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.

The objective of this article is to carry out a comparative study of the main hygrothermal properties of five thermal insulation materials for buildings. These properties are necessary for a correct prediction of heat and moisture transfers through the walls and the selection of the most appropriate materials according to the specific buildings. The studied materials were glass wool, rock wool, expanded polystyrene, wood fiberboard and polyester fiberfill. The article is divided into three parts. The first part presents the phenomena of hygrothermal transfers in walls in order to understand the need for determining specific properties of the insulating materials. The second part describes in details the five studied insulating materials and the methods used for the characterization and identification of their main properties. Finally, the last part presents the experimental results and makes comparisons between materials. The differences between the insulating materials are brought out, such as the strong dependence of the thermal conductivity of polystyrene on temperature, or the good permeability of fibrous insulating materials to water vapor. A detailed analysis of the obtained results is presented

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.