Evidence map›Paper›PMID 41598059›Full record

ArticleMaterials (Basel, Switzerland)2026

Multivariate Machine Learning Framework for Predicting Electrical Resistivity of Concrete Using Degree of Saturation and Pore-Structure Parameters.

Youngdae Kim, Seong-Hoon Kee, Cris Edward F Monjardin, Kevin Paolo V Robles

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2026. 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

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

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

4 authors.

Youngdae KimSchool of Civil, Environmental and Geological Engineering, Mapua University, Manila 1102, Philippines.
Seong-Hoon KeeDepartment of ICT integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Republic of Korea.ORCID 0000-0002-7743-4881
Cris Edward F MonjardinSchool of Civil, Environmental and Geological Engineering, Mapua University, Manila 1102, Philippines.ORCID 0000-0003-3688-4402
Kevin Paolo V RoblesSchool of Civil, Environmental and Geological Engineering, Mapua University, Manila 1102, Philippines.ORCID 0000-0002-4304-1326

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the relationship between apparent electrical resistivity (ER) and key material parameters governing moisture and pore-structure characteristics of concrete. An experimental program was conducted using six concrete mix designs, where ER was continuously measured under controlled wetting and drying cycles to characterize its dependence on the degree of saturation (DS). Results confirmed that ER decreases exponentially with increasing DS across all mixtures, with R

Indexed as

concretedegree of saturationelectrical resistivitymachine learningmultivariate regression

Identifiers

PMID41598059
PMCPMC12843180

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.