Fuzzy Monte Carlo simulation optimization for selecting materials in green buildings
Abstract
Global interest in sustainable and green building design has been increasing in the last few decades. This interest is strengthened by the fact that sustainable measures help in reducing negative social and environmental impacts of buildings. For that, this paper aims to develop a mixed integer optimization model that aids architects/designers and owner representatives during design stage in selecting building materials taking into consideration costs and risks that are involved in the selection process. The model is developed as a simulation optimization tool based on the Leadership in Energy and Environmental Design (LEED) rating system for new construction. The developed model allows deterministic and probabilistic cost analysis of various design alternatives. In addition, it identifies the least possible cost to gain the LEED credits and the risks associated with materials’ quantities and materials’ unit prices. To illustrate the use of the proposed tool, a case study of an office building project constructed in Egypt is presented. An integrated Fuzzy Monte Carlo Simulation (FMCS) analysis is performed to account for the associated risks of using new materials in the considered case study. The proposed model is capable to capture the cost uncertainty of building materials and to identify the cost and sustainability performance of various building materials by relating the LEED rating system for new construction.
Keyword : Fuzzy Monte Carlo Simulation, green buildings, LEED, optimization, materials cost, risk management, sustainability
How to Cite
Marzouk, M. (2020). Fuzzy Monte Carlo simulation optimization for selecting materials in green buildings. Journal of Environmental Engineering and Landscape Management, 28(2), 95-104. https://doi.org/10.3846/jeelm.2020.12087
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ahuja, H. N., Dozzi, S. P., & Abourizk, S. M. (1994). Project management: Techniques in planning and controlling construction projects. John Wiley & Sons.
Akadiri, P. O., Olomolaiye, P. O., & Chinyio, E. A. (2013). Multicriteria evaluation model for the selection of sustainable materials for building projects. Automation in Construction, 30, 113–125. https://doi.org/10.1016/j.autcon.2012.10.004
Antucheviciene, J., Kala, Z., Marzouk, M., & Vaidogas, E. R. (2015). Solving civil engineering problems by means of fuzzy and stochastic MCDM methods: Current state and future research. Mathematical Problems in Engineering, 2015, 362579. https://doi.org/10.1155/2015/362579
Ashby, M. F. (2000). Multi-objective optimization in material design and selection. Acta Materialia, 48(1), 359–369. https://doi.org/10.1016/S1359-6454(99)00304-3
Castro-Lacouture, D., Sefair, J. A., Flórez, L., & Medaglia, A. L. (2009). Optimization model for the selection of materials using a LEED-based green building rating system in Colombia. Building and Environment, 44(6), 1162–1170. https://doi.org/10.1016/j.buildenv.2008.08.009
Chan, J. W., & Tong, T. K. (2007). Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Materials & Design, 28(5), 1539–1546. https://doi.org/10.1016/j.matdes.2006.02.016
Chen, Z. S., Martínez, L., Chang, J. P., Wang, X. J., Xionge, S. H., & Chin, K. S. (2019). Sustainable building material selection: A QFD-and ELECTRE III-embedded hybrid MCGDM approach with consensus building. Engineering Applications of Artificial Intelligence, 85, 783–807. https://doi.org/10.1016/j.engappai.2019.08.006
Clayton, K. (1993). Confronting climatic change: Risks, implications and responses: Mintzer, I. M. (Ed.) Cambridge: Cambridge University Press, 1992. 382 pp. £50 hardback; £19.95 paperback [Book Review]. Applied Geography, 13(3), 289– 290. https://doi.org/10.1016/0143-6228(93)90011-O
Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004). Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing, 10(4), 273–297. https://doi.org/10.1023/B:REOM.0000032115.22510.b5
Farag, M. M. (2014). Quantitative methods of materials selection. In Mechanical Engineers’ Handbook (pp. 1–22). John Wiley & Sons. https://doi.org/10.1002/9781118985960.meh115
Franzoni, E. (2011). Materials selection for green buildings: Which tools for engineers and architects? Procedia Engineering, 21, 883–890. https://doi.org/10.1016/j.proeng.2011.11.2090
Giudice, F. L. R. G., La Rosa, G., & Risitano, A. (2005). Materials selection in the life-cycle design process: A method to integrate mechanical and environmental performances in optimal choice. Materials & Design, 26(1), 9–20. https://doi.org/10.1016/j.matdes.2004.04.006
Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420. https://projecteuclid.org/euclid.ba/1340371036
Heijungs, R., Huppes, G., & Guinée, J. B. (2010). Life cycle assessment and sustainability analysis of products, materials and technologies. Toward a scientific framework for sustainability life cycle analysis. Polymer Degradation and Stability, 95(3), 422–428. https://doi.org/10.1016/j.polymdegradstab.2009.11.010
Holloway, L. (1998). Materials selection for optimal environmental impact in mechanical design. Materials & Design, 19(4), 133–143. https://doi.org/10.1016/S0261-3069(98)00031-4
Jee, D. H., & Kang, K. J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3), 199–206. https://doi.org/10.1016/S0261-3069(99)00066-7
Khishtandar, S. (2019). Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design. Applied Energy, 236, 183–195. https://doi.org/10.1016/j.apenergy.2018.11.092
Kim, Y. J. (2017). Monte Carlo vs. Fuzzy Monte Carlo simulation for uncertainty and global sensitivity analysis. Sustainability, 9(4), 539. https://doi.org/10.3390/su9040539
Langston, C. (2008). Sustainable practices in the built environment. Routledge. https://doi.org/10.4324/9780080518251
Ljungberg, L. Y. (2007). Materials selection and design for development of sustainable products. Materials & Design, 28(2), 466–479. https://doi.org/10.1016/j.matdes.2005.09.006
Lurie, N. H., & Mason, C. H. (2007). Visual representation: Implications for decision making. Journal of Marketing, 71(1), 160–177. https://doi.org/10.1509/jmkg.71.1.160
Marzouk, M., Abdelhamid, M., & Elsheikh, M. (2013). Selecting sustainable building materials using system dynamics and ant colony optimization. Journal of Environmental Engineering and Landscape Management, 21(4), 237–247. https://doi.org/10.3846/16486897.2013.788506
Marzouk, M., Azab, S., & Metawie, M. (2018). BIM-based approach for optimizing life cycle costs of sustainable buildings. Journal of Cleaner Production, 188, 217–226. https://doi.org/10.1016/j.jclepro.2018.03.280
Menassa, C. C. (2011). Evaluating sustainable retrofits in existing buildings under uncertainty. Energy and Buildings, 43(12), 3576–3583. https://doi.org/10.1016/j.enbuild.2011.09.030
Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Mit Press. https://doi.org/10.7551/mitpress/3926.001.0001
Peña, A., Bonet, I., Lochmuller, C., Chiclana, F., & Góngora, M. (2018). An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management. Expert Systems with Applications, 98, 11–26. https://doi.org/10.1016/j.eswa.2018.01.001
Raoufi, M., Seresht, N. G., & Fayek, A. R. (2016, October). Overview of fuzzy simulation techniques in construction engineering and management. In Fuzzy Information Processing Society (NAFIPS), 2016 Annual Conference of the North American (pp. 1–6). El Paso, TX, USA. https://doi.org/10.1109/NAFIPS.2016.7851610
Robati, M., Daly, D., & Kokogiannakis, G. (2019). A method of uncertainty analysis for whole-life embodied carbon emissions (CO2-e) of building materials of a net-zero energy building in Australia. Journal of Cleaner Production, 225, 541–553. https://doi.org/10.1016/j.jclepro.2019.03.339
Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer‐Aided Civil and Infrastructure Engineering, 25(4), 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x
Sameer, H., & Bringezu, S. (2019). Life cycle input indicators of material resource use for enhancing sustainability assessment schemes of buildings. Journal of Building Engineering, 21, 230–242. https://doi.org/10.1016/j.jobe.2018.10.010
Sirisalee, P., Ashby, M. F., Parks, G. T., & Clarkson, P. J. (2004). Multi‐criteria material selection in engineering design. Advanced Engineering Materials, 6(1–2), 84–92. https://doi.org/10.1002/adem.200300554
Teng, J., Mu, X., Wang, W., Xu, C., & Liu, W. (2019). Strategies for sustainable development of green buildings. Sustainable Cities and Society, 44, 215–226. https://doi.org/10.1016/j.scs.2018.09.038
USGBC. (2009). LEED – Leadership in energy and environmental design: Green building rating system, V.3.0. US Green Building Council.
Wang, W., Rivard, H., & Zmeureanu, R. (2005). An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Advanced Engineering Informatics, 19(1), 5–23. https://doi.org/10.1016/j.aei.2005.03.002
Wang, Y., & Ran, W. (2019). Comprehensive eutrophication assessment based on fuzzy matter element model and Monte Carlo-triangular fuzzy numbers approach. International Journal of Environmental Research and Public Health, 16(10), 1769. https://doi.org/10.3390/ijerph16101769
WCED. (1987). Report of the World Commission on environment and development: Our common future. http://www.un-documents.net/our-common-future.pdf
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zheng, D., Yu, L., Wang, L., & Tao, J. (2019). Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation. Sustainable Cities and Society, 44, 291–309. https://doi.org/10.1016/j.scs.2018.10.008
Zhou, C. C., Yin, G.-F., & Hu, X.-B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials & Design, 30(4), 1209–1215. https://doi.org/10.1016/j.matdes.2008.06.006
Akadiri, P. O., Olomolaiye, P. O., & Chinyio, E. A. (2013). Multicriteria evaluation model for the selection of sustainable materials for building projects. Automation in Construction, 30, 113–125. https://doi.org/10.1016/j.autcon.2012.10.004
Antucheviciene, J., Kala, Z., Marzouk, M., & Vaidogas, E. R. (2015). Solving civil engineering problems by means of fuzzy and stochastic MCDM methods: Current state and future research. Mathematical Problems in Engineering, 2015, 362579. https://doi.org/10.1155/2015/362579
Ashby, M. F. (2000). Multi-objective optimization in material design and selection. Acta Materialia, 48(1), 359–369. https://doi.org/10.1016/S1359-6454(99)00304-3
Castro-Lacouture, D., Sefair, J. A., Flórez, L., & Medaglia, A. L. (2009). Optimization model for the selection of materials using a LEED-based green building rating system in Colombia. Building and Environment, 44(6), 1162–1170. https://doi.org/10.1016/j.buildenv.2008.08.009
Chan, J. W., & Tong, T. K. (2007). Multi-criteria material selections and end-of-life product strategy: Grey relational analysis approach. Materials & Design, 28(5), 1539–1546. https://doi.org/10.1016/j.matdes.2006.02.016
Chen, Z. S., Martínez, L., Chang, J. P., Wang, X. J., Xionge, S. H., & Chin, K. S. (2019). Sustainable building material selection: A QFD-and ELECTRE III-embedded hybrid MCGDM approach with consensus building. Engineering Applications of Artificial Intelligence, 85, 783–807. https://doi.org/10.1016/j.engappai.2019.08.006
Clayton, K. (1993). Confronting climatic change: Risks, implications and responses: Mintzer, I. M. (Ed.) Cambridge: Cambridge University Press, 1992. 382 pp. £50 hardback; £19.95 paperback [Book Review]. Applied Geography, 13(3), 289– 290. https://doi.org/10.1016/0143-6228(93)90011-O
Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004). Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing, 10(4), 273–297. https://doi.org/10.1023/B:REOM.0000032115.22510.b5
Farag, M. M. (2014). Quantitative methods of materials selection. In Mechanical Engineers’ Handbook (pp. 1–22). John Wiley & Sons. https://doi.org/10.1002/9781118985960.meh115
Franzoni, E. (2011). Materials selection for green buildings: Which tools for engineers and architects? Procedia Engineering, 21, 883–890. https://doi.org/10.1016/j.proeng.2011.11.2090
Giudice, F. L. R. G., La Rosa, G., & Risitano, A. (2005). Materials selection in the life-cycle design process: A method to integrate mechanical and environmental performances in optimal choice. Materials & Design, 26(1), 9–20. https://doi.org/10.1016/j.matdes.2004.04.006
Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420. https://projecteuclid.org/euclid.ba/1340371036
Heijungs, R., Huppes, G., & Guinée, J. B. (2010). Life cycle assessment and sustainability analysis of products, materials and technologies. Toward a scientific framework for sustainability life cycle analysis. Polymer Degradation and Stability, 95(3), 422–428. https://doi.org/10.1016/j.polymdegradstab.2009.11.010
Holloway, L. (1998). Materials selection for optimal environmental impact in mechanical design. Materials & Design, 19(4), 133–143. https://doi.org/10.1016/S0261-3069(98)00031-4
Jee, D. H., & Kang, K. J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3), 199–206. https://doi.org/10.1016/S0261-3069(99)00066-7
Khishtandar, S. (2019). Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design. Applied Energy, 236, 183–195. https://doi.org/10.1016/j.apenergy.2018.11.092
Kim, Y. J. (2017). Monte Carlo vs. Fuzzy Monte Carlo simulation for uncertainty and global sensitivity analysis. Sustainability, 9(4), 539. https://doi.org/10.3390/su9040539
Langston, C. (2008). Sustainable practices in the built environment. Routledge. https://doi.org/10.4324/9780080518251
Ljungberg, L. Y. (2007). Materials selection and design for development of sustainable products. Materials & Design, 28(2), 466–479. https://doi.org/10.1016/j.matdes.2005.09.006
Lurie, N. H., & Mason, C. H. (2007). Visual representation: Implications for decision making. Journal of Marketing, 71(1), 160–177. https://doi.org/10.1509/jmkg.71.1.160
Marzouk, M., Abdelhamid, M., & Elsheikh, M. (2013). Selecting sustainable building materials using system dynamics and ant colony optimization. Journal of Environmental Engineering and Landscape Management, 21(4), 237–247. https://doi.org/10.3846/16486897.2013.788506
Marzouk, M., Azab, S., & Metawie, M. (2018). BIM-based approach for optimizing life cycle costs of sustainable buildings. Journal of Cleaner Production, 188, 217–226. https://doi.org/10.1016/j.jclepro.2018.03.280
Menassa, C. C. (2011). Evaluating sustainable retrofits in existing buildings under uncertainty. Energy and Buildings, 43(12), 3576–3583. https://doi.org/10.1016/j.enbuild.2011.09.030
Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Mit Press. https://doi.org/10.7551/mitpress/3926.001.0001
Peña, A., Bonet, I., Lochmuller, C., Chiclana, F., & Góngora, M. (2018). An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management. Expert Systems with Applications, 98, 11–26. https://doi.org/10.1016/j.eswa.2018.01.001
Raoufi, M., Seresht, N. G., & Fayek, A. R. (2016, October). Overview of fuzzy simulation techniques in construction engineering and management. In Fuzzy Information Processing Society (NAFIPS), 2016 Annual Conference of the North American (pp. 1–6). El Paso, TX, USA. https://doi.org/10.1109/NAFIPS.2016.7851610
Robati, M., Daly, D., & Kokogiannakis, G. (2019). A method of uncertainty analysis for whole-life embodied carbon emissions (CO2-e) of building materials of a net-zero energy building in Australia. Journal of Cleaner Production, 225, 541–553. https://doi.org/10.1016/j.jclepro.2019.03.339
Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer‐Aided Civil and Infrastructure Engineering, 25(4), 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x
Sameer, H., & Bringezu, S. (2019). Life cycle input indicators of material resource use for enhancing sustainability assessment schemes of buildings. Journal of Building Engineering, 21, 230–242. https://doi.org/10.1016/j.jobe.2018.10.010
Sirisalee, P., Ashby, M. F., Parks, G. T., & Clarkson, P. J. (2004). Multi‐criteria material selection in engineering design. Advanced Engineering Materials, 6(1–2), 84–92. https://doi.org/10.1002/adem.200300554
Teng, J., Mu, X., Wang, W., Xu, C., & Liu, W. (2019). Strategies for sustainable development of green buildings. Sustainable Cities and Society, 44, 215–226. https://doi.org/10.1016/j.scs.2018.09.038
USGBC. (2009). LEED – Leadership in energy and environmental design: Green building rating system, V.3.0. US Green Building Council.
Wang, W., Rivard, H., & Zmeureanu, R. (2005). An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Advanced Engineering Informatics, 19(1), 5–23. https://doi.org/10.1016/j.aei.2005.03.002
Wang, Y., & Ran, W. (2019). Comprehensive eutrophication assessment based on fuzzy matter element model and Monte Carlo-triangular fuzzy numbers approach. International Journal of Environmental Research and Public Health, 16(10), 1769. https://doi.org/10.3390/ijerph16101769
WCED. (1987). Report of the World Commission on environment and development: Our common future. http://www.un-documents.net/our-common-future.pdf
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zheng, D., Yu, L., Wang, L., & Tao, J. (2019). Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation. Sustainable Cities and Society, 44, 291–309. https://doi.org/10.1016/j.scs.2018.10.008
Zhou, C. C., Yin, G.-F., & Hu, X.-B. (2009). Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Materials & Design, 30(4), 1209–1215. https://doi.org/10.1016/j.matdes.2008.06.006