Advancing Construction 3D Printing with Predictive Interlayer Bonding Strength: A Stacking Model Paradigm.

Dinglue Wu, Qiling Luo, Wujian Long, Shunxian Zhang, Songyuan Geng

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

A Prediction Model of Interlayer Bond Strength for 3D-Printed Concrete Considering Printing Interval and Environmental Effects.Materials (Basel, Switzerland) · 2026
Article

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

5 authors.

Dinglue WuPoly Changda Engineering Co., Ltd., Guangzhou 510620, China.

Qiling LuoCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.

Wujian LongCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-4760-0009

Shunxian ZhangPoly Changda Engineering Co., Ltd., Guangzhou 510620, China.

Songyuan GengCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.

Funding

Guangdong Basic and Applied Basic Research Foundation 2023A1515012136

6 · The paper itself

Abstract

To enhance the quality stability of 3D printing concrete, this study introduces a novel machine learning (ML) model based on a stacking strategy for the first time. The model aims to predict the interlayer bonding strength (IBS) of 3D printing concrete. The base models incorporate SVR, KNN, and GPR, and subsequently, these models are stacked to create a robust stacking model. Results from 10-fold cross-validation and statistical performance evaluations reveal that, compared to the base models, the stacking model exhibits superior performance in predicting the IBS of 3D printing concrete, with the

Indexed as

3D printing concreteintelligent predictioninterlayer bonding strengthmachine learningstacking strategy

Identifiers

PMID38473505

DOI10.3390/ma17051033

PMCPMC10935051

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.