Abstract
In recent years, facility management (FM) has adopted many computer technology solutions for building maintenance, such as building information modelling (BIM) and computerized maintenance management systems (CMMS). However, maintenance requests management in buildings remains a manual and a time-consuming process that depends on human management. In this paper, a machine-learning algorithm based on natural language processing (NLP) is proposed to classify maintenance requests. This algorithm aims to assist the FM teams in managing day-to-day maintenance activities. A healthcare facility is addressed as a case study in this work. Ten-year maintenance records from the facility contributed to the design and development of the algorithm. Multiple NLP methods were used in this study, and the results reveal that the NLP model can classify work requests with an average accuracy of 78%. Furthermore, NLP methods have proven to be effective for managing unstructured text data.
Highlights
Facility management (FM) is an essential aspect of the life cycle of a building
This paper aims to explore the use of natural language processing (NLP) and machine learning techniques to improve text data management in the FM industry, through a use case of text data management in a healthcare facility
This study investigated the potential of NLP for Facility Management
Summary
Facility management (FM) is an essential aspect of the life cycle of a building. It is an integrated process to maintain and improve building performances. FM deals with all the operation and maintenance (O&M) activities during the life cycle of a building. Operation and maintenance is the longest phase of a building’s life cycle, and it represents around 60% of the entire life cycle cost [1]. The infrastructure of buildings tends to be increasingly complex and evolves towards a smart building approach which integrates new technologies such as Internet of Things and smart sensors. The FM industry faces new challenges to evolve and adapt to this new context
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