Evidence map›Paper›PMID 35161963›Full record

ArticleSensors (Basel, Switzerland)2022

Vehicle Bump Testing Parameters Influencing Modal Identification of Long-Span Segmental Prestressed Concrete Bridges.

Wilson Hernandez, Alvaro Viviescas, Carlos Alberto Riveros-Jerez

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Wilson HernandezSchool of Civil Engineering, Industrial University of Santander, Carrera 27 Calle 9, Bucaramanga 680002, Colombia.
Alvaro ViviescasSchool of Civil Engineering, Industrial University of Santander, Carrera 27 Calle 9, Bucaramanga 680002, Colombia.
Carlos Alberto Riveros-JerezFaculty of Engineering, University of Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia.ORCID 0000-0002-7128-4165

Funding

Industrial University of Santander 2700
6 · The paper itself

Abstract

In-service prestressed concrete box girder bridges have received increasing attention in recent years due to a large number of bridges reaching decades in service. Therefore, the ageing of infrastructure demands the development of robust condition assessment methodologies based on affordable technology such as vehicle-induced vibration tests (VITs) in contrast with more expensive existing technologies such as tests using hammers or shakers. Ambient vibration tests (AVTs) have been widely used worldwide, taking advantage of freely available ambient excitation sources. However, the literature has commonly reported insufficient input energy to excite the structure to obtain satisfactory modal identification results, especially in long-span concrete bridges. On the other hand, the use of forced vibration tests (FVTs) requires more economic resources. This paper presents the results of field measurements at optimally selected locations in VITs consisting of a 32-ton truck and a springboard with a height of 50 mm. AVTs using optimal sensor placement (OSP) provide similar results to VITs without considering OSP locations. Additionally, the VIT/AVT cost ratio is reduced to 2 since a shorter data collection time is achieved within a one-day (8 h) test framework, which minimizes temperature effects, thus leading to improvements in AVT identification results, especially in vertical modes.

Indexed as

ambient vibration testforced vibration testmodal identificationnumerical simulationoptimal sensor placementprestressed concrete box girder bridgevehicle-induced vibration test

Identifiers

PMID35161963
PMCPMC8838569

What OpenQuestion holds

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LicenceCC BY
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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.