GAA-Based Decision Approach for Hospital Building Renovation Management

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More and more attention has been paid to hospital facilities since modern pandemics have emerged such as SARS and avian influenza. Energy consumption by buildings accounts for 20-40% of energy use in developed countries, so many global organizations make efforts to develop sustainable technologies or materials to create a sustainable environment, and to reduce energy consumption when renovating building. Therefore, maintaining high standards of hygiene and reducing energy consumption has become the major task for hospital buildings. This study develops an integrated decision support system to assess existing hospital building conditions and to recommend an optimal scheme of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines the A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A simulated hospital renovation project is established to demonstrate the system. The result reveals the system can solve complicated and large-scale combinational, discrete and determinate problems such as the hospital renovation project, and also improve traditional building condition assessment to be more effective and efficient.

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Advanced Materials Research (Volumes 403-408)

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5265-5272

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November 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] G. W. K. Wong, T. F. Leung, Bird flu: lessons from SARS, Paediatric Respiratory Reviews 8(2) (2007), pp.171-176.

DOI: 10.1016/j.prrv.2007.04.003

Google Scholar

[2] World Health Organization. Summary of probable SARS cases with onset of illness from: 1 November 2002 to 31 July (2003).

Google Scholar

[3] B. F. Yu, Z. B. Hu, M. Liu, H. L. Yang, Q. X. Kung, Y. H. Liu, Review of research on air-conditioning systems and indoor air quality control for human health, International Journal of Refrigeration 32(1) (2009), pp.3-20.

DOI: 10.1016/j.ijrefrig.2008.05.004

Google Scholar

[4] B. Gamage, D. Moore, R. Copes, A. Yassi, E. Bryce, members of The BC Interdisciplinary Respiratory Protection Study Group, Protecting health care workers from SARS and other respiratory pathogens: A review of the infection control literature, American Journal of Infection Control 33(2) (2005).

DOI: 10.1016/j.ajic.2004.12.002

Google Scholar

[5] H. Qian, Y. Li, P. V. Nielsen, X. Huang, Spatial distribution of infection risk of SARS transmission in a hospital ward, Building and Environment 44(8) (2009), pp.1651-1658.

DOI: 10.1016/j.buildenv.2008.11.002

Google Scholar

[6] Y. Li, X. Huang, I. T. S. Yu, T. W. Wong, H. Qian, Role of air distribution in SARS transmission during the largest nosocomial outbreak in Hong Kong, Indoor air 15(2) (2004), pp.83-95.

DOI: 10.1111/j.1600-0668.2004.00317.x

Google Scholar

[7] A. Joseph, M. Rashid, The architecture of safety : hospital design, Current Opinion Critical Care 13(6) (2007), pp.714-719.

DOI: 10.1097/mcc.0b013e3282f1be6e

Google Scholar

[8] L. Pe´rez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and Buildings 40(3) (2008), pp.394-398.

DOI: 10.1016/j.enbuild.2007.03.007

Google Scholar

[9] Y. K. Juan, P. Gao, J. Wang, A hybrid decision support system for sustainable office building renovation and energy performance improvement, Energy and Buildings 42(3) (2010), pp.290-297.

DOI: 10.1016/j.enbuild.2009.09.006

Google Scholar

[10] E. Brandt, M. H. Rasmussen, Assessment of building conditions, Energy and Buildings 34(2) (2002), pp.121-125.

Google Scholar

[11] M. Jaggs, J. Palmer, Energy performance indoor environmental quality retrofit—a European diagnosis and decision making method for building refurbishment, Energy and Buildings 31(2) (2000), pp.97-101.

DOI: 10.1016/s0378-7788(99)00023-7

Google Scholar

[12] E. Dascalaki, C. A. Balaras, Xenios—a methodology for assessing refurbishment scenarios and the potential of application of RES and RUE in hotels, Energy and Buildings 36(11) (2004), pp.1091-1105.

DOI: 10.1016/j.enbuild.2004.03.007

Google Scholar

[13] J. Yang, H. Peng, Decision support to the application of intelligent building technologies, Renewable Energy 22(1) (2001), p.67–77.

DOI: 10.1016/s0960-1481(00)00085-9

Google Scholar

[14] T. Ramesh, Ravi Prakash, K.K. Shukla, Life cycle energy analysis of buildings: An overview, Energy and Buildings 42(10) (2010), pp.1592-1600.

DOI: 10.1016/j.enbuild.2010.05.007

Google Scholar

[15] Y. k. Juan, J. H. Kim, K. Roper, D. Castro-Lacouture, GA-based decision support system for housing condition assessment and refurbishment strategies, Automation in Construction 18(4) (2009), pp.394-401.

DOI: 10.1016/j.autcon.2008.10.006

Google Scholar

[16] W.L. Lee, J. Burnett, Benchmarking energy use assessment of HK-BEAM, BREEAM and LEED, Building and Environment 43(11) (2008), pp.1882-1891.

DOI: 10.1016/j.buildenv.2007.11.007

Google Scholar

[17] U.S. Green Building Council . (2010). http: /www. usgbc. org/Default. aspx.

Google Scholar

[18] GGHC (Green Guide for Health Care). (2010). http: /www. gghc. org/about. objectives. php.

Google Scholar

[19] BREEAM Healthcare Assessor Manual. (2008). http: /www. breeam. org/page. jsp?id=105.

Google Scholar

[20] K. Gowri, Green building rating systems: an overview, ASHRAE Journal 46(11) (2004), p.56–59.

Google Scholar

[21] A.A. Javadi, R. Farmani, T.P. Tan, A hybrid intelligent genetic algorithm, Advanced Engineering Informatics 19(4) (2005), pp.255-262.

DOI: 10.1016/j.aei.2005.07.003

Google Scholar

[22] C.K. Lee, S.K. Kim, GA-based algorithm for selecting optimal repair and rehabilitation methods for reinforced concrete (RC) bridge decks, Automation in Construction 16(2) (2007), p.153–164.

DOI: 10.1016/j.autcon.2006.03.001

Google Scholar

[23] C. Preechakul, S. Kheawhom, Modified genetic algorithm with sampling techniques for chemical engineering optimization, Journal of Industrial and Engineering Chemistry 15(1) (2009), p.110–118.

DOI: 10.1016/j.jiec.2008.09.003

Google Scholar

[24] Y. Zhang, X.P. Li, Q. Wang, Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization, European Journal of Operational Research 196(3) (2009), pp.869-876.

DOI: 10.1016/j.ejor.2008.04.033

Google Scholar

[25] S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, New Jersey, (2003).

Google Scholar