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Performance of full-scale 3D-printed concrete walls: Effects of vertical reinforcements and window opening

2025· article· en· W4410196922 on OpenAlexaff
Mohsen Khanverdi, Sreekanta Das

Bibliographic record

VenueEngineering Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsReinforcementWindow (computing)Structural engineeringScale (ratio)EngineeringMaterials scienceGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

The 3D concrete printing is an advancing construction technology with the potential to revolutionize the building industry. This study investigates the structural performance of large-scale 3D-printed walls made with regular concrete containing coarse aggregates. The objectives of this study are to investigate important gaps related to the structural behavior of full-scale loadbearing walls, including the effects of a large window opening and vertical reinforcement. The methodology involved conducting full-scale compression load tests on wall specimens, printed and cured on-site under real-world conditions. These wall specimens included configurations with and without window openings, as well as reinforced and unreinforced walls. Key findings indicate that 3D-printed concrete walls with vertical reinforcement achieved up to a 26 % increase in loadbearing capacity and exhibited energy absorption up to three times greater than unreinforced walls. This study also found that the wall with a large window opening reached approximately 80 % of the loadbearing capacity of the specimen without an opening. The implications of this study provide novel knowledge about the performance of large 3D-printed concrete walls under compressive loads. This information will help in establishing guidelines for 3D printing in construction and building design codes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.002
GPT teacher head0.196
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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