Evidence map›Paper›PMID 41398041›Full record

ArticleScientific reports2025

Explainable machine learning model and gene expression programming for predicting reinforced concrete beams moment capacity exposed to fire.

Pouyan Fakharian, Younes Nouri, Arezoo Asaad Samani, Danial Rezazadeh Eidgahee, Mohammad Reza Torabi, Seyed Rohollah Hoseini Vaez

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

6 authors.

Pouyan FakharianInstitute of Research and Development, Duy Tan University, Da Nang, Vietnam.ORCID https://orcid.org/0000-0003-4307-1944
Younes NouriDepartment of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. nouri.younes@stu.um.ac.ir.ORCID https://orcid.org/0000-0002-0446-5185
Arezoo Asaad SamaniDepartment of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran.ORCID https://orcid.org/0000-0003-3284-362X
Danial Rezazadeh EidgaheeDepartment of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.ORCID https://orcid.org/0000-0001-8023-2896
Mohammad Reza TorabiDepartment of Civil Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran.ORCID https://orcid.org/0009-0004-9434-6471
Seyed Rohollah Hoseini VaezDepartment of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran.ORCID https://orcid.org/0000-0002-5574-8345

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, a new formulation for the moment capacity (M

Indexed as

FireGene expression programmingMachine learningMoment capacityReinforced concrete beam

Identifiers

PMID41398041
PMCPMC12816139

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.