Evidence map›Paper›PMID 32492976›Full record

ArticleInternational journal of environmental research and public health2020

A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project.

Xiaoyan Jiang, Sai Wang, Jie Wang, Sainan Lyu, Martin Skitmore

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Xiaoyan JiangSchool of Civil Engineering, Hefei University of Technology, Hefei 230009, China.ORCID 0000-0003-0954-5843
Sai WangSchool of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
Jie WangSchool of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
Sainan LyuSchool of Property, Construction and Project Management, RMIT University, Melbourne City Campus, Melbourne, VIC 3000, Australia.
Martin SkitmoreSchool of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, QLD 4001, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.

Indexed as

RailroadsSafety ManagementAlgorithmsProblem SolvingRisk ManagementCBRcorrelation algorithmontologysafety risksimilarity algorithmsubway

Identifiers

PMID32492976
PMCPMC7312838

What OpenQuestion holds

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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.