Evidence map›Paper›PMID 42513808›Full record

ArticleMaterials (Basel, Switzerland)2026

A Probabilistic Framework for Finite Strain Damage Response of Thick Curved Beams Including the Shear Effect.

Arian Mohammadkhani, Hamid Shahsavari, Mostafa Baghani

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

3 authors.

Arian MohammadkhaniSchool of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 14399-57131, Iran.ORCID 0009-0004-9591-0003
Hamid ShahsavariDepartment of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.ORCID 0000-0002-0929-5533
Mostafa BaghaniSchool of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 14399-57131, Iran.ORCID 0000-0001-6695-3128

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Curved beams are widely used in engineering applications due to their unique geometry and mechanical advantages. In this study, the mechanical response of hyperelastic curved beams under cyclic loading is investigated, considering the Mullins stress-softening effect and the probabilistic variability of material parameters. A Neo-Hookean hyperelastic model integrated with the Ogden-Roxburgh damage formulation is employed to capture the cyclic stress-softening behavior. The governing partial differential equations are derived in cylindrical coordinates under plane-stress conditions and solved numerically. Unlike classical pure bending assumptions, the present formulation captures the coupled radial, circumferential, and shear stress components arising from the finite thickness and curvature of the beam. The model is validated through comparison with two-dimensional finite element solutions. The influence of material uncertainty is further examined via a probabilistic parametric analysis, and statistical measures including mean, standard deviation, skewness, and kurtosis are evaluated. The results demonstrate that Mullins-induced stress softening is concentrated near the inner curvature and beam root, and that inherent material variability significantly affects the damage distribution. The proposed framework provides a fast and accurate method for predicting the mechanical behavior of hyperelastic curved beams under cyclic loading.

Indexed as

curved beamfinite element methodhyperelasticitymullins effectnonlinear solid mechanicprobabilistic analysis

Identifiers

PMID42513808
PMCPMC13413442

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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.