...%), nozzle temperature (NT) (200–210–220 °C) and printing speed (PS) (40–80–120 mm/s). Predictive models were developed using four machine learning (ML) algorithms: Gaussian process regression (GPR), extreme gradient boosting (XGBoost), artificial neural network (ANN) and random forest regression (RFR...

... ) ε t i n E o Figure 2 The performance of concrete in the plastic model (a) under compression (b) under tension CFRP retrofitting Database Finite element analysis CFST Artificial neural networks (ANN) Damaged plastic model Machine learning Analytical model...

... that the most accurate model order is RBF, ANN, MLR and ELM. Originality/value There are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling...

..., there is ongoing interest in exploring the potential of machine learning techniques for this purpose. Design/methodology/approach Within the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter...

...) of CFST columns, there is no systematic review of these Machine Learning methods. Findings The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools...

... Publishing Limited Licensed re-use rights only Thrust force Drilling Composite material Gaussian process regression Machine learning (13) Linear basis :  B = [ 1 , X ] The composite material consists of two or more micro-constituents which are different in chemical...