Evidence map›Paper›PMID 41597974›Full record

ArticleMaterials (Basel, Switzerland)2026

Study on the Tensile Properties and Influencing Factors of Superelastic SMAF-Reinforced PP/PVA-ECC Materials.

Yan Cao, Xiaolong Qi, Zhao Yang

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.

Yan CaoCollege of Creative Design, Wuchang University of Technology, Wuhan 430223, China.
Xiaolong QiSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.
Zhao YangSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.ORCID 0000-0003-2176-3472

Funding

The Hubei Provincial Construction Science and Technology Program JK2025054The Hubei Provincial Construction Science and Technology Program JK2025074The National Natural Science Fundation of China 52178158The National Undergraduate Innovation and Entrepreneurship Training Program 202410488007
6 · The paper itself

Abstract

To develop a cost-effective shape memory alloy fiber-reinforced engineered cementitious composite (SMAF-ECC) with excellent mechanical properties, polypropylene (PP) fibers were used to partially replace polyvinyl alcohol (PVA) fibers to prepare the ECC matrix, and superelastic shape memory alloy fibers (SMAFs) were incorporated to fabricate a novel SMAF-ECC. Uniaxial tensile tests were systematically performed to characterize the tensile mechanical properties of the composites, focusing on the effects of SMAF volume content and diameter. The results indicate that the optimal base ECC mix proportion is 0.8 vol.% PP fibers and 1.2 vol.% PVA fibers, achieving an ultimate tensile strain of 4.88% (only a 4.69% reduction compared to pure PVA-ECC) while significantly reducing material cost without sacrificing superior ductility. SMAF volume content and diameter notably influence the tensile performance of SMAF-ECC, with the specimen containing 0.2 mm diameter SMAFs at 0.2 vol.% exhibiting the best performance: initial cracking stress, ultimate tensile stress, and ultimate tensile strain are enhanced by 16.79%, 20.85%, and 2.87%, respectively, compared to pure ECC. This study provides a theoretical basis and parametric guidance for the engineering popularization and application of cost-effective SMAF-ECCs.

Indexed as

PP fibersPVA fibersSMAFSMAF-ECCTensile property

Identifiers

PMID41597974
PMCPMC12842741

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.