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Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

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Listed:
  • Seung Yeoun Choi

    (Hanil Mechanical & Electrical Consultant, Seoul 07271, Korea)

  • Sean Hay Kim

    (School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Korea)

Abstract

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Suggested Citation

  • Seung Yeoun Choi & Sean Hay Kim, 2021. "Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System," Sustainability, MDPI, vol. 13(9), pages 1-36, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4640-:d:540848
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    References listed on IDEAS

    as
    1. Sean Hay Kim & Jungmin Nam, 2020. "Can Both the Economic Value and Energy Performance of Small- and Mid-Sized Buildings Be Satisfied? Development of a Design Expert System in the Context of Korea," Sustainability, MDPI, vol. 12(12), pages 1-29, June.
    2. Homaei, Shabnam & Hamdy, Mohamed, 2020. "A robustness-based decision making approach for multi-target high performance buildings under uncertain scenarios," Applied Energy, Elsevier, vol. 267(C).
    3. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    4. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    Full references (including those not matched with items on IDEAS)

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