Name
Title | ||
|---|---|---|
Machine learning and BIM visualization for maintenance issue classification and enhanced data collection. |
Abstract | ||
|---|---|---|
•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% |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.aei.2018.06.007 | Advanced Engineering Informatics |
Keywords | Field | DocType |
Machine learning,Building information modelling,Visualization,Facility management,Predictive models,Work orders | Data collection,Work order,Data quality,Facility management,Visualization,Specific-information,Artificial intelligence,Engineering,Random forest,Machine learning,Information quality | Journal |
Volume | ISSN | Citations |
38 | 1474-0346 | 1 |
PageRank | References | Authors |
0.36 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. J. McArthur | 1 | 1 | 0.36 |
| Nima Shahbazi | 2 | 1 | 1.03 |
| ricky fok | 3 | 1 | 2.38 |
| Christopher Raghubar | 4 | 1 | 0.36 |
| Brandon Bortoluzzi | 5 | 1 | 0.36 |
| Aijun An | 6 | 1584 | 109.73 |