Determination of optimum life span of container houses by using Neuro-Fuzzy methods

Abstract

Life span is one of the most effective parameter in Life Cycle Assessment of building analysis. The purpose of the study is to display the life span and consumed energy relation with different usage areas of a typical post-disaster container house via Neuro-Fuzzy approach. The proposed Fuzzy model in the study motivated on the construction phase of the containers to estimate total energy use for different life span years. Life span years are chosen between 5 and 40 years. By using Life Cycle Energy Assessment (LCEA) analysis, it is found that energy values are decreasing with the increase in life span of the container house models. The most drastic reduction in energy values has been observed in the first years with respect to the usage areas. Besides the analytical LCEA analysis, an Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling approach is used to predict the life span of the container houses. The results of the ANFIS modeling approach have shown promising results. The optimum life span for the CH models has been calculated to be around 16 years. There is a remarkable increase in EE values of the CH having a gross area bigger than 26 m². It is shown that the Neuro-Fuzzy application is a very viable tool for accurate life span predictions in Life Cycle Assessment studies.

Keywords

Life Cycle Assessment / Life span / Adaptive Neuro-Fuzzy Inference System / Container houses

References

[1] Adalberth K. Energy use during the life cycle of buildings: a method. Build Environ, 1997, 32(4): 317-320

[2] AFAD (2012) Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency 2013–2017 Strategic Plan AFAD Publication from https://www.afad.gov.tr/upload/Node/2584/files/Afad_Strtjk_web_en_son.pdf

[3] Ang and Wyatt (1999) Performance concept in the procurement of durability and serviceability of buildings 8th international conference on durability of building materials and components May 30–June 3 Vancouver

[4] Asdrubali F, Baldassarri C, Fthenakis V. Life cycle analysis in the construction sector: guiding the optimization of conventional Italian buildings. Energy Build, 2013, 64: 73-89

[5] Atmaca N. Life cycle assessment of post-disaster temporary housings in Turkey. Build Res Inf, 2016, 45(5): 524-538

[6] Atmaca A. Life cycle assessment and cost analysis of residential buildings in South East of Turkey: part 1–review and methodology. Int J Life Cycle Assess, 2016, 21(6): 831-846

[7] Atmaca A. Life cycle assessment and cost analysis of residential buildings in South East of Turkey: part 2—a case study. Int J Life Cycle Assess, 2016, 2(7): 925-942

[8] Atmaca A, Atmaca N. Life cycle energy (LCEA) and carbon dioxide emissions (LCCO₂A) assessment of two residential buildings in Gaziantep, Turkey. Energy Build, 2015, 102: 417-431

[9] Babaizadeh H, Hassan M. Life cycle assessment of nano-sized titanium dioxide coating on residential windows. Constr Build Mater, 2013, 40: 314-321

[10] Bastos J, Batterman SA, Freire F. Life-cycle energy and greenhouse gas analysis of three building types in a residential area in Lisbon. Energy Build, 2014, 69: 344-353

[11] Buyle M, Braet J, Audenaert A. Life cycle assessment in the construction sector: a review. Renew Sustain Energy Rev, 2013, 26: 379-388

[12] Collinge WO, Landis AE, Jones AK, Schaefer LA, Bilec MM. Dynamic life cycle assessment: framework and application to an institutional building. Int J Life Cycle Assess, 2013, 18: 538-552

[13] de Gracia A, Rincón L, Castell A, Jiménez M, Boer D, Medrano M, Cabeza LF. Life cycle assessment of the inclusion of phase change materials (PCM) in experimental buildings. Energy Build, 2010, 9: 1517-1523

[14] Fay R, Treloar G, Iyer-Raniga U. Life-cycle energy analysis of buildings: a case study. Build Res Inf, 2000, 28(1): 31-41

[15] Hammond G, Jones C. Inventory of carbon and energy Version 1.6 Sustainable Energy Research Team (SERT), 2008 Bath Department of Mechanical Engineering, University of Bath

[16] Hammond G, Jones C. Inventory of carbon and energy, Version 2.0 Sustainable Energy Research Team (SERT), 2011 Bath Department of Mechanical Engineering, University of Bath

[17] Jang JSR, Sun CT, Mizutani E. Neuro-Fuzzy and soft computing: a computational approach to learning and machine intelligence, 1997 London Prentice-Hall International

[18] Keoleian GA, Blanchard S, Reppe P. Life-cycle energy, costs, and strategies for improving a single-family house. J Ind Ecol, 2000, 2: 135-156

[19] Kofoworola OF, Gheewala SH. Environmental life cycle assessment of a commercial office building in Thailand. Int J Life Cycle Assess, 2008, 6: 498-511

[20] Kua HW, Wong CL. Analysing the life cycle greenhouse gas emission and energy consumption of a multi-storied commercial building in Singapore from an extended system boundary perspective. Energy Build, 2012, 51: 6-14

[21] Liebowitz J. The dynamics of decision support system and expert system, 1990 Orlando The Dryden Press

[22] Radhi H, Sharples S. Global warming implications of facade parameters: a life cycle assessment of residential buildings in Bahrain. Environ Impact Assess, 2013, 38: 99-108

[23] Rutkowski L. Flexible Neuro-Fuzzy systems: structures, learning and performance evaluation, 2004 Dordrecht Kluwer Academic Publishers

[24] Scheuer C, Keoleian GA, Reppe P. Life cycle energy and environmental performance of a new university building: modeling challenges and design implications. Energy Build, 2003, 10: 1049-1064

[25] Singh A, Berghorn G, Joshi S, Syal M. Review of life-cycle assessment applications in building construction. J Archit Eng, 2011, 1: 15-23

[26] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern, 1985, 15: 116-132