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Intelligent Computing for Building Performance Analysis

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

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Abstract

A challenge towards the intelligent use of computing in civil and architectural engineering is the definition of the questions that the ICT technology has to address. To some extent this is implicitly covered by activities such as the definition of search and option spaces, development of model views, the specification of objective functions, definitions of ontologies, or the development of multi-criterion decision methods. However, the underlying needs and drivers of design, construction and facility management processes of buildings are hard to capture, while they are essential to effective use of computing techniques. This paper reviews the starting point for intelligent computing within the domain of building performance analysis. It explores how approaches from the field of requirement engineering may help to support proper definition of computational needs, while embedding computational analysis efforts within the wider context of assessment approaches that are available in the building domain.

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References

  1. Eastman, C., Teicholz, P., Sacks, R., Liston, K.: BIM Handbook – A Guide to Building Information Modelling, 2nd edn. Wiley, Hoboken (2011)

    Google Scholar 

  2. Raphael, B., Smith, I.: Fundamentals of Computer-Aided Engineering. Wiley, Chichester (2003)

    Google Scholar 

  3. de Wilde, P.: The concept of building performance in building performance simulation – a critical review. In: Barnaby, C., Wetter, M. (eds.) 15th International IBPSA Conference on Building Simulation 2017, San Francisco (2017)

    Google Scholar 

  4. de Wilde, P.: Building Performance Analysis. Wiley-Blackwell, Hoboken (2018)

    Google Scholar 

  5. Morgan, M.: Vitruvius: The Ten Books on Architecture. Dover Publications, New York (1960)

    Google Scholar 

  6. Augenbroe, G.: Trends in building simulation. In: Malkawi, A., Augenbroe, G. (eds.) Advanced Building Simulation. Spon Press, New York (2003)

    Google Scholar 

  7. Clarke, J.: Energy Simulation in Building Design, 2nd edn. Butterworth-Heinemann, Oxford (2001)

    Google Scholar 

  8. Oh, S., Haberl, J.: Origins of analysis methods used to design high-performance commercial buildings: whole-building energy simulation. Sci. Technol. Built Environ. 22(1), 118–137 (2016)

    Article  Google Scholar 

  9. Eastman, C.: Building Product Models: Computer Environments Supporting Design and Construction. CRC Press, Boca Raton (1999)

    Google Scholar 

  10. Augenbroe, G.: COMBINE 2 Final Report. Commission of the European Communities, Brussels (1995)

    Google Scholar 

  11. Lee, Y., Eastman, C., Solihin, W.: An ontology-based approach for developing data exchange requirements and model views of building information modeling. Adv. Eng. Inform. 30, 354–367 (2016)

    Article  Google Scholar 

  12. Singh, V., Gu, N., Wang, X.: A theoretical framework of a BIM-based multi-disciplinary collaboration platform. Autom. Constr. 20, 134–144 (2011)

    Article  Google Scholar 

  13. Petersen, S., Svendsen, S.: Method and simulation programs informed decisions in the early stages of building design. Energy Build. 42, 1113–1119 (2010)

    Article  Google Scholar 

  14. Negendahl, K.: Building performance simulation in the early design stage: an introduction to integrated dynamic models. Autom. Constr. 54, 39–53 (2015)

    Article  Google Scholar 

  15. Oduyemi, O., Okoroh, M.: Building performance modelling for sustainable building design. Int. J. Sustain. Built Environ. 5, 461–469 (2016)

    Article  Google Scholar 

  16. Hopfe, C., Hensen, J.: Uncertainty analysis in building performance simulation for design support. Energy Build. 43, 2798–2805 (2011)

    Article  Google Scholar 

  17. Montgomery, D.: Design and Analysis of Experiments, 8th edn. Wiley, Hoboken (2013)

    Google Scholar 

  18. de Souza, C.B.: Contrasting paradigms of design thinking: the building thermal simulation tool user vs the building designer. Autom. Constr. 22, 112–122 (2012)

    Article  Google Scholar 

  19. Becker, R.: Fundamentals of performance-based building design. Build. Simul. 1(4), 356–371 (2008)

    Article  Google Scholar 

  20. Marsh, A.: Peformance Analysis and Conceptual Design. Ph.D. thesis. University of Western Australia, Perth (1997)

    Google Scholar 

  21. Macdonald, I: Quantifying the Effects of Uncertainty in Building Simulation. Ph.D. thesis. University of Strathclyde, Glasgow (2002)

    Google Scholar 

  22. Marsh, R.: LCA profiles for building components: strategies for the early design process. Build. Res. Inf. 44(4), 358–375 (2016)

    Article  Google Scholar 

  23. Machairas, V., Tsangrassoulis, A., Axarli, K.: Algorithms for optimization of building design: a review. Renew. Sustain. Energy Rev. 31, 101–112 (2014)

    Article  Google Scholar 

  24. Nguyen, A., Reiter, S., Rigo, P.: A review on simulation-based optimization methods applied to building performance analysis. Appl. Energy 113, 1043–1058 (2014)

    Article  Google Scholar 

  25. Attia, S., Hensen, J., Beltrán, L., De Herde, A.: Selection criteria for building performance simulation tools: contrasting architects’ and engineers’ needs. J. Build. Perform. Simul. 5(3), 155–169 (2012)

    Article  Google Scholar 

  26. International Energy Agency: Annex 21 – Calculation of Energy and Environmental Performance of Buildings. Subtask B: Appropriate Use of Programs. Volume 1: Executive Summary. Building Research Establishment, Watford (1994)

    Google Scholar 

  27. Augenbroe, G., de Wilde, P., Moon, Y., Malkawi, A., Choudhary, R., Mahdavi, A., Brame, R.: Design Analysis Interface (DAI) Final Report. Georgia Institute of Technology, Atlanta (2003)

    Google Scholar 

  28. Pressman, R.: Software Engineering: A Practitioner’s Approach, 6th edn. McGraw-Hill, New York (2005)

    MATH  Google Scholar 

  29. INCOSE: Systems Engineering Handbook: a Guide for System Life Cycle Processes and Activities. 3th edn. Wiley, Hoboken (2015)

    Google Scholar 

  30. Gilb, T.: Competitive Engineering: a Handbook for Systems Engineering, Requirements Engineering, and Software Engineering using Planguage. Butterworth-Heinemann, Oxford (2005)

    Google Scholar 

  31. Pohl, K., Rupp, C.: Requirements Engineering Fundamentals, 2nd edn. RockyNook, Santa Barbara (2015)

    Google Scholar 

  32. Robertson, S., Robertson, J.: Mastering the Requirements Process, 3rd edn. Pearson Education, Upper Saddle River (2012)

    Google Scholar 

  33. Lucas, J., Bulbul, T., Thabet, W.: An object-oriented model to support healthcare facility information management. Autom. Constr. 31, 281–291 (2013)

    Article  Google Scholar 

  34. Wang, Y., Yu, S., Xu, T.: A user requirement driven framework for collaborative design knowledge management. Adv. Eng. Inform. 33, 16–28 (2017)

    Article  Google Scholar 

  35. Girodon, J., Monticolo, D., Bonjour, E., Perrier, M.: An organizational approach to designing an intelligent knowledge-based system: application to the decision-making process in design projects. Adv. Eng. Inform. 29, 696–713 (2015)

    Article  Google Scholar 

  36. Chong, Y., Chen, C.: Management and forecast of dynamic customer needs: an artificial immune and neural systems approach. Adv. Eng. Inform. 24, 96–106 (2010)

    Article  Google Scholar 

  37. Golzarpoor, B., Haas, C., Rayside, D.: Improving process conformance with industry foundation processes (IFP). Adv. Eng. Inform. 30, 143–156 (2016)

    Article  Google Scholar 

  38. Luo, X., Shen, G., Fan, S.: A case-based reasoning system for using functional performance specification in the briefing process of building projects. Autom. Constr. 19, 725–733 (2010)

    Article  Google Scholar 

  39. Ren, Z., Anumba, C., Augenbroe, G., Hassan, T.: A functional architecture of an e-Engineering hub. Autom. Constr. 17, 930–939 (2008)

    Article  Google Scholar 

  40. Schulz, C., Amor, R., Lobb, B., Guesgen, H.: Qualitative design support for engineering and architecture. Adv. Eng. Inform. 23, 68–80 (2009)

    Article  Google Scholar 

  41. Gadeyne, K., Pinte, G., Berx, K.: Describing the design space of mechanical computational design synthesis problems. Adv. Eng. Inform. 28, 198–207 (2014)

    Article  Google Scholar 

  42. Tucker, S., Bleil de Souza, C.: Thermal simulation outputs: exploring the concept of patterns in design decision making. J. Build. Perform. Simul. 9(1), 30–49 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Plymouth University, Plymouth, PL4 8AA, UK

    Pieter de Wilde

Authors
  1. Pieter de Wilde
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Correspondence to Pieter de Wilde .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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de Wilde, P. (2018). Intelligent Computing for Building Performance Analysis. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_23

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91634-7

  • Online ISBN: 978-3-319-91635-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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