This study advances the field of concrete compressive strength prediction by introducing an
innovative approach incorporating recycled coarse aggregates and the Newton's Boosted …

This paper presents a state-of-the-art hybrid stacked machine learning (ML) model for
predicting the compressive strength of high-performance concrete (HPC). The proposed …

The paper proposes a novel approach for predicting surface chloride penetration in marine
concrete using a faster and more efficient Backpropagation Neural Network (BPNN) model …

When simulating the mechanical behavior of complex materials, the failure behavior is
strongly influenced by the internal structure. To account for such dependence, models at the …

This study focuses on predicting surface chloride concentration (C s) in marine concrete
structures using machine learning (ML) models. The dataset includes input features related …

We applied physics-informed neural networks to solve the constitutive relations for
nonlinear, path-dependent material behavior. As a result, the trained network not only …

The mix design of concrete is conventionally performed in the laboratory. However, the
limitations of time, cost of materials, single-objective optimization, and the limited number of …

In this study, synthetic CaCO 3 materials were utilized as precursors for CaO-based CO 2
sorbents. The investigation examined how various operating parameters—such as synthesis …

This study investigates the predictive capacity of machine learning models for the
compressive, flexural, and split tensile strengths of concrete incorporating coconut shell as a …

We applied physics-informed neural networks to solve the constitutive relations for
nonlinear, path-dependent material behavior. As a result, the trained network not only …