Reducing the Environmental Impact of New Construction Projects through General Purpose Building Design and Multi-agent Crowd Simulation

  • Kieron Ekron
  • Jaco Bijker
  • Elize Ehlers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)

Abstract

This paper presents a two stage process for using intelligent agent technology in designing space-efficient buildings in order to reduce their environmental impact. An environment editor for designing new construction projects is described, followed by a microscopic crowd simulation model that is used to test the operational efficiency of the designed building. Each member of the crowd is represented as an intelligent agent, which allows for more complex goal-directed behaviour, which in turn leads to more realistic crowd behaviour. Crowd simulations can be used to detect potential problem areas, as well as identify areas that may safely be made smaller.

Keywords

Construction Project Intelligent Agent Shopping Mall Building Design Path Planner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chenney, S.: Flow tiles. In: Proceedings of the 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, pp. 233–242 (2004)Google Scholar
  2. 2.
    Chew, L.P.: Constrained delaunay triangulations. In: Proceedings of the Third Annual Symposium on Computational Geometry, SCG 1987, pp. 215–222. ACM, New York (1987)CrossRefGoogle Scholar
  3. 3.
    Dijkstra, J., Jessurun, J., Timmermans, H.J.P.: A multi-agent cellular automata model of pedestrian movement. In: Pedestrian and Evacuation Dynamics, pp. 173–181 (2001)Google Scholar
  4. 4.
    Gangolells, M., Casals, M., Gasso, S., Forcada, N., Roca, X., Fuertes, A.: A methodology for predicting the severity of environmental impacts related to the construction process of residential buildings. Building and Environment 44(3), 558–571 (2009)CrossRefGoogle Scholar
  5. 5.
    Gangolells Solanellas, M., Casals Casanova, M., Gassó Domingo, S., Forcada Matheu, N., Roca Ramon, X., Fuertes Casals, A.: Identifying potential environmental impacts at the pre-construction stage. Development (2009)Google Scholar
  6. 6.
    Helbing, D., Farkas, I.J., Molnár, P., Vicsek, T.: Simulation of pedestrian crowds in normal and evacuation situations. In: Pedestrian and Evacuation Dynamics, pp. 21–58 (2002)Google Scholar
  7. 7.
    Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Physical Review E 51(5), 4282–4286 (1995)CrossRefGoogle Scholar
  8. 8.
    Hughes, R.L.: A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological 36(6), 507–535 (2002)CrossRefGoogle Scholar
  9. 9.
    International Alliance for Interoperability: An Introduction to the International Alliance for Interoperability and the Industry Foundation Classes. Tech. rep., International Alliance for Interoperability (1999)Google Scholar
  10. 10.
    Johnston, A., Hutchison, J., Smith, A.: Significant environmental impact evaluation: a proposed methodology. Eco-Management and Auditing 7(4), 186–195 (2000)CrossRefGoogle Scholar
  11. 11.
    Lingjiang, H.: Technology in Computer Aided Architectural Design. In: Second International Conference on Information and Computing Science, ICIC 2009, vol. 2, pp. 221–223 (May 2009)Google Scholar
  12. 12.
    Noth, M., Borning, A.: An extensible, modular architecture for simulating urban development, transportation, and environmental impacts. Computers, Environment and Urban Systems 27(2), 181–203 (2003)CrossRefGoogle Scholar
  13. 13.
    Pelechano, N., Allbeck, J.M., Badler, N.I.: Virtual Crowds: Methods, Simulation, and Control. Morgan & Claypool (2008)Google Scholar
  14. 14.
    Reynolds, C.: Flocks, herds and schools: A distributed behavioral model. Computer Graphics 21(4), 25–34 (1987)CrossRefGoogle Scholar
  15. 15.
    Snook, G.: Simplified 3d movement and pathfinding using navigation meshes. In: Game Programming Gems, vol. 1, pp. 288–304. Charles River Media (2000)Google Scholar
  16. 16.
    Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM Trans. Graph. 25(3), 1160–1168 (2006)CrossRefGoogle Scholar
  17. 17.
    U.S. Census Bureau: World POPClock Projection (2011), http://www.census.gov/ipc/www/popclockworld.html, (accessed June 13, 2011)
  18. 18.
    Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kieron Ekron
    • 1
  • Jaco Bijker
    • 1
  • Elize Ehlers
    • 1
  1. 1.University of JohannesburgSouth Africa

Personalised recommendations