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Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics. • Genetic Algorithm samples large space of inputs while delivering desired solutions. • Clustering discovers classes of models with similar outputs via different mechanics. • Three unique internal load state configurations were observed in the model. • The approach is general and could be applied to other areas of mechanisms science. • Uncertainty Quantification meets Machine Learning to unravel complex mechanisms. [ABSTRACT FROM AUTHOR]

We present a numerical study on virtual concrete beams with shallow midspan notches loaded in three-point bending. We document how spatial randomness of material parameters allows a crack to initiate outside the shallow notch. The interacting effects of notch depth and fluctuation rate of material parameters are shown. We use a discrete meso-level model to simulate material behavior. An approach that introduces material spatial randomness into the discrete mechanical model for concrete fracture through an autocorrelated random field is briefly presented.

This paper contains the results of an investigation into the effect of the discretization of lattice models. The study is performed with homogeneous models where all elements share the same strength. Elemental constitutive law is linearly-brittle, meaning that elements behave linearly but are completely removed from the structure as soon as they reach the limit of their strength. The relation between structural size and discretization density is studied with unnotched beams loaded in three point bending (modulus of rupture test). We report the results for regular discretization and irregular networks obtained via Voronoi tessellation. This is carried out for two types of models: these being with and without rotational springs (normal and shear springs are always present). The numerically obtained dependence of strength on discretization density is compared to the analytical size effect formula.

This paper shows that mesh density dependency in brittle element lattice models cannot be completely removed by incorporating material mesostructure. This is concluded as a result of a series of simulations of fracturing in concrete using a 2D rigid-body-spring network. In the case of notched beams loaded in three-point-bending (TPB), adding finer grain structure led to a significant reduction in the dependency of the peak force on the mesh density. However, the energy dissipated during the fracture simulation was still affected significantly by the mesh density.