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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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Construction customers want more complex facilities delivered faster and at a lower cost. Transaction costs account for a significant proportion of each new or refurbished facility (a 2017 report from the Infrastructure Client Group in the UK suggests as high as 50%), yet they contribute no value to the customer. Blockchain is being suggested as a way to reduce transaction costs by eliminating the need for intermediaries to build trust as a prerequisite for successfully executed agreements. This study first describes the thinking that underpins blockchain technology, outlining how it works, and the potential limitations of the technology. Second, using a case study, reviews the potential cost savings from the use of blockchain for a real estate company. The results reveal a potential cost savings from blockchain deployment at 8.3% of the total cost of residential construction, with a standard deviation of 1.26%. Third, we explore the implications, risks and applications of blockchain technology for improving flow in the end-to-end design and construction process and we identify opportunities for future research on blockchain applications in construction.