AI-BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Abstract We introduce AI-BioMech, a deep learning based framework that directly predicts the mechanical response of cellular structures from 2D images, eliminating the need for manual geometry definition and traditional finite element simulations. The framework is trained on synthetic datasets representing biological cellular structures and benchmarked against real experimental data. Finite element analysis (FEA) based labeling is used to generate pixel level annotations for semantic segmentation, enabling accurate identification of stress and strain distributions. By learning spatial and hierarchical patterns from these annotations, the model automatically extracts complex features to predict cellular material responses under compressive loading conditions. Transfer learning with fine tuning by using the DeepLabv3 architecture with ResNet50, ResNet101, and Inception ResNetV2 backbones enhances prediction accuracy and generalization from limited datasets. Model predictions are validated against experimental results and Digital Image Correlation (DIC) measurements, demonstrating strong agreement with physical observations. The results show that AI-BioMech achieves up to 99% prediction accuracy while significantly outperforming traditional methods in computational speed and scalability.