Design exploration of quantitative performance and geometry typology for indoor arena based on self-organizing map and multi-layered perceptron neural network

Wang, Pan; Sun, Ye; Turrin, Michela; Louter, Christian; Sarıyıldız, Sevil

doi:10.1016/j.autcon.2020.103163

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Performance-based parametric design operates in the conceptual "design space", containing all possible alternatives that can be generated by a parametric script, and the "objective space", covering the designs based on their performance, thus designers get quick feedback about how different alternatives behave and get guidance for decision-making (Brown et al 2020). Parametric and performance-based design tools in building design have been widely employed by authors in architectural and structural design domain, focusing on design space exploration and structural optimization of sub-systems (Brown et al 2020;Brown and Mueller 2016;Makris et al 2013;Mueller and Ochsendorf 2015;Pan et al 2020;Turrin et al 2011). In this general approach, a parametric design process shows remarkable potential to support optimization and decision making in IIBD.…”

Section: Literature Reviewmentioning

confidence: 99%

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021

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Industrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment tools do not respect flexibility metrics and manufacturing and building design processes run sequentially, neglecting discipline-specific interaction, leading to inflexible solutions. In integrated industrial building design (IIBD), incorporating manufacturing and building disciplines simultaneously, design teams are faced with the choice of multiple conflicting criteria and complex design decisions, opening up a huge design space. To address these issues, this paper presents a parametric design process for efficient design space exploration in IIBD. A state-of-the-art survey and multiple case study are conducted to define four novel flexibility metrics and to develop a unified design space, respecting both building and manufacturing requirements. Based on these results, a parametric design process for automated structural optimization and quantitative flexibility assessment is developed, guiding the decision-making process towards increased sustainability. The proposed framework is tested on a pilot-project of a food and hygiene production, evaluating the design space representation and validating the flexibility metrics. Results confirmed the efficiency of the process that an evolutionary multi-objective optimization algorithm can be implemented in future research to enable multidisciplinary design optimization for flexible industrial building solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021

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“…The advance in the design tool has stimulated relevant research, and recent years have witnessed rapid development in the field. In most recent studies, the research focus has shifted to developing and evaluating new algorithms or workflows, [14][15][16][17] and many of the studies concentrate on integrating new techniques such as surrogate modeling [18][19][20] and clustering 21,22 into the optimization. In contrast, the design of the building and the corresponding parametric modeling has become insignificant.…”

Section: The Design Issue In Performance-based Building Design Optimi...mentioning

confidence: 99%

Optimization-aided design: two approaches for reflective exploration of design search space

Wang

2022

International Journal of Architectural Computing

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The role of using performance-based design optimization in early-stage architectural design to prevent poorly designed buildings has been increasingly recognized by researchers and designers. Recently, a large amount of research has been made focusing on technical advancement, which, however, also reflects the limited research on how this technique can be applied to the design process and how it can aid designers when confronting ill-defined design problems. In this regard, this paper centers on the design approaches assisted by using performance-based design optimization. The paper proposes two optimization-aided design approaches that were identified in the previous applications of a design tool, called EvoMass, and showcases these approaches in case-study designs. The case studies demonstrate how the use of performance-based design optimization can facilitate designers’ reflection and exploration. With the demonstration of the design approaches, we discuss the utility of performance-based design optimization in assisting architects in the early design stages.

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“…Thus, the practicability of the SOM algorithm was verified. Recently, Pan, Sun, Turrin, Louter and Sariyildiz [10] developed a novel design method for a combined multilayer perceptron neural network (SOM-MLPNN). Although not for use in the field of construction, Gardner et al [11] proposed an algorithm that was more advanced than the existing SOM to produce visualizations of hyperspectral data with improved accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SOM creates a feature map by mapping multidimensional data to two-dimensional (2D) data. Because of its simple applicability, the SOM has been widely applied to optimization problems, robotics and control, function approximation, estimation, and evaluation [10,14,15].…”

Section: Self-organizing Mapsmentioning

confidence: 99%

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Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

et al. 2021

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Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%.

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Design exploration of quantitative performance and geometry typology for indoor arena based on self-organizing map and multi-layered perceptron neural network

Cited by 13 publications

References 41 publications

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

Optimization-aided design: two approaches for reflective exploration of design search space

Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

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