Automated priority assignment of building maintenance tasks using natural language processing and machine learning

Building maintenance tasks to solve unpredictable faults typically start with written communications from end-users (e.g., emails). Technicians manually translate end-users’ requests in work-orders (WOs) assigning them a priority level and the needed staff typology. When the number of contemporary requests is too high, these actions can lead to the interruption of critical services and then possible safety issues. Machine Learning (ML) methods can be trained to automatize this process due to large databases of annotated requests. Nevertheless, natural language preprocessing is needed to apply ML methods because of the unstructured form of the requests. This work aims to verify how preprocessing impacts the ability of ML methods to properly assign priority to the requests. The research methodology combines four different text preprocessing approaches (e.g., symbols and numbers remotion, stop-words remotion, stemming, meaningful words selection) and five consolidated ML methods to classify WOs according to two different priority scales (binary, 4-classes). Accuracy, recall, precision, and F1 are calculated for each combination. Tests are performed on a database of about 12,000 end-users’ maintenance requests, generated for 34 months in 23 university buildings. Results show that strong preprocessing methods, usually performed to increase the effectiveness of ML, do not significantly improve the accuracy of the predictions. Moreover, they show that four of the five tested ML methods obtained a higher accuracy for binary classification and for high and mean priority classes of 4-classes classification. This means that ML methods are especially effective in a preliminary check of the most urgent requests. These results then encourage the use of ML methods in automatic priority assignment of building maintenance tasks, even if based on natural language unstructured requests. The ML can significantly speed up the interventions assignment process for the technical staff, thus improving the maintenance process especially in large and complex buildings organizations.