The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

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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.

This paper presents the design and development of a test protocol for accelerated aging by hydrolysis in basic and acidic media, as well as application of this protocol for testing two fabrics intended for use as facing materials for buildings. The durability indicators were selected in order to quantify and analyse the impact of accelerated aging on fabrics. The experimental results reveal that the two fabrics' behaviour depends on the medium's nature. The aging in an acidic medium was hardly noticeable. Conversely, aging in a basic medium resulted in a quick deterioration of the fabrics: after 35 days of aging, the tensile strength of both fabrics was close to zero. Furthermore, the two fabrics behave differently in the basic medium, which is mainly due to their separate structures: the first fabric resistance to nail tear increased during the first day of aging unlike the second fabric.