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Record W4244947500 · doi:10.32920/ryerson.14665752

Self-healing cementitious composites incorporating innovative materials

2021· preprint· en· W4244947500 on OpenAlex
Mohamed A. A. Sherir

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSelf-healingMaterials scienceDurabilityShrinkageComposite materialComposite numberCementCementitiousCuring (chemistry)ExpansiveCompressive strength

Abstract

fetched live from OpenAlex

Discovering new cement based materials characterized by higher durability and longer service life is crucial for sustainable infrastructure. Engineered Cementitious Composite (ECC) with high potential of micro-crack healing can enhance ductility/durability of concrete structures. However, low water-to-cement ratio in ECC matrix in addition to the lack of cementitious properties of newly formed healing-products in micro-cracks could be an obstacle to implementing continuous hydration to stimulate self-healing and stop re-opening of old healed micro-cracks during re-loading. MgO-type expansive agent (MEA), widely used to compensate effectively autogenous shrinkage in mass concrete, can be used to eliminate such obstacles and to induce self-healing. This research studied self-healing capability of MEA in ECC through an extensive experimental investigation and developed/proposed an ECC-MgO system. Test results indicated “900°C-2 hours of holding time-45 μm particle size” as the best calcination system based on higher MEA hydration in powder state. Additionally, 5% lightly burnt MgO combined with low-calcium Class-F-fly ash (as 55% cement replacement) was found to be a better choice in designing self-healing-ECC-MgO system in terms of lower expansion effect of MEA. Further, self-healing property of ECC-MgO system under different environmental exposures (laboratory/water/natural(field)/autoclave curing) was investigated based on mechanical/durability properties of control/pre-cracked specimens compared with their ECC counterparts. The ECC-MgO system exhibited remarkable self-healing property in multiple-cracked/damaged specimens when test results were analyzed based on development/recovery of compressive/flexural strength/load resistance, ultrasonic pulse velocity, heat of hydration, expansion/drying shrinkage, rapid chloride permeability, sorptivity/water absorption and freeze/thaw resistance in addition to crack healing/cracking characteristics and microstructural characterizations through Thermogravimetric Analysis (TGA)/Differential Scanning Calorimetry (DSC) and Scanning Electron Microscopy (SEM). Finally, the self-healing ability of ECC-MgO system was evaluated through structural performance (regarding strength/stiffness/energy absorbing/crack-healing capacity recovery) of damaged and subsequently healed water/field cured link slabs used for joint-free bridge deck construction. Superior self-healing ability of ECC-MgO system was attributed to low MEA water demand coupled with its delayed hydration characteristics which lead to the formation of more cohesive/strong cementitious MgO crystals within crack walls (at later ages) supplementing CaCO3 precipitations (formed earlier) leading to more effective crack-healing and developing post-healed cracks at new locations upon reloading

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.

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), Insufficient payload (model declined to judge)
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 score0.306
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 science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.001

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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