Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review

Touloupaki, Eleftheria; Theodosiou, Theodoros

doi:10.3390/en10050637

Cited by 85 publications

(42 citation statements)

References 73 publications

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“…Based on evolutionary algorithms (EAs), evolutionary optimization can technically solve performance-based building design problems by evolving a population of design solutions. For architectural design, such an evolutionary process can be guided by various energy-related performance criteria such as daylighting and passive solar energy (Touloupaki and Theodosiou, 2017). In addition, evolutionary optimization also allows for an automated exploration of the design space, which facilitates architects to extract information about the design problem.…”

Section: Introductionmentioning

confidence: 99%

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Wang

Janssen

2020

AIEDAM

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Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

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Section: Introductionmentioning

confidence: 99%

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Wang

Janssen

2020

AIEDAM

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“…Although SBMOO methods have been increasingly used to support conceptual architectural design in recent years [34][35][36], there are still a number of shortcomings. A major one is that, in general, existing SBMOO methods have largely been "cut and paste" from the field of detailed engineering design, with little-to-no adaptation for the needs required in conceptual architectural design [37], as specified below.…”

Section: Problemmentioning

confidence: 99%

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Ding

Stefano²,

Turrin

et al. 2020

Automation in Construction

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Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometrysimulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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“…These software can deliver a simulation-based optimisation workflow that updates the target model with the desirable design parameters seamlessly [21,83]. A few attempts have been made to formulate different window design problems using optimisation packages in modelling environment.…”

Section: Optimisation Techniquementioning

confidence: 99%

Optimizing Window Design on Residential Building Facades by Considering Heat Transfer and Natural Lighting in Non-tropical Regions of Australia

Chen¹,

Hammad²,

Kamardeen³

et al. 2020

Preprint

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Windows account for a significant proportion of the total energy lost in buildings. The interaction of window type, Window-to-Wall Ratio (WWR) scheduled and window placement height would influence the natural lighting and heat transfer through windows. This is a pressing issue for non-tropical regions considering their high emissions and distinct climatic characteristics. A limitation exists in the adoption of common simulation-based optimisation approaches in the literature, which are hardly accessible to practitioners. This article develops a numerical-based window design optimisation model using a common Building Information Modelling (BIM) platform adopted throughout the industry, focusing on non-tropical regions of Australia. Three objective functions are proposed; the first objective is to maximize the available daylight, and the other two emphasize on the undesirable heat transfer through windows in summer and winter respectively. The developed model is tested on a case study located in Sydney, Australia, and a set of Pareto-optimum solutions is obtained. Through the use of the proposed model, energy savings of up to 16.43% are achieved. Key findings on the case example indicate that leveraging winter heat gain to reduce annual energy consumption should not be the top priority when designing windows for Sydney.

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Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review

Cited by 85 publications

References 73 publications

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Optimizing Window Design on Residential Building Facades by Considering Heat Transfer and Natural Lighting in Non-tropical Regions of Australia

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