MétaCan
Menu
Back to cohort
Record W7117487529 · doi:10.1061/jsdccc.sceng-1951

Low-Cement Engineered Cementitious Composites: Application in Bridge Link Slabs

2025· article· en· W7117487529 on OpenAlexaff
Erfan Zandi Lak, Fatemeh Valikhah, Sreekanta Das, Emad Booya

Bibliographic record

VenueJournal of structural design and construction practice. · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDurabilityPortland cementCementitiousLink (geometry)Expansion jointGround granulated blast-furnace slagCementBridge (graph theory)

Abstract

fetched live from OpenAlex

One effective method for mitigating corrosion issues stemming from expansion joints in bridge decks is to replace expansion joints with link slabs. New link slabs are being constructed with fiber-reinforced concrete (FRC). Engineered cementitious composite (ECC) is one of the FRC materials that offer better deformability and higher durability in terms of controlling crack width for link slabs. However, the total amount of Portland cement (PC) used in ECC is much higher than that in the normal concrete, and, thus, ECC is not considered an environmentally friendly construction material. Hence, the objective of this study is to develop novel link slabs using greener ECCs that require substantially less PC and possibly use waste material such as fly ash (FA) or blast furnace slag (BFS) as an alternative binder. Different ECC mixtures were studied with substantial substitution of PC with FA or BFS, developing low-cement ECCs. A sustainability analysis was conducted on these materials, and they were used to construct link slabs to evaluate their performance in reinforced members. This study found that the embodied CO2 in low-cement ECCs reduced by up to 54% when compared with conventional ECC. This study also found that the low-cement ECC link slabs offer higher deformability and similar or higher durability compared to the control ECC link slab.

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: none
Teacher disagreement score0.548
Threshold uncertainty score0.598

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 communication0.0000.001
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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of structural design and construction practice.Same topicInnovative concrete reinforcement materialsFrench-language works237,207