Automated Text Classification of Maintenance Data of Higher Education Buildings Using Text Mining and Machine Learning Techniques

Abstract

Facility 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.