1 Citation (Scopus)
7 Downloads (Pure)

Abstract

Sustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customised information retrieval according to a defined goal.
Original languageEnglish
JournalArchitectural Engineering and Design Management
Volume15
Issue number5
Pages (from-to)334-356
ISSN1745-2007
DOIs
Publication statusPublished - 2019

Fingerprint

Data mining
Decision making
Semantics
Design aids
Decision support systems
Information retrieval
Data structures
Ecodesign
Knowledge discovery
Sustainable design
Decision support
Acoustic waves

Keywords

  • BIM
  • Sustainability
  • Building Design
  • Semantics
  • Data Mining
  • Pattern Recognition
  • Knowledge Discovery in Databases
  • Information Retrieval

Cite this

@article{a99c7a5b6f2b499b8e01bdd31df1bc21,
title = "Towards Data-Driven Sustainable Design: Decision Support based on Knowledge Discovery in Disparate Building Data",
abstract = "Sustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customised information retrieval according to a defined goal.",
keywords = "BIM, Sustainability, Building Design, Semantics, Data Mining, Pattern Recognition, Knowledge Discovery in Databases, Information Retrieval, BIM, Sustainability, Building Design, Semantics, Data Mining, Pattern Recognition, Knowledge Discovery in Databases, Information Retrieval",
author = "Petrova, {Ekaterina Aleksandrova} and Pieter Pauwels and Kjeld Svidt and Jensen, {Rasmus Lund}",
year = "2019",
doi = "10.1080/17452007.2018.1530092",
language = "English",
volume = "15",
pages = "334--356",
journal = "Architectural Engineering and Design Management",
issn = "1745-2007",
publisher = "Earthscan Ltd.",
number = "5",

}

TY - JOUR

T1 - Towards Data-Driven Sustainable Design

T2 - Decision Support based on Knowledge Discovery in Disparate Building Data

AU - Petrova, Ekaterina Aleksandrova

AU - Pauwels, Pieter

AU - Svidt, Kjeld

AU - Jensen, Rasmus Lund

PY - 2019

Y1 - 2019

N2 - Sustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customised information retrieval according to a defined goal.

AB - Sustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customised information retrieval according to a defined goal.

KW - BIM

KW - Sustainability

KW - Building Design

KW - Semantics

KW - Data Mining

KW - Pattern Recognition

KW - Knowledge Discovery in Databases

KW - Information Retrieval

KW - BIM

KW - Sustainability

KW - Building Design

KW - Semantics

KW - Data Mining

KW - Pattern Recognition

KW - Knowledge Discovery in Databases

KW - Information Retrieval

U2 - 10.1080/17452007.2018.1530092

DO - 10.1080/17452007.2018.1530092

M3 - Journal article

VL - 15

SP - 334

EP - 356

JO - Architectural Engineering and Design Management

JF - Architectural Engineering and Design Management

SN - 1745-2007

IS - 5

ER -