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Record W4416181705 · doi:10.1139/cgj-2025-0243

Coconut shell fiber for reinforcing lime-stabilized soil: A sustainable approach to improve resilience

2025· article· en· W4416181705 on OpenAlexvenueno aff
Lihua Li, Wen Liu, Xunchang Fei, Zhanbo Cheng, Wentao Li

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsDurabilityBrittlenessReinforcementResilience (materials science)Ultimate tensile strengthCompressive strengthFiberLime

Abstract

fetched live from OpenAlex

Lime-stabilized soils often exhibit brittleness and limited durability under environmental loading. This study investigates coconut shell fiber (CSF) as a sustainable, cost-effective reinforcement for enhancing mechanical and durability performance of lime-stabilized clayey soil. Laboratory tests with 1–9% lime and 0.25–1.0% CSF assessed compaction, permeability, strength, wet–dry and freeze–thaw durability, and microstructure. The results show that permeability decreased from 8.0×10⁻⁸ to 3.0×10⁻⁸ cm/s with 9% lime, but rose slightly to 5.0×10⁻⁸ cm/s at 0.75% CSF. Optimum performance was at 3% lime, 3 cm fiber length, and 0.75% CSF, yielding UCS of 4795.7 kPa and tensile strength of 9.0 Pa after curing 60 days. CSF addition transformed failure from brittle to ductile and enhanced long-term strength. After the first wet–dry cycle, UCS increased 30.9–53.4% for lime-only and 61.7–79.9% for CSF-reinforced specimens. After six cycles, UCS and mass losses were 40.0–71.3% and 6.2–31.1% (lime-only) versus 18.0–53.4% and 5.0–29.9% (CSF-reinforced). Under freeze–thaw, UCS losses were 6.3–62.3% (lime-only) and 5.9–54.5% (CSF-reinforced). Microstructural analysis showed improved matrix integrity, reduced porosity, and greater hydration product formation with CSF. Overall, CSF improved strength by up to 89.9% under standard curing, 58.0% under freeze–thaw, and 112.2% under wet–dry conditions, demonstrating its effectiveness as a low-carbon reinforcement for resilient infrastructure.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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