ArticleScientific reports2024
Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 48 citations in OpenAlex.
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- Performance evaluation of machine learning algorithms for predicting liquefaction-induced lateral displacement.Scientific reports · 2026Article
- Predicting unconfined compressive strength of geopolymer-stabilized clays using a sector fruit fly-based extreme learning machine.Scientific reports · 2026Article
- Mechanical properties analysis of geopolymer concrete based on the sugarcane bagasse ash using machine learning.Scientific reports · 2026Article
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Authors and funding
7 authors at 7 institutions in 6 countries.
Funding
Abstract
The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R
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Registered trials
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