Evidence map›Paper›PMID 42738059›Full record

ArticleMaterials (Basel, Switzerland)2026

Upcycling of Decommissioned Wind Turbine Blades: An Investigation of Stress Distributions in Glass Fiber-Reinforced Polymers Beams.

Changlang Wu, Jacob Wittrup Schmidt, Dario Parigi

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.

Changlang WuDepartment of Built Environment, Aalborg University, 9220 Aalborg, Denmark.ORCID 0000-0002-0288-743X
Jacob Wittrup SchmidtDepartment of Built Environment, Aalborg University, 9220 Aalborg, Denmark.
Dario ParigiDepartment of Built Environment, Aalborg University, 9220 Aalborg, Denmark.

Funding

The Villum Foundation VIL78951
6 · The paper itself

Abstract

Wind turbines are among the most widely adopted renewable energy systems, yet the end-of-life management of wind turbine blades remains a major challenge. The turbine blades are primarily made of glass fiber-reinforced polymers (GFRP), which are difficult to recycle without degrading their inherent structural integrity. Instead of shredding or downcycling, this study explores a reuse strategy that preserves intact laminate from decommissioned blades and repurposes them into structural beams via glue-laminated fiber-reinforced polymers (GL-FRP) assemblies. This concept was implemented using commercial GFRP for blade-derived laminates, focusing on the structural feasibility rather than material sourcing. Adhesive joints were designed as the key enabling mechanism for structural reuse and characterized under tensile loading. Building on this, the flexural performance of GL-FRP beams was investigated in horizontal and vertical configurations, yielding an ultimate load-bearing capacity of 8 kN and 20 kN, respectively. Furthermore, stress distributions obtained from experiments, finite element (FE) simulations, and analytical frameworks were evaluated and compared. The results highlight the governing role of interlaminar stress in determining structural performance and failure of the horizontal beam. The findings provide a foundational understanding of repurposing decommissioned wind turbine blades as structural elements, offering a novel strategy for material upcycling in wind energy sector and contributing to the development of circular construction solutions.

Indexed as

decommissioned wind turbine bladesGFRP compositeGL-FRP beamstress distributionupcycling

Identifiers

PMID42738059
PMCPMC13566841

What OpenQuestion holds

Textmetadata
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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.