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Reinforced concrete (RC) elements frequently undergo biaxial shear forces, for example, columns under seismic events and spandrel beams that support an inclined roof. Nonetheless, experimental testing of RC elements subject to biaxial shear deserved far less consideration, and even fewer attempts have been made to develop analytical models for the biaxial shear capacity prediction. This paper proposes a new resisting mechanism that extends the well-established variable-angle truss model in use for uniaxial shear to calculate the biaxial shear capacity of RC beams under monotonic loading and of RC columns under cyclic loading. The coefficients ruling the concrete contribution—for which new expressions were recently derived using a machine-learning algorithm in case of uniaxial shear—are straightforwardly adapted to the proposed biaxial shear resisting mechanism without supplemental recalibration. The accuracy of the proposed analytical formulation to predict the biaxial shear capacity of RC elements is assessed against new experimental results reported in the present study and those already available in the current literature. A comparative statistical assessment shows that the proposed analytical formulation is more accurate than alternative uniaxial shear capacity equations of technical codes applied to the biaxial case under the assumption of a quadratic interaction domain. [ABSTRACT FROM AUTHOR]

Abstract Digital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.