Machine learning for predicting mechanical behavior of concrete beams with 3D printed TPMS

Abstract

Bioinspired structures are remarkable porous structures with great strength-to-weight ratios. Hence, they have been applied in various fields including biomedical, transportation, and aerospace materials, etc. Recent studies have shown the significant impact of the plastic 3D printed triply periodic minimal surfaces (TPMS) structure on the cement beam including increasing the peak load, reducing the deflection, and improving the ductility. In this study, a machine learning (ML) surrogate model has been conducted to predict the beam behavior under static bending load. At first, various combinations of plastic volume fractions and numbers of core layers have been adopted to reinforce the constituent beam. The finite element method (FEM) was implemented to investigate the influences of these reinforcement strategies. Next, the above data were employed to create the ML model. A three-process assessment was proposed to achieve the most suitable model for the present problem, these processes were the model hyperparameter tuning, the performance assessment, and the handling overfitting with deep learning (DL) techniques. Consequently, both beam peak loads and maximum deflections were proportional to the volume fraction. The increment in TPMS layers could lead to the enhancement in both traits but with a nonlinear relationship. Furthermore, each trait may be a ceiling value that could not be exceeded with a specific volume fraction despite any number of layers. This conclusion was indicated by the surrogate model predictions. The final model in this study could deal with noisy data from FEM and with the support of a new early stopping condition, excellent performance could be found on both train and test data. The maximum deviations of 2.5% and 3.5% for peak loads and maximum midpoint displacements, respectively, have verified the robustness of the present surrogate model.