Comparison of regression based functions and ANN models for predicting the compressive strength of geopolymer mortars.

ABSTRACT: This study investigated the predictability of the compressive strength (CS) of geopolymeric mortars based on blast furnace slag (BFS) and steel mill slag (SMS). For this purpose, the study consists of two parts. In the first part of the study, BFS and SMS, two different types of slag were used as binders in 11 different proportions. At the end of the curing period, the weight, ultrasonic pulse velocity (UPV) and compressive strength of the mortars were determined. In the second part of the study, the compressive strength was predicted using regression analysis (CRA), multivariate adaptive regression spline (MARS), random forest (RF), multiple additive regression trees (TreeNet) and artificial neural networks (ANN). The model performance of the methods was compared using root mean square er

SUBMITTER: Katatchambo AY 

PROVIDER: S-EPMC11971458 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature