased to between 68% (simple term frequency) to 90% (random forest) when the dataset only included the ten most common (accounting for 70% of all WOs) subcategories. Hierarchical classification decreased performance. An FM-BIM integration approach is finally presented using the resultant classifiers to provide facilities management teams with spatio-temporal visualization of the work order categories across a series of buildings to help prioritize and streamline operations and maintenance task assignments.