Use of automated mineralogy for the quantification of pyrrhotite in concrete aggregates
Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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