DOI QR코드

DOI QR Code

A System Dynamics Model for Evaluation of Maintenance Cost Policy in Deteriorated School Building

노후 학교건물의 유지관리비용 정책 평가를 위한 시스템 다이내믹스 모델

  • Received : 2019.11.06
  • Accepted : 2019.12.10
  • Published : 2019.12.30

Abstract

The maintenance of school building is pivotal issue. However, it is difficult to obtain basic analysis data for LCC(Lifecycle Cost) analysis and maintenance planning of school building. Therefore, this study proposed System Dynamics(SD) techniques to make maintenance decisions for school building. The interaction between the major parameters related to the aging of a building, maintenance activities, and cost were expressed in Causal Loop Diagram. Based on this, the formula for the relationship between causal maps was defined and converted to Stock and Flow Diagram. Through the completed SD model the 50-year plan of 214 educational building were tested by considered in account budget, maintainability, and budget allocation opinions. As a result, the integrated SD model demonstrated that it can support strategic decision making by identifying the status class and LCC behavior of school buildings by scenario. According to the scenario analysis, the rehabilitation action of preventive maintenance that primarily repairs the buildings in condition grade C showed the best performance improvement effect relative to the cost. Therefore, if the proposed SD model is expanded to consider the effects of other educational policies, the crucial performance improvement budget can be estimated in the long-term perspective.

Keywords

Acknowledgement

Supported by : 한국연구재단

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2017R1C1B2009750)

References

  1. Bluyssen, P. M. (2017). Health, comfort and performance of children in classrooms-new directions for research, Indoor and Built Environment, 26(8), 1040-1050. https://doi.org/10.1177/1420326X16661866
  2. Duarte, J., Gargiulo, C., & Moreno, M. (2011). Infraestructura Escolar y Aprendizajes en la Educacion Basica Latinoamericana: Un analisis a partir del SERCE.
  3. Echaveguren, T., Chamorro, A., & De Solminihac, H. (2017). Concepts for modeling road asset management systems using agent-based simulation. Revista Ingenieria de Construccion, 32(1), 47-56. https://doi.org/10.4067/S0718-50732017000100005
  4. Elbehairy, H., Elbeltagi, E., Hegazy, T., & Soudki, K. (2006). Comparison of two evolutionary algorithms for optimization of bridge deck repairs. Computer-Aided Civil and Infrastructure Engineering, 21(8), 561-572. https://doi.org/10.1111/j.1467-8667.2006.00458.x
  5. Feng, Y. Y., Chen, S. Q., & Zhang, L. X. (2013). System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China. Ecological Modelling, 252, 44-52. https://doi.org/10.1016/j.ecolmodel.2012.09.008
  6. Forrester, J. W. (1961). Industrial Dynamics. Cambridge: MIT Press, Currently available from Pegasus Communications: Waltham, MA.
  7. Guo, Y., & Hawkes, A. (2019). Asset stranding in natural gas export facilities: An agent-based simulation. Energy Policy, 132, 132-155. https://doi.org/10.1016/j.enpol.2019.05.002
  8. Issa, M. H., Rankin, J. H., Attalla, M., & Christian, A. J. (2011). Absenteeism, performance and occupant satisfaction with the indoor environment of green Toronto schools. Indoor and Built Environment, 20(5), 511-523. https://doi.org/10.1177/1420326X11409114
  9. Kielb, C., Lin, S., Muscatiello, N., Hord, W., Rogers-Harrington, J., & Healy, J. (2015). "Building-related health symptoms and classroom indoor air quality: a survey of school teachers in New York State." Indoor Air, 25(4), 371-380. https://doi.org/10.1111/ina.12154
  10. Lee, H., Shin, H., Rasheed, U., & Kong, M. (2017). Establishment of an inventory for the Life Cycle Cost (LCC) analysis of a water supply system. Water, 9(8), 592. https://doi.org/10.3390/w9080592
  11. Liu, X., Ma, S., Tian, J., Jia, N., & Li, G. (2015). A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO2 emissions: A case study of Beijing. Energy Policy, 85, 253-270. https://doi.org/10.1016/j.enpol.2015.06.007
  12. Lyneis, J. M., Cooper, K. G., & Els, S. A. (2001). Strategic management of complex projects: a case study using system dynamics. System Dynamics Review: The Journal of the System Dynamics Society, 17(3), 237-260. https://doi.org/10.1002/sdr.213
  13. Mallory, A., Crapper, M., & Holm, R. H. (2019). Agent-Based Modelling for Simulation-Based Design of Sustainable Faecal Sludge Management Systems. International journal of environmental research and public health, 16(7), 1125. https://doi.org/10.3390/ijerph16071125
  14. Mohammadifardi, H., Knight, M. A., & Unger, A. A. (2019). Sustainability Assessment of Asset Management Decisions for Wastewater Infrastructure Systems-Development of a System Dynamic Model. Systems, 7(2), 26. https://doi.org/10.3390/systems7020026
  15. Mostafavi, A., Abraham, D., DeLaurentis, D., Sinfield, J., Kandil, A., and Queiroz, C. (2015). "Agent-based simulation model for assessment of financing scenarios in highway transportation infrastructure systems." J. COMPUT. CIVIL. ENG., 30(2), 04015012. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000482
  16. Osman, H. (2012). Agent-based simulation of urban infrastructure asset management activities. Automation in Construction, 28, 45-57. https://doi.org/10.1016/j.autcon.2012.06.004
  17. Rashedi, R., & Hegazy, T. (2015). Holistic analysis of infrastructure deterioration and rehabilitation using system dynamics. Journal of Infrastructure Systems, 22(1), 04015016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000273
  18. Rasoulkhani, K., Logasa, B., Reyes, M. P., & Mostafavi, A. (2017, December). Agent-based modeling framework for simulation of complex adaptive mechanisms underlying household water conservation technology adoption. In Proceedings of the 2017 winter simulation conference (p. 82). IEEE Press.
  19. Rasoulkhani, K., Logasa, B., Presa Reyes, M., & Mostafavi, A. (2018). Understanding fundamental phenomena affecting the water conservation technology adoption of residential consumers using agent-based modeling. Water, 10(8), 993. https://doi.org/10.3390/w10080993
  20. Rehan, R., Knight, M. A., Haas, C. T., & Unger, A. J. (2011). Application of system dynamics for developing financially self-sustaining management policies for water and wastewater systems. Water research, 45(16), 4737-4750. https://doi.org/10.1016/j.watres.2011.06.001
  21. Ross, S. M. (2014). Introduction to probability models, 11th Ed., San Diego.
  22. Shen, L., Soliman, M., Ahmed, S., & Waite, C. (2019, April). Life-Cycle Cost Analysis of Reinforced Concrete Bridge Decks with Conventional and Corrosion Resistant Reinforcement. In MATEC Web of Conferences (Vol. 271, p. 01009). EDP Sciences.
  23. Sing, M. C., Love, P. E., & Liu, H. J. (2019). Rehabilitation of existing building stock: A system dynamics model to support policy development. Cities, 87, 142-152. https://doi.org/10.1016/j.cities.2018.09.018
  24. Soetjipto, J. W. (2016). System Dynamics Approach for Bridge Deterioration Monitoring System. International Journal of Engineering and Technology Innovation, 6(4), 264-273.
  25. Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill.
  26. Wang, J., & Yuan, H. (2016). System dynamics approach for investigating the risk effects on schedule delay in infrastructure projects. Journal of Management in Engineering, 33(1), 04016029. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000472
  27. Zechman, E. M. (2011). Agent-based modeling to simulate contamination events and evaluate threat management strategies in water distribution systems. Risk Analysis: An International Journal, 31(5), 758-772. https://doi.org/10.1111/j.1539-6924.2010.01564.x
  28. Han, S. (2017). The Significance of Investing in School Facilities and Measures for Reform). KDI FOCUS, (87).
  29. Hong, Z., Fangmin, R., & Rongbei, Z. (2016). Simulation Research for Highway Maintenance Management System Based on System Dynamics. Journal of System Simulation, (3), 23.