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Emergency management can benefit from advanced information and communication technology (ICT), since they can support officers in charge with rescue operation to deal with urgent decision within a really short deadline. Further enhancement can derive from the application of holonic systems, inherited from the manufacturing industry, to tackle unforeseen scenario. The aim of this research is to develop a BIM-integrated technology for the real-time pathfinding during emergencies. A virtual reality platform, exploiting building topological data from the digital model, can support the standard emergency approach if an unexpected event voids the network of predetermined paths. The developed system has been tested in a large mixed-used building assumed as case study.

In the wide context of facility management, several processes, such as operations, maintenance, retrofitting, and renovations, ensure that buildings comply with the principles of efficiency, cost-effectiveness, and indoor comfort. Apart from ordinary operation, facility management is responsible for the renovation of and long-term performance improvement of building facilities. In such a scenario, the cyber-physical system (CPS) paradigm with holonic architecture, which is the focus of this study, can successfully guide the operation management and longterm refurbishment processes of buildings. Analogous to the manufacturing field, the developed CPS maximizes holons' self-configuration and self-organization and overall throughput effectiveness metrics to detect the best corrective actions toward system improvements. Consequently, suggestions and lessons learned from the evaluation of building efficiency are redirected to the building information model. Hence, the digital model acts as a repository of currently available equipment for operations management and the history of diagnoses that support decision-making during the maintenance, retrofitting, and renovation processes. Evidently, the repeated detection of a specific issue, which is unaffected by operations management, should be considered an opportunity to act and enhance the performances of existing building components. Similar to a goods-producing industry, the building management system developed in this study applies the aforementioned methodology to provide services related to indoor comfort and building health. This approach indicates that a method for automatic real-time diagnosis is tested in a case study consisting of a multi-use and large public building. The current paper, which is an extended version of the one presented in the Creative Construction Conference 2018, deepens the decision support tool and the supervision policy. Moreover, the developed system is contextualized by providing an example of use case and highlighting the step forward in the field of smart buildings.

This paper provides an analysis of the relationships between dwelling, household and motivation, behaviour and perception characteristics and thermostatic radiator valve (TRV) settings in living rooms (n=187) and bedrooms (n=159) in UK social housing. The work capitalises on primary data from a sociotechnical household survey undertaken in Plymouth, UK during 2015, which was coupled with building audit data. The mean reported TRV setpoint temperatures in the living rooms and bedrooms were 23.4°C and 21.9°C, implying that occupants prefer cooler conditions in bedrooms. There were systematic variations according to dwelling, household, and motivation, behaviour and perception characteristics. In general, the setpoint temperatures in individual rooms were higher than the whole house thermostat setpoint temperature, implying that there may be a misunderstanding of the role of TRVs in the home heating system. The research could enable social housing providers, the government and commercial organisations to target energy efficiency measures (i.e. thermal upgrades) and social interventions (i.e. behaviour change) at those dwellings and households where their impact may be most beneficial. The results presented could also be used to improve the assumptions of zonal heating behaviour in energy models, which could result in more realistic predictions of the heating demands of social housing.

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