An approach to collect building sensors data based on Building Information Models
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Some thoughts we shared at Business Meets Research BMR 2013 (Luxembourg) about Building Information Modelling and Big Data approaches.
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An approach to collect building sensors data based on Building Information Models
- 1. An approach to collect building sensors data based on Building Information Models. Pierre Brimont & Sylvain Kubicki CRP Henri Tudor
- 2. CRP Henri Tudor, three objectives Research: Contribute through scientific excellence to the production and transfer of knowledge and to the international recognition of the scientific community in Luxembourg. Innovation: Sustainably strengthen the innovation capacity of companies and public organisations. Policy support: Support through research and innovation, the definition, implementation and evaluation of national public policies.
- 3. CRP Henri Tudor Scientific & Technological Domains: Materials technologies Environmental technologies Health care technologies Information and communication technologies Business organisation and management • Industrial Production and Manufacturing • Construction and Building • Transport and Logistics • Service Industry • IT, Multimedia and Communication • Finance and Banking • Healthcare, Medical and Social • Governmental and Public Organisations Key Economic Sectors:
- 4. Construction @ CRP Henri Tudor Construction Program. Our competencies • Business “experts” (Architects, Civil Engineer / Dr., PhD students) • IT scientists • Appropriation, networking, IPR Our team is historically involved in CRTI-B innovation projects (http://www.crti-b.lu) Today Tudor is co-animator of the NeoBuild innovation pole (http://www.neobuild.lu)
- 5. Context 2020 challenge in the construction industry • Towards zero-energy buildings (EU regulations for new buildings) Passiv/Positiv energy buildings characteristics • Very high level of insulation and airtightness of interior spaces • Heating, Ventilation and Air Conditioning become high-tech systems
- 6. Context Most of new-built houses are passiv houses, with high control of: • Heat recovery ventilation, insulation, solar gains Issues are emerging from these technology- driven design choices (Hasselaar 2008) • Comfort (overheating), noise (from installations/systems), health risks (legionella contamination of domestic water buffers, moistures because of low ventilation volumes)
- 7. Context Building pathology data • Usually comes from the assessment of insurance agencies experience • Could be widely collected from sensors implemented within buildings, buildings elements and equipments An example: • Multi-layer wall panels in wood construction Source: Leverwood! Air-moisture sensor (Savory et al. 2012)!
- 8. Big Data relevance Challenges and Opportunities with Big Data! Computing Community Consortium ! www.cra.org/ccc ! Sensor mesures ! Context metadata! Linear and trustfull sources ! Security perspective ! No real time! Modeling : use of the BIM! ! !
- 9. BIM According to most of the practitioners and researchers, BIM is both • Product modeling, i.e. modeling of building-related information, • Process modeling, i.e. the way practitioners contribute to a single/ interoperable model of the (future) building Towards standardization (BuildingSMART, research community) • IFC: standardizing product model (expected software interoperability) • IDM: standardizing process model (understanding collaborative work process) • IFD: effort towards common definitions and translations Source:Autodesk!
- 10. BIM BIM through the life-cycle of a building/facility Source: www.bccomfort.com!
- 11. BIM as a step to big data modeling buildingSMART data model standard • IFC (ISO 16739:2013) • Usually implemented by AEC software vendors IFC Property Sets • Define all dynamically extensible properties. • Can be customely defined (e.g. for sensors- specific data modeling?) www.buildingsmart-tech.org!
- 12. Thank you for your attention pierre.brimont@tudor.lu sylvain.kubicki@tudor.lu
Editor's Notes
- Other reasons are Complex control mechanisms Lack of flexibility of ventilation services
- So. When Iounis and Jean-Paul have asked us to present our work in this workshop, we have thought that this use case could be an adapted scenario. « A lot of automatic records coming from IoT sensors that needed to be analysed to infer recommandations or decisions » It sounds like a typical big data problem. But, if we look closely to the big data challenges that are investigated nowadays. With our positive house isolation optimisation, are we really facing a big data project ? Here there is an example coming from a white paper written by a community gathering scientific and industrials : If we refer to them, we can see 5 typical phases : The first one is the acquisition phase where the flow of different data requires to make decisions, about what to discard and what to keep through the use of efficient filters. Here, we will get data across different buildings but coming from same types of sensors already calibrated and tested. It could have some drawbacks and a few measures can be fault, but we can consider that it will not affect dramatically the result of the analyses. For the same reason, the extraction and cleaning phase can also be considered as a minor issue for us. In our case the main challenge in this step is to add right metadata to help the analysis later. Some extra-information (about the moment of the day or the number of people that were in the room when the sensor is on) will increase the quality of the data itself Same remark about the aggregation. We are not dealing with data coming from crowded sources like social networks and we don’t work with different data types like video, text or audio sources that need first to be integrated in a single format. No. Heterogeneity is limited. Other aspects related aspects like security or quick computation are not really relevant. Data about moisture or temperature is not critical in terms of privacy and the analysis of the large scale results to produce recommandations doesn’t need real-time. The main challenge is elsewhere. It concerns the modeling. The format of the information hence collected. What is the best way to structure it for analysis and how to integrate it in existing tools to facilitate the interpretation of the result ? The construction sector already use a wide range of dedicated IT tools to manage the business information and processes and one of the remaining challenge is to make them work together, to exchange data in a seamless way : in other words how to foster interoperability ? The BIM – Building Information Model – could be seen as a solution regarding this problem and Sylvain will explain how we plan to extend it to solve our analysis and interpretation issue. Note : D ’ après mes petites lectures partielles, je pense que le fait d ’ utiliser le BIM (dont ce n ’ est pas l ’ objectif initial) pour structurer et analyser est innovant : a priori peu d ’ exemple où un model/format pivot existant déjà au niveau business est utilisé pour du big data. (à vérifier cependant) The main challenge will be to