Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

Abstract

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior.

Keywords

Parametric modelling / Multi-objective optimization (MOO) / Integrated building envelope design process (IBEDP) / Two-step optimization strategy

References

[1] Brownlee AEI, Wright JA (2012) Solution analysis in multi-objective optimization. In: IBPSA-ENGLAND: first building simulation and optimization conference. Loughborough University, Loughborough, UK, pp 317–324

[2] Wang W, Zmeureanu R, Rivard H. Applying multi-objective genetic algorithms in green building design optimization. Build Environ, 2005, 40(11): 1512-1525.

[3] Asadi E, Da Silva MG, Antunes CH, et al. Multi-objective optimization for building retrofit strategies: a model and an application. Energy Build, 2012, 44: 81-87.

[4] Bénabes J, Bennis F, Poirson E, et al. Interactive optimization strategies for layout problems. Int J Interact Des Manuf, 2010, 4(3): 181-190.

[5] Del Río-Cidoncha MG, Iglesias JE, Martínez-Palacios J. A comparison of floor plan design strategies in architecture and engineering. Autom Constr, 2007, 16(5): 559-568.

[6] Yan W, Asl MR, Su Z et al (2015) Towards multi-objective optimization for sustainable buildings with both quantifiable and non-quantifiable design objectives. In: Sustainable human-building ecosystems: 1st international symposium on sustainable human-building ecosystems. American Society of Civil Engineers, Pittsburgh, USA, pp 223–230

[7] Hopfe CJ (2009) Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. Department of the Built Environment, Eindhoven University, Eindhoven, the Netherlands

[8] Eisenhower B, O’Neill Z, Narayanan S, et al. A methodology for meta-model based optimization in building energy models. Energy Build, 2012, 47: 292-301.

[9] Su Z, Yan W. Creating and improving a closed loop: design optimization and knowledge discovery in architecture. Int J Archit Comput, 2015, 13(2): 123-142.

[10] Caldas LG, Norford LK. A design optimization tool based on a genetic algorithm. Autom Constr, 2002, 11(2): 173-184.

[11] Suga K, Kato S, Hiyama K. Structural analysis of Pareto-optimal solution sets for multi-objective optimization: an application to outer window design problems using multiple objective genetic algorithms. Build Environ, 2010, 45(5): 1144-1152.

[12] Yi YK, Malkawi AM. Optimizing building form for energy performance based on hierarchical geometry relation. Autom Constr, 2009, 18(6): 825-833.

[13] Caldas L. Generation of energy-efficient architecture solutions applying GENE_ARCH: an evolution-based generative design system. Adv Eng Inform, 2008, 22(1): 59-70.

[14] Wright JA, Brownlee A, Mourshed MM, et al. Multi-objective optimization of cellular fenestration by an evolutionary algorithm. J Build Perform Simul, 2014, 7(1): 33-51.

[15] Bernal W, Behl M, Nghiem TX et al (2012) MLE+: a tool for integrated design and deployment of energy efficient building controls. In: Proceedings of the 4th ACM workshop on embedded sensing systems for energy-efficiency in buildings. ACM, pp 123–130

[16] Shi X. Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, 2011, 36(3): 1659-1667.

[17] Karaguzel OT, Zhang R, Lam KP. Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings. Build Simul, 2014, 7(2): 111-121.

[18] Shi X, Yang W. Performance-driven architectural design and optimization technique from a perspective of architects. Autom Constr, 2013, 32: 125-135.

[19] Turrin M, von Buelow P, Stouffs R. Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Adv Eng Inform, 2011, 25(4): 656-675.

[20] Roudsari MS, Pak M, Smith A et al (2013) Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. In: Proceedings of the 13th international IBPSA conference. Lyon, France

[21] Asl MR, Stoupine A, Zarrinmehr S et al (2015) Optimo: a BIM-based multi-objective optimization tool utilizing visual programming for high performance building design. In: eCAADe: proceedings of the conference of education and research in computer aided architectural design in europe. eCAADe and TU Wien, Vienna, Austria, pp 673–682

[22] Nembrini J, Samberger S, Sternitzke A et al (2012) Combining sensitivity analysis with parametric modeling to inform early design. In: SCS: proceedings of the 2012 symposium on simulation for architecture and urban design. Orlando, USA, pp 39–46

[23] Nabil A. Useful daylight illuminance: a new paradigm for assessing daylight in buildings. Light Res Technol, 2005, 37(1): 41-59.