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contributor authorSungil Hong
contributor authorJunghyun Kim
contributor authorEunhwa Yang
date accessioned2022-05-07T21:02:36Z
date available2022-05-07T21:02:36Z
date issued2022-3-1
identifier other(ASCE)AE.1943-5568.0000522.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283235
description abstractFacility maintenance data sets have not been actively employed because of missing data and data inconsistency. This research attempts to resolve the issues (i.e., missing data and data inconsistency) by proposing a systematic approach that leverages machine learning–based text classification algorithms. This study specifically utilizes four different classification algorithms [i.e., support vector machine (SVM), multilayer perceptron, random forest, and naïve Bayes] and evaluates the performance of the algorithms to identify the most appropriate prediction model. A case study is constructed with 3,632 HVAC-related maintenance requests of higher education buildings retrieved from Computerized Maintenance Management System (CMMS) software as a proof of concept. The results show that the best performance of the prediction model (e.g., the capability to predict missing data correctly) with the SVM achieves an 85% accuracy rate compared with the other algorithms. The findings of this research can be used to improve the performance or efficiency of the data-driven decision-making processes in the facility management (FM) field by providing the ability to predict missing data inputs more consistently.
publisherASCE
titleAutomated Text Classification of Maintenance Data of Higher Education Buildings Using Text Mining and Machine Learning Techniques
typeJournal Paper
journal volume28
journal issue1
journal titleJournal of Architectural Engineering
identifier doi10.1061/(ASCE)AE.1943-5568.0000522
journal fristpage04021045
journal lastpage04021045-10
page10
treeJournal of Architectural Engineering:;2022:;Volume ( 028 ):;issue: 001
contenttypeFulltext


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