Acommodating Change in Parametric Design

Vierlinger, Robert; Bollinger, Klaus

doi:10.52842/conf.acadia.2014.609

Cited by 11 publications

(4 citation statements)

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“…Therefore, a GA has become very common as types of optimization algorithms. The GA is commonly used in the field of multi-objective performance optimization by combining technologies for parametric modelling platforms [26,27]. The utilization of GA to solve optimization issues is sensible and likely successful in finding the best solutions in a relatively short computational period [9].…”

Section: Optimization Processmentioning

confidence: 99%

Optimization of Daylight Performance Based on Controllable Light-shelf Parameters using Genetic Algorithms in the Tropical Climate of Malaysia

Bahdad

Fadzil

Taib

2020

Journal of Daylighting

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This study aims to achieve a balance of daylight availability in the work-plane environments of a fully glazed facade integrated with a light shelf system using an optimization procedure that can assist architects with assessing the daylighting performance of numerous design alternatives, and build-up the optimized design. The approach uses parametric design, simulation modelling, and genetic algorithms. A case study of a typical office is carried out to test and verify the effectiveness of the optimization procedure for a light shelf system. Five parameters of light shelf design are optimized in two solar solstices (June and December) and one equinox in March under the Malaysian sky conditions. The optimization results indicate that the optimal design options of light-shelf parameters have great potential for illuminance improvement. After the optimization, the daylighting performance of useful daylight illuminance compared to reference models is increased respectively with an average value of 15.6% and 4.7% on the 21st of June, and by 17.5% and 5.8% on 21st of March, and by 5.8% and 11.3% on 21st of December. Statistical analysis is achieved to investigate the relationship between the performance metric of the optimal design options and the other cases, the outcomes showed that the regression analysis indicated a high level of reliability as well as different levels of variation coefficients.

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Section: Optimization Processmentioning

confidence: 99%

Optimization of Daylight Performance Based on Controllable Light-shelf Parameters using Genetic Algorithms in the Tropical Climate of Malaysia

Bahdad

Fadzil

Taib

2020

Journal of Daylighting

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“…PYMOO, an open-source optimisation library for python, is used to build and test MOO and SOO optimisation models. 22 In comparison to existing optimisation plugins for Grasshopper, like the well-established Opossum 23 and Octopus, 24 this presents the advantage of permitting automated workflows between python-based analytics/ML techniques and adjustments to the optimisation model. The hybrid workflow combining optimisation and clustering is described in further detail below.…”

Section: Methodsmentioning

confidence: 99%

Exploring a circular economy solution space A comparative study to develop automated optimisation workflows supported by machine learning for circular design problems

Ortner

Tay

2023

International Journal of Architectural Computing

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Embedding circular economy (CE) principles in early design requires iterative evaluation across multiple lifecycle phases, with trade-offs between objectives complicating the identification of best solutions. This paper puts forward methods to automatically discover diverse, yet well-performing solution types within complex multi-objective CE design optimisation models. Working with a parametric model derived from a furniture design for CE case study, a comparison is made between weighted-sum single objective optimisation and multi-objective optimisation augmented with clustered solution types targeted by the reference point-based NSGA-II optimisation algorithm. Efficiency of optimisation, quality of results and distinctiveness of solution types presented by each method is compared in an effort to understand which will best assist designers to manage complexity in CE design. The generalisability of the presented methods to larger scale CE design problems is discussed and future areas of work on computational design for CE are extrapolated from the presented results.

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“…Janssen et al ( 2011), Janssen (2013Janssen ( , 2015, Von Buelow ( , 2016, Vierlinger and Bollinger (2014), Negendahl and Nielsen (2015), Danhaive and Mueller (2015), Integration and Design Optimization (PIDO) software. The integration approach used is a BIM file-based approach (Bernal et al, 2015).…”

Section: Scope Of the Reviewmentioning

confidence: 99%

Design as Exploration

Yang

2023

Architecture and the Built Environment

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There are an increasing number of optimal-design paradigms used in architectural design nowadays. In these paradigms, a design task is formulated, or partially formulated, as an optimization problem. Multi-Disciplinary Optimization and Multi-Objective Optimization, as two important optimal-design paradigms, have shown their great potential in improving the performances of complex buildings in recent decades. Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms. To address this problem, it is necessary to shift the focus from Optimization Problem Solving to Optimization Problem Formulation. This research particularly focuses on knowledge‑supported, dynamic and interactive Optimization Problem Re-Formulation in order to construct a new Multi‑Objective and Multi-Disciplinary Optimization (MOMDO) method suitable for use in ill‑defined conceptual architectural design. The proposed method consists of two subtype methods: Non‑dynamic, Interactive Re-formulation method (Subtype-I) and Dynamic, Interactive Re‑formulation method (Subtype-II), which can be used to explore design space in a convergent and divergent manner respectively. To support the re-formulation, various kinds of information and knowledge need to be extracted by utilizing different computational techniques, such as advanced sampling algorithms, Self-Organizing Map, Hierarchical Clustering, Smoothing Spline Analysis of Variance, Two-Level Variable Structure and modular programming. Moreover, a software workflow that can provide these computational techniques is developed; it integrates McNeel’s Grasshopper, ESTECO's modeFRONTIER and simulation software tools Daysim, EnergyPlus and Karamba3D. With the support of this software workflow, the proposed method is demonstrated via two case studies concerning the conceptual design of indoor sports halls.

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Acommodating Change in Parametric Design

Cited by 11 publications

References 0 publications

Optimization of Daylight Performance Based on Controllable Light-shelf Parameters using Genetic Algorithms in the Tropical Climate of Malaysia

Optimization of Daylight Performance Based on Controllable Light-shelf Parameters using Genetic Algorithms in the Tropical Climate of Malaysia

Exploring a circular economy solution space A comparative study to develop automated optimisation workflows supported by machine learning for circular design problems

Design as Exploration

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