Predicting unconfined compressive strength of geopolymer-stabilized clays using a sector fruit fly–based extreme learning machine
Mohamed Abdellatief; Mohamed Mortagi · 2026 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Accurate prediction of the unconfined compressive strength (UCS) of geopolymer-stabilized clayey soil is critical for geotechnical engineering. Conventional regression algorithms and even advanced machine learning approaches such as artificial neural networks often struggle to fully capture the highly non-linear interactions among soil properties and geopolymer mix parameters while maintaining computational efficiency and interpretability on limited datasets. To address these challenges, this investigation proposes a novel hybrid predictive framework based on a sector fruit fly optimization al
Abstract by Mohamed Abdellatief; Mohamed Mortagi, Scientific Reports (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-026-47208-z