Evidence map›Paper›PMID 40745457›Full record

ArticleScientific reports2025

Systemic and dynamic risk analysis of drilling construction based on bayesian network and system dynamics model.

Jinhao Zhang, Lirong Wu, Tao Zhang, Dun Wu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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
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0citing papers in PubMed
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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

4 authors.

Jinhao ZhangShandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Mineral, Shandong University of Science and Technology, Qingdao, 266590, China.
Lirong WuShandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Mineral, Shandong University of Science and Technology, Qingdao, 266590, China. skd992994@sdust.edu.cn.
Tao ZhangShandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Mineral, Shandong University of Science and Technology, Qingdao, 266590, China.
Dun WuAnhui Institute of Intelligent Underground Detection Technology, Anhui Jianzhu University, Hefei, 230601, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drilling construction is a key means to obtain underground resources and information, and construction risk also directly affects the safety, economy and sustainability of engineering projects. Aiming at the limitations of traditional risk assessment methods in the dynamic and systematic aspects, this paper proposes a comprehensive analysis method combining Bayesian network (BN) and system dynamics (SD). First, through expert interviews and field investigations, an evaluation system consisting of 5 dimensions and 20 indicators was established, and the combination of entropy weight method and expert score was used to empower the system. Then the three-layer Bayesian network was constructed by GeNIe, and the system dynamics model with 5 feedback loops was established by Vensim-PLE to realize the static and dynamic coupling analysis of risk factors. The study found that equipment integrity (59%), geological conditions (55%) and operational skills (51%) were the leading risk factors. Finally, the model is applied to the actual scene, and the results are in agreement with the actual situation. This study provides a theoretical framework of probability prediction and process optimization for the systematic analysis of complex drilling construction, and provides a more comprehensive risk analysis for drilling safety under complex geological conditions.

Indexed as

Bayesian networkDrilling constructionEntropy weight method (EWM)Risk analysisSystem dynamics

Identifiers

PMID40745457
PMCPMC12313943

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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.