Evidence map›Paper›PMID 41876576›Full record

ArticleScientific reports2026

Data-driven springback prediction of curved metal sections in curtain wall systems.

Weiguang Jiang, Yuyang Zhang, Yuting Liang, Ke Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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.

Weiguang JiangSchool of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan, Hubei, China.
Yuyang ZhangSchool of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan, Hubei, China.
Yuting LiangSchool of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan, Hubei, China.
Ke ChenSchool of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan, Hubei, China. chenkecm@hust.edu.cn.

Funding

National Natural Science Foundation of China 72401103Science and Technology R&D Program of CSCEC International Operations CSCI-2023-Z-5
6 · The paper itself

Abstract

Curtain wall systems (CWS) are essential components of modern architecture, yet their metal sections exhibit high slenderness ratios, complex multi-curvature geometries, and shape specificity. These characteristics make such components prone to springback after machining, adversely affecting dimensional accuracy and construction quality. Reliable springback prediction is critical, but conventional finite element method (FEM)-based approaches are computationally inefficient when applied to large-scale and diverse components. To overcome this limitation, this study proposes a data-driven springback prediction framework for curved metal sections that integrates numerical simulation, data augmentation, and machine learning model. Experimental validation demonstrates that the proposed framework achieves robust prediction performance, with an average coefficient of determination (R

Indexed as

Curtain wall systemsCurved metal sectionsData-driven modelingSpringback prediction

Identifiers

PMID41876576
PMCPMC13168384

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.