Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network

Chen Zhaorong; Deng Xiaoji; Cai Zhili; Zeng Changluo; Zhang Guoshen

doi:doi:10.11648/j.sd.20221006.32

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Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network

Chen Zhaorong, Deng Xiaoji, Cai Zhili, Zeng Changluo, Zhang Guoshen

Published in Science Discovery (Volume 10, Issue 6)

Received: 21 November 2022 Accepted: 22 December 2022 Published: 28 December 2022

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Abstract

The unfinished building project has seriously affected the overall planning image of the city and occupied the city's very valuable land and social resources, its reasonable renewal of waste into treasure is particularly important. In order to quantitatively study the main risk sources that affect the construction schedule of the unfinished building, this paper adopts the dynamic Bayesian network method, a Quantitative analysis of progress risk identification was conducted for high-level projects that had been under construction for up to eight years. The influencing factors of the schedule risk of the project are discussed. This paper introduces the principle of dynamic Bayesian networks, discusses the main analysis basis of Bayesian networks, and summarizes the main analysis steps of Bayesian networks. Through backward reasoning, the paper determined the biggest possible factors and the most likely cause chain for the delay of the reconstruction schedule of multi-high-rise old buildings. Incomplete project handover data, the degree of interest consultation with residents before the reconstruction, unreasonable construction organization, and the change of regulations and policies would be the main risks affecting the progress of the renewal project.

Published in	Science Discovery (Volume 10, Issue 6)
DOI	10.11648/j.sd.20221006.32
Page(s)	522-527
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Uncompleted Building Project, Renovation Project, Risk Identification, Bayesian Network

References

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APA Style

Chen Zhaorong, Deng Xiaoji, Cai Zhili, Zeng Changluo, Zhang Guoshen. (2022). Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Science Discovery, 10(6), 522-527. https://doi.org/10.11648/j.sd.20221006.32

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ACS Style

Chen Zhaorong; Deng Xiaoji; Cai Zhili; Zeng Changluo; Zhang Guoshen. Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Sci. Discov. 2022, 10(6), 522-527. doi: 10.11648/j.sd.20221006.32

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AMA Style

Chen Zhaorong, Deng Xiaoji, Cai Zhili, Zeng Changluo, Zhang Guoshen. Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Sci Discov. 2022;10(6):522-527. doi: 10.11648/j.sd.20221006.32

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@article{10.11648/j.sd.20221006.32,
  author = {Chen Zhaorong and Deng Xiaoji and Cai Zhili and Zeng Changluo and Zhang Guoshen},
  title = {Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {522-527},
  doi = {10.11648/j.sd.20221006.32},
  url = {https://doi.org/10.11648/j.sd.20221006.32},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.32},
  abstract = {The unfinished building project has seriously affected the overall planning image of the city and occupied the city's very valuable land and social resources, its reasonable renewal of waste into treasure is particularly important. In order to quantitatively study the main risk sources that affect the construction schedule of the unfinished building, this paper adopts the dynamic Bayesian network method, a Quantitative analysis of progress risk identification was conducted for high-level projects that had been under construction for up to eight years. The influencing factors of the schedule risk of the project are discussed. This paper introduces the principle of dynamic Bayesian networks, discusses the main analysis basis of Bayesian networks, and summarizes the main analysis steps of Bayesian networks. Through backward reasoning, the paper determined the biggest possible factors and the most likely cause chain for the delay of the reconstruction schedule of multi-high-rise old buildings. Incomplete project handover data, the degree of interest consultation with residents before the reconstruction, unreasonable construction organization, and the change of regulations and policies would be the main risks affecting the progress of the renewal project.},
 year = {2022}
}

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TY  - JOUR
T1  - Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network
AU  - Chen Zhaorong
AU  - Deng Xiaoji
AU  - Cai Zhili
AU  - Zeng Changluo
AU  - Zhang Guoshen
Y1  - 2022/12/28
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.32
DO  - 10.11648/j.sd.20221006.32
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 522
EP  - 527
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.32
AB  - The unfinished building project has seriously affected the overall planning image of the city and occupied the city's very valuable land and social resources, its reasonable renewal of waste into treasure is particularly important. In order to quantitatively study the main risk sources that affect the construction schedule of the unfinished building, this paper adopts the dynamic Bayesian network method, a Quantitative analysis of progress risk identification was conducted for high-level projects that had been under construction for up to eight years. The influencing factors of the schedule risk of the project are discussed. This paper introduces the principle of dynamic Bayesian networks, discusses the main analysis basis of Bayesian networks, and summarizes the main analysis steps of Bayesian networks. Through backward reasoning, the paper determined the biggest possible factors and the most likely cause chain for the delay of the reconstruction schedule of multi-high-rise old buildings. Incomplete project handover data, the degree of interest consultation with residents before the reconstruction, unreasonable construction organization, and the change of regulations and policies would be the main risks affecting the progress of the renewal project.
VL  - 10
IS  - 6
ER  -

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Author Information

Chen Zhaorong

China Construction Fifth Engineering Bureau the Third Construction Co., Ltd., Changsha, China
Deng Xiaoji

Public Works Buruau of Shenzhen Nanshan, Shenzhen, China
Cai Zhili

China Construction Fifth Engineering Bureau the Third Construction Co., Ltd., Changsha, China
Zeng Changluo

China Construction Fifth Engineering Bureau the Third Construction Co., Ltd., Changsha, China
Zhang Guoshen

China Construction Fifth Engineering Bureau the Third Construction Co., Ltd., Changsha, China

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Plain Text BibTeX RIS

APA Style

Chen Zhaorong, Deng Xiaoji, Cai Zhili, Zeng Changluo, Zhang Guoshen. (2022). Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Science Discovery, 10(6), 522-527. https://doi.org/10.11648/j.sd.20221006.32

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ACS Style

Chen Zhaorong; Deng Xiaoji; Cai Zhili; Zeng Changluo; Zhang Guoshen. Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Sci. Discov. 2022, 10(6), 522-527. doi: 10.11648/j.sd.20221006.32

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AMA Style

Chen Zhaorong, Deng Xiaoji, Cai Zhili, Zeng Changluo, Zhang Guoshen. Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network. Sci Discov. 2022;10(6):522-527. doi: 10.11648/j.sd.20221006.32

Copy | Download

@article{10.11648/j.sd.20221006.32,
  author = {Chen Zhaorong and Deng Xiaoji and Cai Zhili and Zeng Changluo and Zhang Guoshen},
  title = {Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network},
  journal = {Science Discovery},
  volume = {10},
  number = {6},
  pages = {522-527},
  doi = {10.11648/j.sd.20221006.32},
  url = {https://doi.org/10.11648/j.sd.20221006.32},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20221006.32},
  abstract = {The unfinished building project has seriously affected the overall planning image of the city and occupied the city's very valuable land and social resources, its reasonable renewal of waste into treasure is particularly important. In order to quantitatively study the main risk sources that affect the construction schedule of the unfinished building, this paper adopts the dynamic Bayesian network method, a Quantitative analysis of progress risk identification was conducted for high-level projects that had been under construction for up to eight years. The influencing factors of the schedule risk of the project are discussed. This paper introduces the principle of dynamic Bayesian networks, discusses the main analysis basis of Bayesian networks, and summarizes the main analysis steps of Bayesian networks. Through backward reasoning, the paper determined the biggest possible factors and the most likely cause chain for the delay of the reconstruction schedule of multi-high-rise old buildings. Incomplete project handover data, the degree of interest consultation with residents before the reconstruction, unreasonable construction organization, and the change of regulations and policies would be the main risks affecting the progress of the renewal project.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Progress Risk Identification of Suspended High-Rise Buildings Renewal Based on Dynamic Bayesian Network
AU  - Chen Zhaorong
AU  - Deng Xiaoji
AU  - Cai Zhili
AU  - Zeng Changluo
AU  - Zhang Guoshen
Y1  - 2022/12/28
PY  - 2022
N1  - https://doi.org/10.11648/j.sd.20221006.32
DO  - 10.11648/j.sd.20221006.32
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 522
EP  - 527
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20221006.32
AB  - The unfinished building project has seriously affected the overall planning image of the city and occupied the city's very valuable land and social resources, its reasonable renewal of waste into treasure is particularly important. In order to quantitatively study the main risk sources that affect the construction schedule of the unfinished building, this paper adopts the dynamic Bayesian network method, a Quantitative analysis of progress risk identification was conducted for high-level projects that had been under construction for up to eight years. The influencing factors of the schedule risk of the project are discussed. This paper introduces the principle of dynamic Bayesian networks, discusses the main analysis basis of Bayesian networks, and summarizes the main analysis steps of Bayesian networks. Through backward reasoning, the paper determined the biggest possible factors and the most likely cause chain for the delay of the reconstruction schedule of multi-high-rise old buildings. Incomplete project handover data, the degree of interest consultation with residents before the reconstruction, unreasonable construction organization, and the change of regulations and policies would be the main risks affecting the progress of the renewal project.
VL  - 10
IS  - 6
ER  -

Copy | Download