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Record W4392585592 · doi:10.5194/egusphere-egu24-4169

Use of automated mineralogy for the quantification of pyrrhotite in concrete aggregates

2024· preprint· en· W4392585592 on OpenAlexaff
Bruno Guimarães Titon, Josée Duchesne, Benoît Fournier

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversité LavalGeological Survey of Canada
Fundersnot available
KeywordsPyrrhotiteGeologyMineralogyGeochemistryPyrite

Abstract

fetched live from OpenAlex

Concrete is one of the most sought-after materials in our time, second only to water in importance to developed and emerging nations alike. It has been a crucial component of major infrastructure projects for thousands of years. In the year 2022 alone, China, the largest producer of concrete, manufactured approximately 2.1 million metric tons of cement, a key ingredient in modern concrete formulations. Various concrete mixes, tailored for specific applications, have been perfected and are widely used in the industry. In broad terms, concrete consists of about three-fifths sand and rock fragments (aggregates), one-fifth cement, and one-fifth water. This combination makes it the world's most widely used building material, particularly for large structures which are reinforced with steel rods. Aggregates can be sourced from crushed rock or naturally occurring sand and gravel, but the type of aggregate used significantly influences the overall properties and robustness of the final product. The mineralogical composition of the rock itself is closely related to the quality and resilience of the concrete. Understanding the mineralogy and chemistry of the lithotypes used allows for predicting their interaction with other concrete constituents, preventing undesirable chemical reactions. Such reactions can compromise the safety and resilience of the finished product, and ultimately prevent the use of materials that would otherwise perform poorly. Among the many concrete pathologies that may arise from the use of inadequate raw materials, internal sulfate attack (ISA) mainly occurs when sulfide-bearing aggregates are used. This leads to complex chemical reactions resulting in the oxidation of sulfide phases and the release of sulfur into the cement paste. Consequently, severe cracking and internal swelling significantly compromise concrete integrity. The European standard (EN 12620:2008) is a widely used guideline that recommends a total sulfur content in aggregates not exceeding 1.0 wt.%. This threshold is reduced to 0.1 wt.% when pyrrhotite is detected in the lithotype to be used as an aggregate. Given that pyrrhotite is the second most common sulfide found in nature, it is crucial to identify and quantify the various sulfide minerals that may be present and to understand how they can affect finished concrete structures. The use of automated mineralogy, represented by software systems such as QEMSCAN, emerges as a powerful solution for identifying and quantifying pyrrhotite and any other sulfide phases in aggregates for concrete. It is a scanning electron microscope (SEM)-based system that uses backscattered electron (BSE) image segmentation and simultaneous acquisition of energy-dispersive X-ray (EDS) spectra to classify mineral phases using a pre-defined list of mineral spectra. Preliminary results from QEMSCAN analysis of both thin sections and mounts comprised of ground rock of different grain sizes have been promising. This SEM-based system can analyze whole rock samples and crushed aggregates, providing accurate results over different size intervals. Despite the expected negative correlation between sample grain size and pyrrhotite content, the results vary from 2.71 to 5.59 wt.% for total pyrrhotite content in the analyzed samples. Moreover, when averaged over all grain sizes, these results align with those obtained through traditional optical petrography.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.058
GPT teacher head0.288
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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