- freely available
Appl. Sci. 2018, 8(7), 1086; https://doi.org/10.3390/app8071086
- Automated integration: automatically collects data from physical sensors installed in a space, and stores the data in the BIM model of the space.
- Automated visualization: automatically calculates an index value based on the collected sensor data, and computationally visualizes the value with a color palette scheme.
- Multiple contexts: allows a user to switch between different contexts that require different subsets of the sensors (e.g., comfort, energy saving, the WELL Building Standard perspectives) installed in the space.
2. Research Methodologies and Experimental Design
2.1. Parametric Design-Based Approach
- Actual buildings (campus): The campus includes buildings, and sensors and Arduino were deployed in indoor spaces to perform environmental data collection.
- Virtual world (BIM model): After establishing a virtual 3D space and sensor locations, we input real environmental data into the virtual environment through Firefly.
- Data reception and transmission: The Firefly suite is a set of computer code fragments employed to bridge the gaps between BIM-based parametric design software such as Dynamo or Grasshopper , a sensor microcontroller such as Arduino, and other input/output devices, such as webcams and mobile phones.
- Parameterization: Depending on decision-making goals in different situations, the operating rules of custom nodes were established using Dynamo, which includes a context view index (including comfort, energy saving, and the WELL Standard).
- Decision support: visualization layer input to the BIM model.
2.2. Experimental Design
2.2.1. Scenario Assumptions
2.2.2. Arduino Microcontroller
- Humidity measurement range: 20–90% RH.
- Humidity measurement accuracy: ± 5% RH.
- Temperature measurement range: 0–50 °C.
- Temperature measurement accuracy: ± 2 °C.
- Power supply range: 3–5 V
2.2.3. Dynamo Automation Platform
- Use the Firefly suite to create a node linking Dynamo and Arduino, forming a basis for interactive prototyping and importing environmental data into Dynamo (see Figure 7).
- The code fragments first create a layer component in BIM at the predefined elevation, collect sensor data and calculate the context view index (PMV) for each grid in the layer using interpolation, and then paint the corresponding color for each grid based on the predefined color palette scheme. The result of this step creates a thermography-like image and allows a user to visually see the distribution of the desired context view index value of the space in BIM (see Figure 10).
- Decisions concerning the indoor comfort pointer are based on predicted mean vote (PMV), and the results of analysis can be presented in the BIM model on this platform as shown in Figure 11. Note that PMV is a common metric for assessing the comfort level of an indoor environment, and has been adopted in ISO 7730 .
- M was set as 70 (w/m2) because students mostly performed stationary tasks (such as typing on laptop computers, reading and writing) in the classroom.
- W was set as 0 (W/m2) because this is usually assumed to be 0 (W/m2) when assessing indoor comfort.
- Icl was set to 0.11 (m2k/W) because the experiment was conducted during the winter, and the students usually wore long-sleeved clothing.
- va was set to 0.1 (m/s) for the wind speed in winter.
3. Case Demonstration
4.1. Comparison with Conventional Sensor Data Representation
4.2. Reusability of the Proposed System
Conflicts of Interest
- McCormick, B.H. Visualization in scientific computing. ACM SIGBIO Newslett. 1988, 10, 15–21. [Google Scholar] [CrossRef]
- Schroeder, W.J.; Lorensen, B.; Martin, K. The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics; Kitware: New York, NY, USA, 2004. [Google Scholar]
- Munzner, T. Process and pitfalls in writing information visualization research papers. In Information Visualization; Springer: Berlin/Heidelberg, Germany, 2008; pp. 134–153. [Google Scholar]
- Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef][Green Version]
- Eastman, C.M. Building Product Models: Computer Environments, Supporting Design and Construction; CRC Press: Boca Raton, FL, USA, 1999. [Google Scholar]
- Azhar, S.; Hein, M.; Sketo, B. Building information modeling: Benefits, risks and challenges. In Proceedings of the 44th Associated Schools of Construction National Conference, Auburn, AL, USA, 2–5 April 2008. [Google Scholar]
- Azhar, S. Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadersh. Manag. Eng. 2011, 11, 241–252. [Google Scholar] [CrossRef]
- Eastman, C.M.; Eastman, C.; Teicholz, P.; Sacks, R. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
- Azhar, S.; Khalfan, M.; Maqsood, T. Building information modeling (BIM): Now and beyond. Constr. Econ. Build. 2015, 12, 15–28. [Google Scholar]
- IFC2x4, Industry Foundation Classes Release 4 (IFC4). Available online: http://www.buildingsmart-tech.org/specifications/ifc-releases/ifc2x4-release/summary (accessed on 11 March 2013).
- IAI. _2007_. IFC/ifcXML specifications. Available online: http://www.iai-international.org (accessed on 30 October 2009).
- Venugopal, M.; Eastman, C.M.; Sacks, R.; Teizer, J. Semantics of model views for information exchanges using the industry foundation class schema. Adv. Eng. Informat. 2012, 26, 411–428. [Google Scholar] [CrossRef]
- Golabchi, A.; Akula, M.; Kamat, V. Automated building information modeling for fault detection and diagnostics in commercial HVAC systems. Facilities 2016, 34, 233–246. [Google Scholar] [CrossRef]
- Che, L.; Gao, Z.; Chen, D.; Nguyen, T.H. Using building information modeling for measuring the efficiency of building energy performance. In Proceedings of the International Conference on Computing in Civil and Building Engineering (ICCCBE), Nottingham, UK, 30 June–2 July 2010; pp. 165–170. [Google Scholar]
- Chwieduk, D.A. Recommendation on modeling of solar energy incident on a building envelope. Renew. Energy 2009, 34, 736–741. [Google Scholar] [CrossRef]
- Kensek, K.M. Integration of Environmental Sensors with BIM: Case studies using Arduino, Dynamo, and the Revit API. Informes Constr. 2014, 66, 536. [Google Scholar] [CrossRef]
- Emad, A.-Q.; Wei, Y.; Philip, G. Establishing parametric relationships for design objects through tangible interaction. In Proceedings of the 22nd International Conference of the Association for Computer-Aided Architectural Design Research in Asia(CAADRIA), Protocols, Flows and Glitches, Suzhou, China, 5–8 April 2017; Janssen, P., Loh, P., Raonic, A., Schnabel, M.A., Eds.; The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA): Hong Kong, China, 2017; pp. 147–157. [Google Scholar]
- Asl, M.R.; Zarrinmehr, S.; Bergin, M.; Yan, W. BPOpt: A framework for BIM-based performance optimization. Energy Build. 2015, 108, 401–412. [Google Scholar][Green Version]
- Delgado, J.M.D.; Butler, L.J.; Brilakis, I.; Elshafie, M.; Middleton, C. Structural performance monitoring using a dynamic data-driven BIM environment. J. Comput. Civil Eng. 2018, 32, 04018009. [Google Scholar] [CrossRef]
- Lather, J.I.; Amor, R.; Messner, J.I. A Case Study in Data Visualization for Linked Building Information Model and Building Management System Data. In Proceedings of the ASCE International Workshop on Computing in Civil Engineering 2017, Seattle, WA, USA, 25–27 June 2017. [Google Scholar]
- Mccaffrey, R.; Coakley, D.; Keane, M.; Melvin, H. Development of a Web-based BMS Data Visualization Platform Using Building Information Models. In Proceedings of the CIBSE 2015 Technical Symposium, London, UK, 16–17 April 2015. [Google Scholar]
- Dynamo BIM. Available online: http://dynamobim.org/ (accessed on 20 December 2012).
- Huang, S.-C. A BIM Workflow for Responsive Energy-Efficient Façade. Master’s Thesis, National Cheng Kung University, Tainan, Taiwan, 2016. [Google Scholar]
- ARDUINO MEGA 2560 REV3. Available online: https://store.arduino.cc/usa/arduino-mega-2560-rev3 (accessed on 11 October 2017).
- ISO-7730. Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, 3rd ed.; ISO: Geneva, Switzerland, 2005. [Google Scholar]
- Fanger, P.O. Thermal Comfort: Analysis and Applications in Environmental Engineering; McGraw-Hill Book Company: New York, NY, USA, 1972; p. 244. ISBN 8757103410. [Google Scholar]
- Lin, Y.-P. The Design and Implement of the Automatic Energy Saving System Based on Environmental Comfort. Master’s Thesis, National Chin-Yi University of Technology, Taichung City, Taiwan, 2016. [Google Scholar]
- Cheng, C.-M.; Ma, H.-X.; Hsu, Y.-L. The pilot test of the environmental monitoring system of the Palace Museum in the framed studio. In Gerontechnology Research in Yuan Ze University; Yuan Ze University: Taoyuan, Taiwan, 2005. [Google Scholar]
- International Well Building Institute (IWBI). WELL Building Standard. Available online: http://standard.wellcertified.com/air/air-quality-standards (accessed on 6 May 2016).
|Author||K.M. Kensek (2014) ||Emad Al-Qattan et al. (2017) ||M. Rahmani Asl et al. (2015) .||Chang, K.M. et al. (2018)|
|Building Life-Cycle Stage||Design stage||Design stage||Design stage||Operation and maintenance stage|
|Sensors||Light, humidity, CO2||Ribbon sensor||Daylight||Humidity, temperature|
|Research Tool||Arduino, Revit, Dynamo, Rhino, Grasshopper||Arduino, Revit, Dynamo, Rhino, Grasshopper||Revit, Dynamo||Arduino, Revit, Dynamo,|
|Automated Integration||Yes||Yes||Exported as a CSV file||Yes|
|Multiple Contexts||A link from the Revit model to a physical model||Generative modeling||Energy performance factor, daylighting performance factor||PMV (Predicted Mean Vote) comfort index|
|Comfort feeling||Cold||Cool||Slightly cool||Neutral||Slightly warm||Warm||Hot|
|System Name||National Palace Museum Collection Environmental Monitoring System ||Automated IoT Visualization BIM Platform|
|Context view index||Temperature,|
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).