Evidence map›Paper›PMID 37895741›Full record

ArticleMaterials (Basel, Switzerland)2023

Effective Notch-Stress-Based Stress Concentration Factors of the Rib-Deck Weld in Orthotropic Steel Decks Considering the Effect of Asphalt Surfacing.

Qiudong Wang, Shanchun Shi, Yue Yao, Zhiqiang Wang, Zhongqiu Fu

Open access · goldAbstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. 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
0.3field-weighted citation impact, top 48% of its field
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, 2 citations in OpenAlex.

  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

5 authors at 3 institutions in 2 countries.

Qiudong WangCollege of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.
Shanchun ShiCollege of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.
Yue YaoCollege of Construction Engineering, Jiangsu Open University, Nanjing 210000, China.
Zhiqiang WangCollege of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.
Zhongqiu FuCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China.
Nanjing Forestry University · CNHohai University · CNHong Kong Metropolitan University · HK

Funding

National Natural Science Foundation of China 52308176Natural Science Foundation of Jiangsu Province BK20220422Natural Science Foundation of the Jiangsu Higher Education Institutions of China 22KJB560008
6 · The paper itself

Abstract

Effective notch stress (ENS) approaches have many application prospects in fatigue damage assessments; however, an ENS can only be obtained by conducting complex and time-consuming numerical analyses, deterring many engineers from applying such an approach. In terms of the rib-deck weld in orthotropic steel decks (OSDs), predictive formulae for determining the ENS concentration factors (ENS-based SCFs) have been proposed; however, the effect of asphalt surfacing is not involved, which limits their applications in practical engineering. In the present study, refined finite element (FE) models, including asphalt surfacing, were developed to obtain the ENS-based SCFs which could be applied to practical engineering. Parametric analyses were conducted to investigate the effect of the transverse loading position, the combined effect of the transverse loading position and asphalt surfacing, and the effect of the temperature of the asphalt surfacing. The amplification coefficients (

Indexed as

concentration factoreffective notch stressorthotropic steel deckpredictive formularib–deck weld

Identifiers

PMID37895741
PMCPMC10608632
OpenAlexW4387780529

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.