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Record W4411025022 · doi:10.1016/j.autcon.2025.106311

Advancing polymer composites in civil infrastructure through 3D printing

2025· article· en· W4411025022 on OpenAlexaff
Sachini Wickramasinghe, Allan Manalo, Omar Alajarmeh, Charles Dean Sorbello, Senarath Weerakoon, Tuan Ngo, Brahim Benmokrane

Bibliographic record

VenueAutomation in Construction · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversité de Sherbrooke
FundersUniversity of Southern Queensland
Keywords3D printingCivil infrastructureComposite materialMaterials sciencePolymerEngineeringCivil engineeringConstruction engineering

Abstract

fetched live from OpenAlex

Polymer composites (PCs) are increasingly used in civil infrastructure and construction due to their high strength, lightweight properties, and durability. When combined with automated manufacturing technologies such as 3D printing, they enable the efficient fabrication of complex engineering structures while minimizing material waste. As construction moves toward sustainable, automated, and digitally driven methods, understanding the potential of 3D-printed polymer composites becomes essential. This review addresses this emerging need by offering a civil-infrastructure specific synthesis of polymer composites in additive manufacturing as a pathway to a more sustainable and resilient built environment. The paper presents a comprehensive overview of the use of PCs in automated construction, highlighting key 3D printing techniques and printable polymer-based materials applied in building and infrastructure projects. It also discusses recent developments, current challenges, and emerging opportunities in PC-based 3D printing for civil engineering, supported by case studies, innovative construction methodologies, and future research directions.

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 score0.225
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.225
Teacher spread0.222 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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