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

• Published in: Minerals (Basel) Vol. 15; no. 4; p. 405
• Main Authors: Cao, Hui, Wang, Aiai, Yilmaz, Erol, Cao, Shuai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.04.2025
• Subjects: 3-D printers; Additives; Age; Algorithms; Artificial intelligence; Carbon; Cellulose; Cement; Composite materials; Compressive strength; Concrete; Construction; Correlation coefficient; Correlation coefficients; Curing; Data mining; Energy consumption; Engineering; Geospatial data; Hydration; Learning algorithms; Machine learning; Mines; Mining; Neural networks; Parameters; Prediction models; Soft computing; Software
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min15040405

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: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) was applied to existing AI and soft computing techniques, using AdaBoost, random forest (RF), SVM, and ANN. Data were arbitrarily separated into training (70%) and test (30%) sets. Results confirm that AdaBoost and RF have the best prediction accuracy, with a correlation coefficient (R2) of 0.957 between these sets for AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 3.9 64-bit), and PyQt5 to achieve the machine learning model computation and software function interface development, the application of this software can quickly predict the strength property of CTF specimens, which saves time and costs efficiently for backfill researchers and developing new eco-efficient components.