Evidence map›Paper›PMID 40508442›Full record

ArticleMaterials (Basel, Switzerland)2025

Data-Driven Approach to Derive Equation for Predicting Ultimate Shear Strength of Reinforced Concrete Beams Without Stirrups.

Menghay Phoeuk, Dong-Yeong Choi, Suchart Limkatanyu, Minho Kwon

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

Menghay PhoeukDepartment of Civil Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0003-1542-1126
Dong-Yeong ChoiDepartment of Civil Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.
Suchart LimkatanyuDepartment of Civil Engineering, Prince of Songkla University, Songkhla 90112, Thailand.ORCID 0000-0002-4343-7654
Minho KwonDepartment of Civil Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0003-0754-285X

Funding

Ministry of Trade, Industry, and Energy (MOTIE) RS-2024-00468763
6 · The paper itself

Abstract

Shear failure in reinforced concrete (RC) beams is abrupt and brittle, occurs without warning, and leaves no opportunity for internal stress redistribution. Despite the critical need for accurate shear strength assessment, existing methods vary widely across regions, leading to inconsistencies in practice. This study presents a unified shear strength equation for non-prestressed rectangular RC beams without stirrups, developed for simplicity and broad applicability. The model requires only basic geometric and material properties and applies to both shear-slender and non-shear-slender beams. It was formulated using a data-driven approach that combines an extensive experimental database collected up to 2007 with advanced computational techniques, including Artificial Neural Networks, Generative Adversarial Networks, and Bayesian optimization. The proposed equation was evaluated against established shear provisions, such as ACI 318-25 and CSA A23.3-24, and benchmarked with an experimental database. The results show that the model improves prediction accuracy, reduces uncertainty, and provides a more consistent method for shear strength assessment. The robustness of the equation was further confirmed through additional experimental database gathered after 2007, demonstrating strong agreement with test results and lower prediction uncertainty than current code provisions. These findings support the potential adoption of the proposed equation in engineering practice.

Indexed as

artificial neural networksBayesian optimizationdata-driven approachshear strengthsynthetic data

Identifiers

PMID40508442
PMCPMC12155873

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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.