ArticleScientific reports2025
Machine learning approaches for resilient modulus modeling of cement-stabilized magnetite and hematite iron ore tailings.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Resilient modulus (Mr) is key for understanding the stiffness and stress‒strain properties of road materials for flexible pavement design. Measuring Mr in a laboratory requires conducting dynamic triaxial loading tests with varying confining and deviatoric stresses, which can be costly and time-consuming process. This study evaluates various machine learning (ML) models to predict the Mr of cement-stabilized magnetite and hematite iron ore tailings based on multiple variables such as cement content, curing time, bulk stress, and deviatoric stress, which are considered as input parameters. For developing ML models, a set of data from experimental studies was collected. After comparison, Gaussian Process Regression outperformed other methods in predicting Mr of both MIOT and HIOT. For the MIOT and HIOT materials, the R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.