BIM, IoT, and data mining integration framework for improving green building energy resilience

Guofeng Qiang; Shu Tang

doi:10.1061/9780784485224.014

BIM, IoT, and data mining integration framework for improving green building energy resilience

Guofeng Qiang
, Shu Tang

Department of Civil Engineering

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

Green buildings (GB) aim to minimize the environmental impact of construction and operation activities. The adoption of building information modeling (BIM), the Internet of Things (IoT), and data mining (DM) presents promising opportunities for managing GBs with high energy efficiency. However, research on integrating these digital technologies to enhance GB energy resilience requires further improvement. This study reviews the applications of these three digital technologies in GB. Subsequently, an integration framework is constructed using semantic technologies to enable data exchange among BIM, IoT, and DM. Additionally, this paper illustrates how digital technologies can improve energy resilience in GB. Finally, the limitations and challenges of enhancing GB energy resilience are discussed. This research provides valuable insights into the practical application of digital technologies to advance energy efficiency in GBs. The proposed conceptual integration framework is a foundation for a digital solution that improves GB energy resilience.

Original language	English
Title of host publication	Computing in Civil Engineering 2023
Subtitle of host publication	Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023
Editors	Yelda Turkan, Joseph Louis, Fernanda Leite, Semiha Ergan
Publisher	American Society of Civil Engineers (ASCE)
Pages	109-115
Number of pages	7
ISBN (Electronic)	9780784485224
DOIs	https://doi.org/10.1061/9780784485224.014
Publication status	Published - 2024
Event	ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023 - Corvallis, United States Duration: 25 Jun 2023 → 28 Jun 2023

Publication series

Name	Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023

Conference

Conference	ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023
Country/Territory	United States
City	Corvallis
Period	25/06/23 → 28/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

BIM
Data mining
Energy resilience
Green buildings
Integration framework
IoT

Access to Document

10.1061/9780784485224.014

Cite this

Qiang, G., & Tang, S. (2024). BIM, IoT, and data mining integration framework for improving green building energy resilience. In Y. Turkan, J. Louis, F. Leite, & S. Ergan (Eds.), Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023 (pp. 109-115). (Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784485224.014

Qiang, Guofeng ; Tang, Shu. / BIM, IoT, and data mining integration framework for improving green building energy resilience. Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023. editor / Yelda Turkan ; Joseph Louis ; Fernanda Leite ; Semiha Ergan. American Society of Civil Engineers (ASCE), 2024. pp. 109-115 (Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023).

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title = "BIM, IoT, and data mining integration framework for improving green building energy resilience",

abstract = "Green buildings (GB) aim to minimize the environmental impact of construction and operation activities. The adoption of building information modeling (BIM), the Internet of Things (IoT), and data mining (DM) presents promising opportunities for managing GBs with high energy efficiency. However, research on integrating these digital technologies to enhance GB energy resilience requires further improvement. This study reviews the applications of these three digital technologies in GB. Subsequently, an integration framework is constructed using semantic technologies to enable data exchange among BIM, IoT, and DM. Additionally, this paper illustrates how digital technologies can improve energy resilience in GB. Finally, the limitations and challenges of enhancing GB energy resilience are discussed. This research provides valuable insights into the practical application of digital technologies to advance energy efficiency in GBs. The proposed conceptual integration framework is a foundation for a digital solution that improves GB energy resilience.",

keywords = "BIM, Data mining, Energy resilience, Green buildings, Integration framework, IoT",

author = "Guofeng Qiang and Shu Tang",

note = "Publisher Copyright: {\textcopyright} International Conference on Computing in Civil Engineering 2023.All rights reserved.; ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023 ; Conference date: 25-06-2023 Through 28-06-2023",

year = "2024",

doi = "10.1061/9780784485224.014",

language = "English",

series = "Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023",

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Qiang, G & Tang, S 2024, BIM, IoT, and data mining integration framework for improving green building energy resilience. in Y Turkan, J Louis, F Leite & S Ergan (eds), Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023. Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023, American Society of Civil Engineers (ASCE), pp. 109-115, ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023, Corvallis, United States, 25/06/23. https://doi.org/10.1061/9780784485224.014

BIM, IoT, and data mining integration framework for improving green building energy resilience. / Qiang, Guofeng; Tang, Shu.
Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023. ed. / Yelda Turkan; Joseph Louis; Fernanda Leite; Semiha Ergan. American Society of Civil Engineers (ASCE), 2024. p. 109-115 (Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - BIM, IoT, and data mining integration framework for improving green building energy resilience

AU - Qiang, Guofeng

AU - Tang, Shu

PY - 2024

Y1 - 2024

N2 - Green buildings (GB) aim to minimize the environmental impact of construction and operation activities. The adoption of building information modeling (BIM), the Internet of Things (IoT), and data mining (DM) presents promising opportunities for managing GBs with high energy efficiency. However, research on integrating these digital technologies to enhance GB energy resilience requires further improvement. This study reviews the applications of these three digital technologies in GB. Subsequently, an integration framework is constructed using semantic technologies to enable data exchange among BIM, IoT, and DM. Additionally, this paper illustrates how digital technologies can improve energy resilience in GB. Finally, the limitations and challenges of enhancing GB energy resilience are discussed. This research provides valuable insights into the practical application of digital technologies to advance energy efficiency in GBs. The proposed conceptual integration framework is a foundation for a digital solution that improves GB energy resilience.

AB - Green buildings (GB) aim to minimize the environmental impact of construction and operation activities. The adoption of building information modeling (BIM), the Internet of Things (IoT), and data mining (DM) presents promising opportunities for managing GBs with high energy efficiency. However, research on integrating these digital technologies to enhance GB energy resilience requires further improvement. This study reviews the applications of these three digital technologies in GB. Subsequently, an integration framework is constructed using semantic technologies to enable data exchange among BIM, IoT, and DM. Additionally, this paper illustrates how digital technologies can improve energy resilience in GB. Finally, the limitations and challenges of enhancing GB energy resilience are discussed. This research provides valuable insights into the practical application of digital technologies to advance energy efficiency in GBs. The proposed conceptual integration framework is a foundation for a digital solution that improves GB energy resilience.

KW - BIM

KW - Data mining

KW - Energy resilience

KW - Green buildings

KW - Integration framework

KW - IoT

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DO - 10.1061/9780784485224.014

M3 - Conference Proceeding

AN - SCOPUS:85184287658

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SP - 109

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BT - Computing in Civil Engineering 2023

A2 - Turkan, Yelda

A2 - Louis, Joseph

A2 - Leite, Fernanda

A2 - Ergan, Semiha

PB - American Society of Civil Engineers (ASCE)

T2 - ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023

Y2 - 25 June 2023 through 28 June 2023

ER -

Qiang G, Tang S. BIM, IoT, and data mining integration framework for improving green building energy resilience. In Turkan Y, Louis J, Leite F, Ergan S, editors, Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023. American Society of Civil Engineers (ASCE). 2024. p. 109-115. (Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023). doi: 10.1061/9780784485224.014

BIM, IoT, and data mining integration framework for improving green building energy resilience

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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