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Cement-based materials, especially those with low water-cement ratios, often experience premature cracking due to plastic shrinkage in the early curing stages. In this study, the development mechanism of early-age plastic shrinkage of cement paste, and the crack shrinkage control effect of recycled cement paste powder on cement paste, was quantitatively investigated using non-contacting two-dimensional digital image technology. The influence of different replacement rates (5%, 10%, 20% and 30%) of recycled cement paste powder on the major principal strain and crack patterns of cement paste was investigated. Furthermore, the mechanism of recycled cement paste powder on the early-age plastic shrinkage of cement-based materials was explored. The results show that the addition of recycled cement paste powder could suppress the early-age plastic shrinkage of cement paste. An appropriate replacement ratio (10%) of recycled cement paste powder shows a 33.3% time delay in crack appearance and a 28.0% reduction in the major principal strain. However, the higher replacement ratio of 30% shows an adverse effect on the major principal strain, with an increase of 35.1%. The core mechanism of the appropriate recycled cement paste powder on plastic shrinkage reduction lies in its porous nature, which allows for water absorption and release and regulates the moisture state inside the pores. This quantitative research of the major principal strain development of the early-age plastic shrinkage of cement paste can facilitate a better understanding of plastic shrinkage reduction in recycled cement paste powder on cement paste.

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85 % larger than 95MPS × SR2 in average. Across every element in all head impacts, the average MPSR1 was 12.73 % smaller than MPSR2, and MPS × SR1 was 11.95 % smaller than MPS × SR2. Furthermore, logistic regression models were trained to predict TBI using MPSR (or MPS × SR), and no significant difference was observed in the predictability. The consequence of misuse of MPSR and MPS × SR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1 %. The evidence suggested that these two methodologies were not significantly different in detecting TBI.

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

The deterioration of the mechanical properties of rock mass in underground engineering due to energy dissipation and microfracture accumulation under cyclic loading and unloading (CLU) has become a hot research topic in recent years. In order to elucidate the relationship between energy dissipation and the dilation processes, an incremental CLU mode is proposed and employed in triaxial tests in this paper. First, the methods and the procedures of the test were proposed to enhance the success rate, and the triaxial tests under incremental CLU were performed at confining pressures ranging from 0 MPa to 50 MPa. Second, the damage variables were defined and calculated from the perspective of energy dissipation, and the relationships between the damage variables and the energy parameters, as well as principal strains (plastic strain and plastic shear strain) were analyzed in detail. Third, a two-parameter shear dilation angle model was established under different confining pressures. The results show that the failure mode is tensile failure in conventional uniaxial compression, and is X-type shear failure in the uniaxial test with incremental CLU, and is shear dilatation failure mode in the triaxial test with incremental CLU, due to volume dilatation and damage accumulation after significant energy dissipation. The energy dissipation rates for single loading and unloading showed a “U” shaped trend as the number of the CLU increased. The reason for this observation is that the energy dissipation rate is greater due to the particle compaction in the initial compaction stage and structural damage of rock material in failure stage. The dilation angle first experiences a nonlinear rapid increase to a certain peak value and then gradually decays, as the rock shear dilation and failure occur gradually with the accumulation and development of cracks.

An improved understanding of the mechanical properties of human femurs is a milestone towards a more accurate assessment of fracture risk. Digital image correlation (DIC) has recently been adopted to provide full-field strain measurements during mechanical testing of femurs. However, it has typically been used to measure strains on the anterior side of the femur, whereas in both single-leg-stance and sideways fall loading conditions, the highest deformations result on the medial and lateral sides of the femoral neck. The goal of this study was to measure full-field deformations simultaneously on the medial and lateral side of the femoral neck in a configuration resembling a fall to the side. Twelve female cadaver femurs were prepared for DIC measurements and tested in sideways fall at 5 mm/s displacement rate. Two pairs of cameras recorded the medial and lateral side of the femoral neck, and deformations were calculated using DIC. The samples exhibited a two-stage failure: first, a compressive collapse on the superolateral side of the femoral neck in conjunction with peak force, followed by complete femoral neck fracture at the force drop following the post-elastic phase. DIC measurements corroborated this observation by reporting no tensile strains above yield limit for the medial side of the neck up to peak force. DIC measurements registered onto the bone micro-architecture showed strain localizations in proximity of cortical pores due to, for instance, blood vessels. This could explain previously reported discrepancies between simulations and experiments in regions rich with large pores, like the superolateral femoral neck.

Brain and spinal cord injuries have devastating consequences on quality of life but are challenging to assess experimentally due to the traumatic nature of such injuries. Finite element human body models (HBM) have been developed to investigate injury but are limited by a lack of biofidelic spinal cord implementation. In many HBM, brain models terminate with a fixed boundary condition at the brain stem. The goals of this study were to implement a comprehensive representation of the spinal cord into a contemporary head and neck HBM, and quantify the effect of the spinal cord on brain deformation during simulated impacts. Spinal cord tissue geometries were developed, based on 3D medical imaging and literature data, meshed, and implemented into the GHBMC 50th percentile male model. The model was evaluated in frontal, lateral, rear, and oblique impact conditions, and the resulting maximum principal strains in the brain tissue were compared, with and without the spinal cord. A new cumulative strain curve metric was proposed to quantify brain strain distribution. Presence of the spinal cord increased brain tissue strains in all simulated cases, owing to a more compliant boundary condition, highlighting the importance of the spinal cord to assess brain response during impact.

Microstructural topology optimization (MTO) is the simultaneous optimization of macroscale topology and microscale structure. MTO holds the promise of enhancing product-performance beyond what is possible today. Furthermore, with the advent of additive manufacturing, the resulting multiscale structures can be fabricated with relative ease. There are however two significant challenges associated with MTO: (1) high computational cost, and (2) potential loss of microstructural connectivity. In this paper, a novel density-and-strain-based K-means clustering method is proposed to reduce the computational cost of MTO. Further, a rotational degree of freedom is introduced to fully utilize the anisotropic nature of microstructures. Finally, the connectivity issue is addressed through auxiliary finite element fields. The proposed concepts are illustrated through several numerical examples applied to two-dimensional single-load problems.

A new data-driven method that generalizes experimentally measured and/or computational generated data sets under different loading paths to build three dimensional nonlinear elastic material law with objectivity under arbitrary loadings using neural networks is proposed. The proposed approach is first demonstrated by exploiting the concept of representative volume element (RVE) in the principal strain and stress spaces to numerically generate the data. A computational data-training algorithm on the generalization of these principal space data to three dimensional objective isotropic material laws subjected to arbitrary deformation is given. To validate these data-driven derived material laws, large deformation and buckling analysis of nonlinear elastic solids with reference material models and engineering structure with microstructure are performed. Numerical experiments show that only seven sets of data under different stress loading paths on RVEs are required to reach reasonable accuracy. The requirements for constitutive law such as objectivity are preserved approximately. The consistent tangent modulus is also derived. The proposed approach also shows a great potential to obtain the material law between different scales in the multiscale analysis by pure data.

Tissue-level brain responses to sport-related head impacts may be stronger predictors of brain injury risk than head kinematics alone. Despite the importance of accurate impact response estimation, the influence of head morphological variations has not been properly considered due to the limited sizes and shapes of existing computational head models. In this study, we developed 101 subject-specific finite element (FE) head-brain models based on CT scans and a parametric modeling approach to estimate tissue-level brain impact responses (maximal principal strain, MPS) under three head impact conditions. Principal component analysis (PCA) was used to quantify the geometric variations, with statistically significant PCs then selected to predict MPS using a stepwise linear regression model. High adjusted R2 values (0.6–0.9) were achieved in the regression model, suggesting a good model predictability. Brain volume explained the largest variance of 51.3%, and it was highly correlated with MPS, indicating a significant size effect on brain impact responses. This is the first modeling study to systematically consider the influence of morphological variations in the inner skull and scalp on brain tissue impact response.

Continuum mechanics-based finite element models of the shoulder aim to quantify the mechanical environment of the joint to aid in clinical decision-making for rotator cuff injury and disease. These models allow for the evaluation of the internal loading of the shoulder, which cannot be measured in-vivo. This study uses human cadaveric rotator cuff samples with surface tendon strain estimates, to validate a heterogeneous finite element model of the supraspinatus-infraspinatus complex during various load configurations. The computational model was considered validated when the absolute difference in average maximum principal strain for the articular and bursal sides for each load condition estimated by the model was no greater than 3% compared to that measured in the biomechanical study. The model can predict the strains for varying infraspinatus loads allowing for the study of load sharing between these two tightly coordinated tendons. The future goal is to use the modularity of this validated model to study the initiation and propagation of rotator cuff tear and other rotator cuff pathologies to ultimately improve care for rotator cuff tear patients.