BibTex RIS Cite

A NOVEL APPROACH TO LIFE SPAN PREDICTION OF CONTAINER HOUSES VIA ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Year 2016, Volume: 1 Issue: 2, 45 - 55, 01.04.2016

Nihat Atmaca

Abstract

Buildings are expected to be long-lived engineered works under usual conditions. In Life Cycle Assessment (LCA) of building analysis, Life Span is one of the most effective parameter. The aim of this study is to make the Life Cycle Assessment analysis of containers and to investigate the relationship between Life Span and consumed energy via Adaptive Neuro-Fuzzy Inference System (ANFIS) approach. The proposed model in the study focused on the construction phase of the containers to estimate total energy use for different life span years. Life span years are chosen between 5-100 years interval. It is found that energy and emission values are decreasing with the increase of life span years in container type houses. The results of the proposed ANFIS modeling approach shows very promising results. According to the results ANFIS approach is a viable tool for Life Span more accurate predictions in LCA studies

Keywords

Life Cycle Assessment Life Span Adaptive Neuro-Fuzzy Inference System Containers

References

  • Adalberth K (1997) Energy use during the life cycle of buildings: a method. Building and Environment 32 (4): 317–320.
  • 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.pd f.
  • Atmaca A and Atmaca N (2015) Life cycle energy (LCEA) and carbon dioxide emissions (LCCO2A) assessment of two residential buildings in Gaziantep, Turkey. Energy and Buildings 102: 417-431.
  • Atmaca N (2016) Life cycle assessment of post-disaster temporary housings in Turkey. Building Research& Information, http://dx.doi.org/10.1080/09613218.2015.1127116.
  • Bai,Y., Zhuang, H., andWang, D. (2006) Advanced fuzzy logic technologies in industrial applications. Springer.
  • Bastos J, Batterman SA and Freire F (2014) Life-cycle energy and greenhouse gas analysis of three building types in a residential area in Lisbon. Energy and Buildings 69: 344–353.
  • Buyle M, Braet J and Audenaert A (2013) Life cycle assessment in the construction sector: a review. Renewable& Sustainable Energy Reviews 26: 379–88.
  • Donald A.W. (1986) A Guide to Expert Systems, (Addison-Wesley: Reading, MA). Fay R, Treloar G and Iyer-Raniga U (2000) Life-cycle energy analysis of buildings: a case study. Building Research and Information 28 (1): 31–41.
  • Liebowitz, J. (1990) The Dynamics of Decision Support System and Expert System, (The Dryden Press: Orlando).
  • Hammond G and Jones C(2008) Inventory of Carbon and Energy, Version 1.6 Sustainable Energy Research Team (SERT), Department of Mechanical Engineering, University of Bath, UK.
  • Hammond G and Jones C(2011). Inventory of Carbon and Energy, Version 2.0 Sustainable Energy Research Team (SERT), Department of Mechanical Engineering, University of Bath, UK.
  • Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997) Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, (Prentice-Hall International: London).
  • Rutkowski, L. (2004). Flexible neuro-fuzzy systems: structures, learning and performance evaluation. Kluwer Academic Publishers.
  • Singh, A., Berghorn,G., Joshi, S.& Syal, M. (2011). Review of life-cycle assessment applications in building construction. Journal of Architectural Engineering, 1, 15– 23.
  • Takagi, T. and Sugeno M. (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern., 15, 116–132

Year 2016, Volume: 1 Issue: 2, 45 - 55, 01.04.2016

Nihat Atmaca

Abstract

References

  • Adalberth K (1997) Energy use during the life cycle of buildings: a method. Building and Environment 32 (4): 317–320.
  • 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.pd f.
  • Atmaca A and Atmaca N (2015) Life cycle energy (LCEA) and carbon dioxide emissions (LCCO2A) assessment of two residential buildings in Gaziantep, Turkey. Energy and Buildings 102: 417-431.
  • Atmaca N (2016) Life cycle assessment of post-disaster temporary housings in Turkey. Building Research& Information, http://dx.doi.org/10.1080/09613218.2015.1127116.
  • Bai,Y., Zhuang, H., andWang, D. (2006) Advanced fuzzy logic technologies in industrial applications. Springer.
  • Bastos J, Batterman SA and Freire F (2014) Life-cycle energy and greenhouse gas analysis of three building types in a residential area in Lisbon. Energy and Buildings 69: 344–353.
  • Buyle M, Braet J and Audenaert A (2013) Life cycle assessment in the construction sector: a review. Renewable& Sustainable Energy Reviews 26: 379–88.
  • Donald A.W. (1986) A Guide to Expert Systems, (Addison-Wesley: Reading, MA). Fay R, Treloar G and Iyer-Raniga U (2000) Life-cycle energy analysis of buildings: a case study. Building Research and Information 28 (1): 31–41.
  • Liebowitz, J. (1990) The Dynamics of Decision Support System and Expert System, (The Dryden Press: Orlando).
  • Hammond G and Jones C(2008) Inventory of Carbon and Energy, Version 1.6 Sustainable Energy Research Team (SERT), Department of Mechanical Engineering, University of Bath, UK.
  • Hammond G and Jones C(2011). Inventory of Carbon and Energy, Version 2.0 Sustainable Energy Research Team (SERT), Department of Mechanical Engineering, University of Bath, UK.
  • Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997) Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, (Prentice-Hall International: London).
  • Rutkowski, L. (2004). Flexible neuro-fuzzy systems: structures, learning and performance evaluation. Kluwer Academic Publishers.
  • Singh, A., Berghorn,G., Joshi, S.& Syal, M. (2011). Review of life-cycle assessment applications in building construction. Journal of Architectural Engineering, 1, 15– 23.
  • Takagi, T. and Sugeno M. (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern., 15, 116–132
There are 15 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Nihat Atmaca This is me

Publication Date April 1, 2016
Published in Issue Year 2016 Volume: 1 Issue: 2

Cite

APA Atmaca, N. (2016). A NOVEL APPROACH TO LIFE SPAN PREDICTION OF CONTAINER HOUSES VIA ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. The International Journal of Energy and Engineering Sciences, 1(2), 45-55.

IMPORTANT NOTES

No part of the material protected by this copyright may be reproduced or utilized in any form or by any means, without the prior written permission of the copyright owners, unless the use is a fair dealing for the purpose of private study, research or review. The authors reserve the right that their material can be used for purely educational and research purposes. All the authors are responsible for the originality and plagiarism, multiple publication, disclosure and conflicts of interest and fundamental errors in the published works.

*Please note that  All the authors are responsible for the originality and plagiarism, multiple publication, disclosure and conflicts of interest and fundamental errors in the published works. Author(s) submitting a manuscript for publication in IJEES also accept that the manuscript may go through screening for plagiarism check using IThenticate software. For experimental works involving animals, approvals from relevant ethics committee should have been obtained beforehand assuring that the experiment was conducted according to relevant national or international guidelines on care and use of laboratory animals.  Authors may be requested to provide evidence to this end.
 
**Authors are highly recommended to obey the IJEES policies regarding copyrights/Licensing and ethics before submitting their manuscripts.


Copyright © 2023. AA. All rights reserved