Machine Learning Algorithm-Based Prediction Model and Software Implementation for Strength Efficiency of Cemented Tailings Fills

Minerals, Год журнала: 2025, Номер 15(4), С. 405 - 405

Опубликована: Апрель 11, 2025

A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) applied existing AI soft computing techniques, using AdaBoost, random forest (RF), SVM, ANN. Data were arbitrarily separated into training (70%) test (30%) sets. Results confirm that AdaBoost RF have best prediction accuracy, with a correlation coefficient (R2) 0.957 between these sets AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 64-bit), PyQt5 achieve machine learning model computation software function interface development, this can quickly property CTF specimens, which saves time costs efficiently backfill researchers developing new eco-efficient components.

Язык: Английский

Impact of Process Parameters and Material Selection on the Mechanical Performance of FDM 3D-Printed Components

Hybrid Advances, Год журнала: 2025, Номер unknown, С. 100502 - 100502

Опубликована: Май 1, 2025

Язык: Английский