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Record W4390175576 · doi:10.3390/ma17010082

Mechanical, Electrical, and Tensile Self-Sensing Properties of Ultra-High-Performance Concrete Enhanced with Sugarcane Bagasse Ash

2023· article· en· W4390175576 on OpenAlexaff
Jinkang Lian, Yulin Wang, Tengfei Fu, Said M. Easa, Yan Zhou, Huawei Li

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthComposite materialFlexural strengthCementCementitiousCompressive strengthBagassePulp and paper industry

Abstract

fetched live from OpenAlex

Although sugarcane bagasse ash (SCBA) possesses favorable cementitious properties, previous research has primarily focused on improving the mechanical performance of conventional concrete- or cement-based composites. Limited attention has been given to ultra-high-performance concrete (UHPC) with SCBA, especially regarding its tensile -sensing properties. This study aimed to comprehensively evaluate the effect of SCBA on the mechanical, electrical, and tensile self-sensing properties of UHPC. The results demonstrated that incorporating SCBA below the critical concentration of 3.0 wt% enhanced the mechanical properties of UHPC. Notably, adding 3.0 wt% SCBA remarkably improved the compressive, flexural, and tensile strengths of UHPC, resulting in increases of 13.1%, 17.4%, and 20.6%, respectively. However, excessive incorporation of SCBA adversely affected the mechanical properties due to reduced workability of UHPC, increased generation of harmful voids, and a lower degree of hydration caused by the excess SCBA. Furthermore, the inclusion of SCBA influenced the electrical resistivity of UHPC, and specifically, an SCBA content of 0.3 wt% yielded the maximum electrical resistivity. Moreover, incorporating SCBA in UHPC enhanced its tensile stress-sensing performance compared to SCBA-free UHPC. Among the various SCBA contents tested, UHPC with 0.3 wt% SCBA presented the best linearity, with values of 8.8% for loading and 17.0% for unloading, respectively, which were significantly lower than those for SCBA-free UHPC, which were 14.0% and 60.0%, respectively. Additionally, UHPC with 0.9 wt% SCBA gained the lowest hysteresis and repeatability, with values of 13.3% and 5.3%, respectively, which were much lower than those for SCBA-free UHPC, which were 50% and 51.6%, respectively. The tensile stress-sensing performance of UHPC is influenced by three key aspects: the gap between adjacent conductive fillers, contact resistance, and the connectivity of the electrical network, which are subject to change due to varying stress states and SCBA concentrations. This study should aid SCBA use and promote UHPC's practical applications.

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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.505

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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