BIM and Genetic Algorithm Optimisation for Sustainable Building Envelope Design

BIM and Genetic Algorithm Optimisation for Sustainable Building Envelope Design

Y.-W. Lim H. A. Majid A. A. Samah M. H. Ahmad D. R. Ossen M. F. Harun F. Shahsavari

Department of Architecture, Faculty of Built Environment, Universiti Teknologi Malaysia (UTM), Malaysia

Centre for the Study of Built Environment in the Malay World (KALAM), Institute for Smart Infrastructures and Innovative Construction, UTM

Faculty of Computing, UTM, Malaysia

Institute Sultan Iskandar, UTM, Malaysia

College of Architecture Engineering and Design, Kingdom University, Kingdom of Bahrain

Page: 
151-159
|
DOI: 
https://doi.org/10.2495/SDP-V13-N1-151-159
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
1 January 2018
| Citation

OPEN ACCESS

Abstract: 

Decision-making (DM) at the early building design stages is essential to optimise sustainability performances. Nevertheless, the current methods of optimising building sustainability are complex as they involve multiple design variables and performance objectives. With the development of building information modelling (BIM), complicated buildings can be digitally constructed with precise geometry and accurate information for design optimisation in the early stages of project. Thus, this study explores the use of BIM and Genetic Algorithm (GA) to support DM and optimisation for sustainable building envelope design. To develop a BIM-GA optimisation method, Autodesk Revit template was created to extract data of building envelope from a Base Model (BM). Then, the data were employed to compute overall thermal transfer value (OTTV) and construction cost for BM evaluation and GA optimisation. A hypothetical building was modelled and then analysed using the proposed method as a test case. The BIM-GA optimisation method can address the difficulties of DM on building sustainability in the early design process.

Keywords: 

autodesk revit, decision-making, design process, optimisation, overall thermal transfer value

1. Introduction
2. Purpose of the Study, Scope and Methodology
3. Development of BIM-GA Optimisation Method for Building Envelope Design
4. Test Case
5. Discussion and Conclusion
Acknowledgement
  References

[1] Lim, Y.W., Shahsavari, F., Noor Fazlenawati, M.N.A., Ossen, D.R. & Ahmad, M.H., Developing a BIM-based process-driven decision-making framework for sustainable building envelope design in the tropics. WIT Transaction on the Built Environment, 149, pp. 531–542, 2015. https://doi.org/10.2139/ssrn.2663945

[2] Nielsen, A.N., Jensen, R.L., Larsen, T.S. & Nissen, S.B., Early stage decision support for sustainable building renovation – A review. Building and Environment, 103, pp. 165–181, 2016. https://doi.org/10.1016/j.buildenv.2016.04.009

[3] GBI, Green Building Index (GBI) assessment criteria for non-residential new construction (Version 1.0), GBI: Malaysia, 2009.

[4] Bank, L.C., McCarthy, M., Thompson, B.P. & Menassa, C.C., Integrating BIM with system dynamics as a decision-making framework for sustainable building design and operation. Proceeding of the 1st International Conference On Sustainable Urbanization (ICSU 2010), Hong Kong, China, pp. 15–17, 2010.

[5] Méndez Echenagucia, T., Capozzoli, A., Cascone, Y. & Sassone, M., The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 154, pp. 577–591, 2015. https://doi.org/10.1016/j.apenergy.2015.04.090

[6] Schlueter, A. & Thesseling, F., Building information model based energy/exergy performance assessment in early design stages. Automation in Construction, 18, pp. 153–163, 2009.

[7] Kriegel, E. & Nies, B., Green BIM: Successful Sustainable Design with Building Information Modeling, Indianapolis: Wiley Publishing, 2008.

[8] Oduyemi, O. & Okoroh, M., Building performance modelling for sustainablebuilding design. International Journal of Sustainable Built Environment, 5(2), pp. 461–469, 2016. https://doi.org/10.1016/j.ijsbe.2016.05.004

[9] Ilhan, B. & Yaman, H., Green building assessment tool (GBAT) for integrated BIMbased design decisions. Automation in Construction, 70, pp. 26–37, 2016. https://doi.org/ 10.1016/j.autcon.2016.05.001

[10] Lim, Y.W., Eka, S., Fatemeh, S. & Noor Fazlenawati, M.N.A., Building information modelling for building energy efficiency evaluation integration with Green Building Index (GBI) in Malaysia. In 4th Annual International Conference on Architecture and Civil Engineering (ACE 2016), Singapore, pp. 42–48, 2016.

[11] Jalaei, F. & Jrade, A., Integrating building information modeling (BIM) and LEED system at the conceptual design stage of sustainable buildings. Sustainable Cities and Society, 18, pp. 95–107, 2015. https://doi.org/10.1016/j.scs.2015.06.007

[12] Gossard, D., Lartigue, B. & Thellier, F., Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy and Buildings, 67, pp. 253–260, 2013. https://doi.org/10.1016/j.enbuild.2013.08.026

[13] Azhar, S. & Brown, J., BIM for sustainability analyses. International Journal of Construction Education and Research, 5(4), pp. 276–292, 2009. https://doi.org/10.1080/15578770903355657

[14] Lim, Y.W., Building information modeling for indoor environmental performance analysis. American Journal of Environmental Science, 11(2), pp. 55–61, 2015. https://doi.org/10.3844/ajessp.2015.55.61

[15] Oh, S., Kim, Y., Park, C. & Kim, I., Process-driven BIM-based optimal design using integration of EnergyPlus, Genetic Algorithm, and Pareto Optimality. In 12th Conference of International Building Performance Simulation Association, Sydney, pp. 14–16, 2011.

[16] Chen, L. & Pan, W., BIM-aided variable fuzzy multi-criteria decision making of low-carbon building measures selection. Sustainable Cities Society, 27, pp. 222–232, 2016. https://doi.org/10.1016/j.scs.2016.04.008

[17] Azhar, S., Carlton, W.A., Olsen, D. & Ahmad, I., Building information modeling for sustainable design and LEED® rating analysis. Automation in Construction, 20(2), pp. 217–224, 2011. https://doi.org/10.1016/j.autcon.2010.09.019

[18] Crawley, D.B., Hand, J.W., Kummert, M. & Griffith, B.T., Contrasting the capabilities of building energy performance simulation programs. 9th International IBPSA Conference, 43, pp. 231–238, 2005.

[19] Kymmell, W., Building Information Modeling: Planning and Managing Construction Projects with 4D CAD and Simulations. McGraw Hill Professional, 2007.

[20] Machairas, V., Tsangrassoulis, A. & Axarli, K., Algorithms for optimization of building design: A review. Renewable Sustainable Energy Review, 31, pp. 101–112, 2014. https://doi.org/10.1016/j.rser.2013.11.036

[21] Jiang, F., Wang, X. & Zhang, Y., Analytical optimization of specific heat of building internal envelope. Energy Conversion and Management, 63, pp. 239–244, 2012. https://doi.org/10.1016/j.enconman.2012.01.038

[22] Daouas, N., A study on optimum insulation thickness in walls and energy savings in Tunisian buildings based on analytical calculation of cooling and heating transmission loads. Applied Energy, 88(1), pp. 156–164, 2011. https://doi.org/10.1016/j.apenergy.2010.07.030

[23] Rapone, G. & Saro, O., Optimisation of curtain wall façades for office buildings by means of PSO algorithm. Energy and Buildings, 45, pp. 189–196, 2012. https://doi.org/10.1016/j.enbuild.2011.11.003

[24] Herrera, F. & Lozano, M., Adaptation of genetic algorithm parameters based on fuzzy logic controllers. Genetic Algorithms and Soft Computing, pp. 95–125, 1996.

[25] Sharma, C., Sabharwal, S. & Sibal, R., A survey on software testing techniques using genetic algorithm. International Journal of Computer Science Issues, 10(1), pp. 381–393, 2013.

[26] Department of Standards Malaysia, Malaysian Standard 1525:2014 - Energy efficiency and use of renewable energy for non-residential buildings - Code of practice (2nd revision). 2014.

[27] Vijayalaxmi, J., Concept of overall thermal transfer value (OTTV) in Design of building envelope to achieve energy efficiency. International Journal of Thermal and Environmental Engineering, 1(2), pp. 75–80, 2010. https://doi.org/10.5383/ijtee.01.02.003