Mechanical, Electrical, and Tensile Self-Sensing Properties of Ultra-High-Performance Concrete Enhanced with Sugarcane Bagasse Ash
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".