Passive determination of anisotropic compressive strength of 3D printed concrete using multiple neural networks enhanced with explainable machine learning (XML).

ABSTRACT: 3D Concrete Printing (3DCP) offers significant advantages over traditional construction such as faster construction time, reduced material wastage, and enhanced ability to execute complex architectural designs. The incorporation of various fibres and industrial wastes into 3DCP can improve performance and sustainability but introduces non-linear effects on compressive strength (CS) that are difficult to predict with standard laboratory methods. This study aims to develop reliable prediction models for the CS of 3DCP by employing advanced neural network and deep learning algorithms such as Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Radial Basis Functional Neural Network (RBFNN). A comprehensive database of 200 experimental instances of CS of 3DCP was collected from

SUBMITTER: Iqbal I 

PROVIDER: S-EPMC12708720 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature