Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework

Author:

Jiao Zedong12,Du Xiuli12,Liu Zhansheng12ORCID,Liu Liang12,Sun Zhe12,Shi Guoliang12

Affiliation:

1. Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China

2. The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China

Abstract

Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional ‘factor-energy consumption’ to three parts of ‘factor-event-energy consumption’, which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference94 articles.

1. Developing Apartment Maintenance Systems as Urban Operations;Raouf;Kybernetes,1978

2. A review of data-driven building energy consumption prediction studies;Amasyali;Renew. Sustain. Energy Rev.,2018

3. Designing activity-based workspaces: Satisfaction, productivity and physical activity;Candido;Build. Res. Inf.,2019

4. Predicting residential energy consumption using CNN-LSTM neural networks;Kim;Energy,2019

5. Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration;Peng;Adv. Civ. Eng.,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3