Evidence map›Paper›PMID 35692310›Full record

ArticleFrontiers in public health2022

Tracing the Trends of General Construction and Demolition Waste Research Using LDA Modeling Combined With Topic Intensity.

Zezhou Wu, Peiying Xie, Jinming Zhang, Baojian Zhan, Qiufeng He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2022. 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
0cells of the map it votes in
0citing 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

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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

5 authors.

Zezhou WuCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China.
Peiying XieCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China.
Jinming ZhangSchool of Political Studies, Nanjing Agricultural University, Nanjing, China.
Baojian ZhanCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China.
Qiufeng HeCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of construction and demolition waste (CDW) has attracted more and more attentions with the increasing CDW pollution caused by the large-scale infrastructure construction. This study used the Latent Dirichlet Allocation (LDA) combined with topic intensity to discover hot topics and development trends in the study area of CDW. First, the LDA was used for topic modeling to extract the existing topics from textual data. Second, the topic intensity was calculated for the extracted topics and the numerical values of the topic intensity represented the popularity of the topics. In this study, 4 topics were extracted from 1,849 relevant articles through the LDA modeling and topic intensity calculation. The results showed that the topic of "CDW management" had an upward trend. Topics such as "recycled aggregate," "environmental impact," and "study of CDW on soil" all showed a downward trend. The methods of this study can dig into the topics of CDW study and help scholars to engage in this field for better understanding the prevalence and evolution trends of these topics.

Indexed as

Construction IndustryWaste ManagementConstruction MaterialsEnvironmentRecyclingconstruction and demolition wastelinear discriminant analysisresearch trendtopic intensitytopic modeling

Identifiers

PMID35692310
PMCPMC9174565

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.