Fuzzy Monte Carlo simulation optimization for selecting materials in green buildings

    Mohamed Marzouk   Affiliation


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.
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Apr 27, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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.

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.

Ashby, M. F. (2000). Multi-objective optimization in material design and selection. Acta Materialia, 48(1), 359–369.

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.

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.

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.

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.

Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004). Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing, 10(4), 273–297.

Farag, M. M. (2014). Quantitative methods of materials selection. In Mechanical Engineers’ Handbook (pp. 1–22). John Wiley & Sons.

Franzoni, E. (2011). Materials selection for green buildings: Which tools for engineers and architects? Procedia Engineering, 21, 883–890.

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.

Goldstein, M. (2006). Subjective Bayesian analysis: Principles and practice. Bayesian Analysis, 1(3), 403–420.

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.

Holloway, L. (1998). Materials selection for optimal environmental impact in mechanical design. Materials & Design, 19(4), 133–143.

Jee, D. H., & Kang, K. J. (2000). A method for optimal material selection aided with decision making theory. Materials & Design, 21(3), 199–206.

Khishtandar, S. (2019). Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design. Applied Energy, 236, 183–195.

Kim, Y. J. (2017). Monte Carlo vs. Fuzzy Monte Carlo simulation for uncertainty and global sensitivity analysis. Sustainability, 9(4), 539.

Langston, C. (2008). Sustainable practices in the built environment. Routledge.

Ljungberg, L. Y. (2007). Materials selection and design for development of sustainable products. Materials & Design, 28(2), 466–479.

Lurie, N. H., & Mason, C. H. (2007). Visual representation: Implications for decision making. Journal of Marketing, 71(1), 160–177.

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.

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.

Menassa, C. C. (2011). Evaluating sustainable retrofits in existing buildings under uncertainty. Energy and Buildings, 43(12), 3576–3583.

Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Mit Press.

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.

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.

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.

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.

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.

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.

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.

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.

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.

WCED. (1987). Report of the World Commission on environment and development: Our common future.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

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.

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.