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

Zedong Jiao, Xiuli Du, Zhansheng Liu, Liang Liu, Zhe Sun, Guoliang Shi

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zedong JiaoFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Xiuli DuFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Zhansheng LiuFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0002-4251-5702

Liang LiuFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Zhe SunFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Guoliang ShiFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

building infrastructuresdigital twinenergy consumption predictionevent backtrackingsustainable operation and maintenance

Identifiers

PMID37177385

DOI10.3390/s23094182

PMCPMC10180953

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.