Nonlinear moment–curvature (M–φ) analysis is essential for evaluating the strength and ductility of reinforced concrete (RC) structures. Traditional numerical methods, such as iterative fiber analysis, provide high accuracy but are computationally intensive. This paper presents the evolution of approaches – from empiri...
Machine learning is increasingly used to predict the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC), with coefficients of determination of 0.81–0.87 reported in the literature. This paper first shows that the widely used reference dataset of 603 mixtures contains only 504 uniqu...
Bojana Milošević, Nenad Kojić, Milan Kragović· Journal of Composites Scienc...· 0 citations
Robust, leakage-aware evaluation, rather than model architecture, emerges as the key to credible strength prediction on this benchmark, with Mutual-information and partial-dependence analyses identify cement as the dominant predictor, with water acting mainly through a nonlinear dependence.
This study investigates the application of artificial neural networks (ANNs) for estimating the compressive strength of clay masonry walls based on the mechanical and geometrical properties of their constituent materials. A multilayer perceptron (MLP) neural network was developed using a hybrid dataset derived from Eur...
