A data-driven framework for developing a unified density-modulus relationship for the human lumbar vertebral body.

Despite the broad agreement that bone stiffness is heavily dependent on the underlying bone density, there is no consensus on a unified relationship that applies to both cancellous and cortical compartments. Bone from the two compartments is generally assessed separately, and few mechanical test data are available for samples from the transitional regions between them. In this study, we present a data-driven framework integrating experimental testing and numerical modeling of the human lumbar vertebra through an energy balance criterion, to develop a unified density-modulus relationship across the entire vertebral body, without the necessity of differentiation between trabecular and cortical regions.

A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials.

Extracting the relation between microstructural features and resulting material properties is essential for advancing our fundamental knowledge on the mechanics of cellular metamaterials and to enable the design of novel material systems. Here, we present a unified framework that not only allows the prediction of macroscopic properties but, more importantly, also reveals their connection to key morphological characteristics, as identified by the integration of machine-learning models and interpretability algorithms. We establish the complex manner in which strut orientation can be critical in determining effective stiffness for certain microstructures and highlight cellular metamaterials with counterintuitive material behavior.