Evidence map›Paper›PMID 38900821›Full record

ArticlePloS one2024

Mining LDA topics on construction engineering change risks based on graded evidence.

Lianghai Jin, Chenxi Li, Zhongrong Zhu, Songxiang Zou, Xushu Sun

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lianghai JinHubei Key Laboratory of Hydro-Power Construction and Management, China Three Gorges University, Yichang, Hubei, China.ORCID 0009-0007-5788-4395
Chenxi LiHubei Key Laboratory of Hydro-Power Construction and Management, China Three Gorges University, Yichang, Hubei, China.
Zhongrong ZhuHubei Key Laboratory of Hydro-Power Construction and Management, China Three Gorges University, Yichang, Hubei, China.
Songxiang ZouHubei Key Laboratory of Hydro-Power Construction and Management, China Three Gorges University, Yichang, Hubei, China.
Xushu SunHubei Key Laboratory of Hydro-Power Construction and Management, China Three Gorges University, Yichang, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Engineering change (EC) risk may negatively impact project schedule, cost, quality, and stakeholder satisfaction. However, existing methods for managing EC risk have certain shortcomings in evidence selection and do not adequately consider the quality and reliability of evidence associated with EC risks. Evidence grading plays a crucial role in ensuring the reliability of decisions related to EC risks and can provide essential scientific and reliability support for decision-making. In order to explore the potential risks associated with architectural engineering changes (ECs) and identify the most significant ones, this study proposed a methodology that combines evidence grading theory and Latent Dirichlet Allocation (LDA) topic analysis means. Initially, the evidence-based grading theory served as the creation of a grading table for evidence sources related to EC risk. Specifically, we categorized the evidence sources into three levels based on their credibility. Subsequently, we selected evidence with higher credibility levels for textual analysis, utilizing the LDA topic model. This involved analyzing regulations, industry standards, and judgment documents related to EC, ultimately identifying the themes associated with EC risks. In addition, by combining EC risk topics with relevant literature, we identified factors influencing EC risks. Subsequently, we designed an expert survey questionnaire to determine the key risks and important risk topics associated with potential risks. The results show that by synthesizing information from both Class A and B evidence, a total of five prominent risk themes were identified, namely contract, technology, funds, personnel, and other hazards. Among them, the technical risk has the highest value, so it implies that the risk is the most important, and the key risks are engineering design defects, errors, and omissions.

Indexed as

EngineeringConstruction IndustryData MiningHumansRisk AssessmentSurveys and Questionnaires

Identifiers

PMID38900821
PMCPMC11189256

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LicenceCC BY
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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.