Evidence map›Paper›PMID 38067735›Full record

ReviewSensors (Basel, Switzerland)2023

A Digital Image Correlation Technique for Laboratory Structural Tests and Applications: A Systematic Literature Review.

Mohammed Abbas Mousa, Mustafasanie M Yussof, Thulfiqar S Hussein, Lateef N Assi, SeyedAli Ghahari

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Mohammed Abbas MousaDepartment of Roads and Transportation, University of Al-Qadisiyah, Al Diwaniyah 58002, Iraq.ORCID 0000-0002-1403-539X
Mustafasanie M YussofDepartment of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia.ORCID 0000-0002-3563-5466
Thulfiqar S HusseinDepartment of Roads and Transportation, University of Al-Qadisiyah, Al Diwaniyah 58002, Iraq.ORCID 0000-0002-1075-2527
Lateef N AssiTerracon Consultants Inc., 9522 E 47th Pl, Tulsa, OK 74145, USA.
SeyedAli GhahariDepartment of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-7969-9859

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital image correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of the contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for the precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the effectiveness of the DIC methodology has been validated as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

Indexed as

digital image correlationfull-scale test designstructural monitoringsurface displacements

Identifiers

PMID38067735
PMCPMC10708567

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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.