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Record W4409799927 · doi:10.11159/icsect25.140

Sulfate and Acid Attack Resistance of Iron Slag and Recycled CoarseAggregate Concrete

2025· article· en· W4409799927 on OpenAlexvenueno aff
Gurpreet Singh, Navdeep Singh

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
FundersDr B R Ambedkar National Institute of Technology Jalandhar
KeywordsAggregate (composite)SulfateSlag (welding)Materials scienceMetallurgyWaste managementComposite materialEngineering

Abstract

fetched live from OpenAlex

This study specifically explores the impact of mutual inclusion of 'iron slag (IS) and recycled concrete aggregates (RCA)' in non-traditional concrete (NTC) aiming to estimate the resistance against sulfate and acid attacks.The newness of this experimental study is in exploring of role of IS along with RCA in NTC for abovementioned durability properties, which have been comparatively underexplored.Natural sand (NS) and natural coarse aggregates (NA) were replaced with IS and RCA at constant (30%) and varying (25%-100%) amount respectively.In all, six (6) numbers of NTC were tested for sulfate and acid attack resistance while compressive and tensile tests were performed in general support till 90 days of standard curing.The resistance against sulfate and acid attack was measured in relation to variation of mass and corresponding strength performance of designed NTC.The highest increase in mass (by 6%) was noted for NTC with 30% of IS and 100% of RCA.Likewise, for the same NTC the reduction in mass due to acid resistance was limited to 17%.While NTC with IS (IS30RCA0) only and 25% of RCA (IS30RCA25) emerged as equivalent performers in terms of resistance and strength outcomes.These findings not only demonstrate a positive potential of mutual inclusion of IS and RCA in NTC but also present a promising start towards utilization of sustainable construction materials.

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 categoriesMeta-epidemiology (narrow)
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 score0.190
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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.

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 venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicRecycled Aggregate Concrete PerformanceFrench-language works237,207