Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning
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.
Bibliographic record
Abstract
Concrete is a brittle material with low tensile strength, requiring reinforcement bars to carry the tensile load and ensure structural serviceability and durability. This study aims to improve the mechanical properties and bond behavior of natural aggregate concrete (NAC) and recycled aggregate concrete (RAC) by incorporating locally available galvanized iron fiber (GIF). Two concrete strengths (30 MPa and 40 MPa) were considered with GIF lengths of 15 mm and diameters of 0.5 mm. Eighteen mix combinations were tested with varying GIF (0 %, 0.25 %, 0.5 %) and recycled coarse aggregate (RCA) contents (0 %, 30 %, 50 %). Three rebar diameters (12 mm, 16 mm, and 20 mm) with embedment lengths of 8D and 12D were used. Results showed significant improvements in compressive strength and split tensile strength, up to 39.3 % and 13.93 %, depending on the GIF and RCA percentages. Up to 40.8 % and 46.5 % higher bond strength was found using 0.25 % and 0.5 % GIF, respectively. The study also employed regression and machine learning (ML) models to predict bond strength. The XGB and ANN models were used to compare the proposed regression equations and existing mechanical models with the ML models. Based on the investigation, it is suggested that 0.25 % or 0.5 % of GIF be used while limiting the RCA content to 30 % for optimal performance. By utilizing locally available and cost-effective GIF alongside RCA, these findings contribute to sustainable construction practices by enhancing the mechanical and bond properties of concrete while addressing environmental concerns.
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 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 it