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Stress, Mechanical

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Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain).

Scientific reports
Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical...

Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism.

Scientific reports
This research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical exper...

Prediction Model of Stress Intensity Factor of Circumferential Through Crack in Elbow Based on Neural Network.

Computational intelligence and neuroscience
Using ANSYS software to establish the finite element model of crack bending tube, the SIF at the tip of the crack is calculated for the difference in the diameter of the pipe, the outer diameter of the elbow, and the bending angle of the bend pipe, a...

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...

Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle.

Medical & biological engineering & computing
Hyperelastic constitutive laws have been commonly used to model the passive behavior of the human skeletal muscle. Despite many efforts, the use of accurate finite element formulations of hyperelastic constitutive laws is still time-consuming for a r...

Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning.

Acta biomaterialia
Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological ...

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...

A strategy to formulate data-driven constitutive models from random multiaxial experiments.

Scientific reports
We present a test technique and an accompanying computational framework to obtain data-driven, surrogate constitutive models that capture the response of isotropic, elastic-plastic materials loaded in-plane stress by combined normal and shear stresse...

Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model.

Sensors (Basel, Switzerland)
Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring t...

A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation.

Biophysical journal
Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechanical stresses...