Enhancing mechanical performance of red soils via lime–phosphogypsum stabilization: A Box–Behnken design approach
Bibliographic record
Abstract
Abstract This study evaluates the feasibility of storing phosphogypsum (PG) on lime‐stabilized red soils (RS) and quantifies the synergistic stabilization capacity of PG‐hydraulic lime (L) blends. Mortar specimens with variable RS/L/PG ratios underwent comprehensive physicochemical (pH, electrical conductivity [EC], X‐ray fluorescence [XRF]), geotechnical (Atterberg limits), mineralogical (X‐ray diffraction [XRD], Fourier transform infrared [FTIR]), microstructural (scanning electron microscopy [SEM]/energy dispersive spectroscopy [EDS]), thermogravimetric (differential thermal analysis coupled with thermogravimetric analysis [DTA‐TG]), and mechanical (unconfined compressive strength [UCS]) characterization. Box–Behnken design (BBD) was applied to delineate the influence of varying proportions of RS, L, and PG on the mechanical performance of stabilized soil composites. The results establish that 10 wt% L with ≤32 wt% PG significantly enhances soil performance. The UCS increased from 1.67 MPa (RS + 2%L) to 4.48 MPa (RS + 10%L + 32%PG), and the plasticity index decreased from 17.47% (untreated RS) to 12.64% (RS + 10%L + 10%PG). Critically, PG addition did not induce ettringite formation despite available sulfate ions (SO 4 2− ), aluminol/silicate groups, Ca 2+ , and OH − ions, eliminating the risks of sulfate‐induced expansion. Scanning electron microscopy (SEM) revealed rod‐shaped gypsum microcrystals (CaSO 4 ·2H 2 O) on particle surfaces, accelerating hydration kinetics and strengthening mechanical performance through microstructural densification. This study establishes PG as a sustainable co‐additive that concurrently mitigates industrial waste liabilities and enhances geotechnical performance in marginal red soils. Component synergies rigorously quantified via BBD provide a mechanistic blueprint for eco‐engineered infrastructure and circular waste management strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".