Evidence map›Paper›PMID 40251350›Full record

ArticleScientific reports2025

Unraveling the evolutionary patterns of construction accidents: a risk assessment framework based on average mutual information theory.

Jian Liu, Hanqiang Tang, Rui Feng

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jian LiuSchool of Resource and Safety Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Hanqiang TangSchool of Resource and Safety Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Rui FengResearch Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing, 100083, China. fengr@ustb.edu.cn.

Funding

Fundamental Research Funds for the Central Universities FRF-TP-22-120A1National Key R&D Program of China 2017YFC0804901National Science Foundation of China 52004139
6 · The paper itself

Abstract

Studies of the evolutionary law of construction accidents and the formulation of effective risk assessments are crucial for ensuring construction safety and reducing accidents. However, existing research often focuses exclusively on one of the four critical perspectives: human, facilities, environment, and management, with limited systematic theoretical analysis. Current risk assessments typically emphasized the impact of risks at specific points in time, neglecting the cumulative effects of risks over extended periods. This study has explored how hazardous and harmful factors affect the evolution of accidents, then has established a risk assessment model utilizing average mutual information theory. The findings indicate that "Command violations", "Command errors", and "Illegal operations" are the primary direct causes, while "Inadequate occupational safety and health management structure and staffing" and "Inadequate or unimplemented occupational health management system" are key management factors. In addition, it also provides practical guidance for construction enterprises to strengthen safety management from aspects such as safety production investment and safety management system integration. These contributions are expected to significantly improve the safety level of construction projects and reduce the occurrence of accidents.

Indexed as

Accident evolutionConstruction safetyHazardous and harmful factorsMutual information theoryRisk assessment

Identifiers

PMID40251350
PMCPMC12008309

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.