Fracture Prediction Research Articles

Prediction of Fragility Fractures and Mortality in a Cohort of Geriatric Patients

ABSTRACTBackgroundRisk factors of refracture after fragility fractures include osteoporosis, female gender and advanced age among others. We hypothesized that the assessment of functionality, muscle health and nutrition status contribute to the risk prediction for further fractures and death.MethodsWe assessed 334 patients admitted to the department of acute geriatrics for sociodemographic data, bone fragility, selected laboratory tests, body composition and data on functionality using the comprehensive geriatric assessment. Patients had follow‐ups until the occurrence of further fractures or death. Dual‐energy X‐ray absorptiometry and pulse echo measurements were performed to assess bone mineral density. Fracture risk was assessed using the FRAX score and muscle strength according to published guidelines on sarcopenia.ResultsThe mean age was 81 years (70–95), and 82.3% (275/334) were women. An incidence of 10.4% (35/334) new fragility fractures was observed within 24 months, and the mortality rate was 12.2% (41/334). A significantly higher rate of further fractures was associated with lower BMI (body mass index) (HR 0.925, CI 0.872–0.98; p = 0.009), lower parathyroid hormone levels (HR 0.986, CI 0.973–0.998; p = 0.026) and with the diagnosis of osteoporosis (HR 2.546, CI 1.192–5.438; p = 0.016). No significant associations were present in patients with previous fractures, with higher age, higher FRAX scores, sarcopenia, in women, sarcopenic obesity, frail patients, lower grip strength, lower walking speed, lower Barthel index or lower DI (density index) values. The predictive power for further fractures was 10.7% higher adding osteosarcopenia, BMI and parathyroid hormone levels to standard assessment parameters osteoporosis, age and the status of previous fractures. Mortality was significantly higher with advanced age (HR 1.101, CI 1.052–1.151; p < 0.001), in men (HR 6.464, CI 3.141–13.305; p < 0.001), in smokers (p = 0.002), higher FRAX score (HR 1.039, CI 1.009–1.070; p = 0.010), lower renal function (HR 0.987, CI 0.976–0.997; p = 0.010), lower Tinetti test scores (HR 0.943, CI 0.900–0.987; p = 0.012), lower walking speed (HR 0.084, CI 0.018–0.382; p = 0.001), lower hand grip (HR 0.876, CI 0.836–0.919; p < 0.001) and lower Barthel index scores (HR 0.984, CI 0.971–0.997; p = 0.015).ConclusionsIn a cohort of geriatric patients, the addition of BMI, low parathyroid hormone levels and osteosarcopenia increases the predictive power for further fractures by 10.7%. These parameters are a valuable addition to the standard assessment parameters age and history of sustained fractures. Mortality is partly associated with potentially treatable functional parameters.

Prediction of Fractures After Open Wedge High Tibial Osteotomy Based on the Distance From the Tibial Osteotomy Point to the Medial Edge of the Tibia

Prediction of Fractures After Open Wedge High Tibial Osteotomy Based on the Distance From the Tibial Osteotomy Point to the Medial Edge of the Tibia

A novel clinical prediction model for hip fractures: a development and validation study in the total population of Sweden

Low bone density and osteoporosis are indications for bone-specific treatment. However, given the limited availability of bone density data in clinical practice and the fact that most patients with hip fracture do not have osteoporosis, accurate prediction of hip fracture risk in the absence of bone density data would be crucial. This development and validation study included the entire Swedish population aged 50 years or older in 2005 (N=3,340,977) and was conducted by cross-linking data from nationwide registers. Potential predictive variables included diagnoses, prescription medications, familial factors, frailty-related factors, and socioeconomic factors. The primary endpoint was the 5-year risk of hip fracture. Fracture prediction algorithms were developed and validated using multivariable models. Model performance and validation was also examined in a sub cohort restricted to 504,431 individuals with non-Swedish background. During a total follow-up of 15.2 million person-years, 87,089 individuals suffered a hip fracture within 5 years. In the final prediction model, 19 variables were associated with a population attributable fraction of 93.9% (95% CI, 93.7-94.1) in women and 92.7% (95% CI, 92.2-93.0) in men. The strongest predictor, besides old age, was the use of homemaker service, with a 5-year risk of hip fracture of 7.8% in women and 4.7% in men. The diagnoses most strongly predicting the 5-year risk of hip fracture was Parkinson's disease (6.8% in women, 4.6% in men) and dementia (6.1% in women, 3.6% in men). Validation of the prediction model suggested that the optimal threshold for treatment with bone-specific agents was an estimated 5-year hip fracture risk of 3%. Assuming a threshold of 3% and a 30% relative risk reduction from bone-specific treatment, the number needed to treat to prevent one hip fracture was estimated to 36 in women and 52 in men. Similar results were obtained in the sub cohort with non-Swedish background. A clinical prediction model developed and validated in the total Swedish population could predict the risk of hip fractures with high precision even in absence of data on bone density. The model was associated with a population attributable fraction for hip fracture of more than 90%, and the strongest predictor besides old age was the use of homemaker service, which likely reflect frailty. Based on the model, individuals with an estimated 5-year risk of hip fracture of at least 3% could be considered for bone-specific treatment. None.

Isolated Rib Response and Fracture Prediction for Young Mid-Size Male, Enabled by Population Specific Material Models and Rib Cross-Sectional Geometry

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Test-and-FE-based method for obtaining complete stress-strain curves of structural steels including fracture

Ductile fracture is a common limit state of frameworks of steel structures and can be predicted by incorporating models for fracture initiation and propagation in finite element (FE) analyses. However, accurate prediction of fracture relies on unique fracture parameters for a given material, requiring precise predictions of fracture strain and stress states obtained through coupon tests and accompanying FE data analysis. In recent decades, various methods have been proposed to predict ductile fracture initiation, including various design geometries of coupons, test setups and FE analyses, which may lead to inconsistent fracture strains and stress states. Hence, there is a need for proposing a standard procedure for the design, test and accompanying FE-based calibration of fracture parameters, as pursued in this paper. Given that ductile fracture initiates under significant plastic strain, a constitutive model for the full strain range is required, as also proposed in this paper. Moreover, to reduce the experimental and data analytical efforts, an empirical method is proposed that uses only a small number of coupon tests to calibrate the fracture parameters. The method encompasses three levels wherein one, two, or three coupon tests are required. All in all, the paper presents a methodology for determining the full-range true stress-strain curve and the initiation of fracture, including a new post-necking constitutive model and methods for determining the free parameters of the post-necking and fracture initiation models. While applied to steel in this paper, the proposed methodology is potentially also applicable to other metals.

Evaluation of fractured carbonate reservoir and prediction of favorable areas in the eastern area of Amu Darya Right Bank

Fractured carbonate reservoirs are significantly developed in the eastern area of the Amu Darya Right Bank. However, their types, distributions, and fracture characteristics remain unclear. This uncertainty complicates reservoir prediction and hampers exploration and development processes. Given the strong correlation between fracture development and productivity, analyzing fractures is crucial. Comprehensive evaluation and prediction methods for fractured reservoirs are essential for advancing the oil and gas industry. Based on core and geological data analyses, it finds that these reservoirs exhibit low porosity and low to ultra-low permeability. By employing conventional logging alongside specialized methods, such as electrical imaging, nuclear magnetic resonance, and far detection logging, fractures and their effectiveness can be identified and evaluated, clarifying the characteristics of reservoir spaces. Constrained by the results from core and logging analyses, seismic single attribute analysis techniques is applied to predict fractures in the HX block of Amu Darya. To mitigate the limitations of single-attribute analysis, utilize a well-supervised BP neural network method for comprehensive fracture prediction. This multi-attribute approach increases the fracture prediction probability from less than 70%–72.7%. By integrating geological understanding and well logging, and considering the influence of lithology and structure on the reservoir, synthesize the fracture prediction results to optimally select favorable areas.

Optimising Neural Networks for Enhanced Fracture Density Prediction in Surrounding Rock of Coalbed Methane Reservoir

ABSTRACTFractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction.

Comparative Study on Artificial Fracture Modeling Schemes in Tight Reservoirs—For Enhancing the Production Efficiency of Tight Oil and Gas

In order to improve the reliability of the deployment of production schemes after artificial fracturing in tight reservoirs, it is urgent to carry out research on the description of fractures after artificial fracturing. In this study, taking the Chang 61 oil formation group in the Wangyao South area of Ordos Basin as an example, three different fracture modeling schemes are used to establish the geological model of fractured reservoirs, and the fitting ratios of the respective reservoir models are calculated by using the method of reservoir numerical simulation of the initial fitting, and the optimal fractured reservoir modeling scheme is screened in the end. The research area adopts three types of fracture prediction results based on FMI fracture interpretation data, seismic fracture prediction data, and rock mechanics artificial fracturing simulation data. On this basis, geological models of fractured reservoirs are established, respectively. The initial fitting of reservoir values of each geological model are compared, and the highest initial fitting rate of reservoir values is 88.44%, which is based on rock mechanics artificial fracturing simulation data. However, the initial fitting rate of the reservoir model was the lowest at 75.76%, which was established based on the fracture random modeling results of FMl fracture interpretation data. Under the constraints of seismic geostress prediction results and microseismic monitoring data, the simulation results of rock mechanics artificial fracturing fracture are used as the basis, on which the geological model of artificially fractured reservoirs is thus established, and this scheme can more realistically characterize the characteristics of fractured reservoirs after artificial fracturing in the study area.

Using QCT for the prediction of spontaneous age- and gender-specific thoracolumbar vertebral fractures and accompanying distant vertebral fractures.

To investigate the value and age- and gender-specific threshold values of bone mineral density (BMD) by quantitative computed tomography (QCT) for the prediction of spontaneous thoracolumbar vertebral fractures and thoracolumbar junction fractures accompanying distant vertebral fractures. Among the 556 patients included, 68 patients had thoracolumbar vertebral fractures (12 patients with distant vertebral fractures, 56 patients without distant vertebral fractures) and 488 patients had no vertebral fractures. All patients were grouped by gender and age. According to the principle of Youden index, the threshold values were calculated from receiver operating characteristic (ROC) curves. The threshold values for predicting thoracolumbar vertebral fractures were 89.8mg/cm3 for all subjects, 90.1mg/cm3 for men, and 88.6mg/cm3 for women. The threshold values for men aged < 60years old and ≥ 60years old were 117.4mg/cm3 and 87.5mg/cm3, respectively. The threshold values for women aged < 60years old and ≥ 60years old were 88.6 and 68.4mg/cm3, respectively. The threshold value for predicting spontaneous thoracolumbar junction fractures with distant vertebral fractures was 62.7mg/cm3. QCT provides a good ability to predict age- and gender-specific spontaneous thoracolumbar vertebral fractures, and to further predict spontaneous thoracolumbar junction fractures with distant vertebral fractures.

Damage Prediction in the Wire Drawing Process

In this study, the prediction of damage in the wire drawing process of 2011 aluminum alloy was investigated through both experimental and numerical methods. A comprehensive experimental setup was designed involving 20 cases of wire drawing with varying die angles (10°, 15°, 21°, 27°, and 34°) and reductions (21%, 29%, 31%, and 38%). Each case was tested three times, and the drawing forces, as well as occurrences of wire breakage, were recorded. The mechanical behavior of the material was firstly characterized using uniaxial tensile tests, whose results were used to determine the material parameters of both the hardening Voce law and those of uncoupled and coupled damage models. Then, the numerical simulations of the wire drawing process were performed using a finite element model, accounting for axisymmetric conditions and mesh convergence analysis to ensure accuracy. The previously characterized damage models were applied to evaluate their fracture prediction capabilities. A novel presentation method using three-dimensional graphs was employed to indicate the level of damage for each angle and reduction, providing greater sensitivity and insight into the damage values. Good agreement between the experimental and numerical data was demonstrated for the bilinear coupled damage model, validating its effectiveness. This study contributes to a better understanding of damage prediction in the wire drawing process, with implications for improving industrial practices and material performance evaluations.

Research on fracture prediction and control of UHSS thin-walled component with complex section in roll forming process

Aiming at the fracture of an ultra-high-strength steel (UHSS) thin-walled component with complex section in the roll forming process, the three-dimensional finite element model was established. It is found that the stress triaxiality and equivalent plastic strain are the main factors affecting the fracture behaviour of the UHSS thin-walled component with complex section, and the influence laws of forming strategies and process parameters on the fracture behaviour were discussed. The results show that the damage value, equivalent plastic strain and stress triaxiality at the fracture position are reduced by increasing the inter-distance and roll diameter, decreasing friction coefficient and forming velocity. The mathematical model of the UDP comprehensive measure with the forming strategies and process parameters was established by the response surface method. The numerical simulation and industrial test verify the effectiveness of the UDP comprehensive measure, which provides a theoretical and practical basis for the fracture prevention control.

8136 Using High-Resolution Peripheral Quantitative Computed Tomography to Assess Bone Microarchitecture and Guide Osteoporosis Treatment

Abstract Disclosure: S.V. Rupani: None. J. Sfeir: None. L. Graeff-Armas: None. Bone microarchitecture is an important factor that impacts bone strength. Unlike other factors that affect bone strength, such as bone mineral density and bone turnover, assessment of bone microarchitecture is not yet widely accessible in clinical practice. Computed tomography is the primary method of non-invasively assessing bone microarchitecture. High-resolution peripheral quantitative CT (HR-pQCT) is an emerging imaging modality that provides a three-dimensional assessment of bone densitometry, morphometry, and microarchitecture with a low effective radiation dose. It provides a quantitative and qualitative assessment of cortical and trabecular structural and mechanical properties. We present two similar cases of osteoporosis in males, where knowledge of bone microarchitecture through HR-pQCT helped formulate two distinct and targeted therapeutic approaches. The first case is of a 46-year-old male with a history of idiopathic osteoporosis with multiple nontraumatic vertebral fractures. Given his presentation and age, anabolic therapy such as teriparatide was being considered. He underwent HR-pQCT scanning, which showed average radial trabecular parameters, but his cortical porosity was significantly above average, with similar findings in his tibial parameters. Since teriparatide has been shown to increase cortical porosity, this would not be the best option for our patient, given the risk of further weakening of the microarchitecture of his cortical bone. Therefore, denosumab, which has been shown to decrease cortical porosity, was chosen. Our second patient is a 52-year-old male with idiopathic osteoporosis with multiple nontraumatic vertebral fractures, whose HR-pQCT showed average cortical parameters at both the radius and tibia but low trabecular number and increased trabecular separation at both sites. Since teriparatide increases trabecular thickness and mass, we could confidently choose teriparatide for this patient, with less concern for significant deleterious effects on his cortical bone parameters. These case reports highlight the impact that the assessment of qualitative properties of bone, such as microarchitecture, can have in the management of osteoporosis. Currently, the lack of sufficient validated normative data for HR-pQCT limits its widespread use in the diagnosis of osteoporosis and fracture prediction. However, given that HR-pQCT can characterize the properties of trabecular and cortical bone, it may have a role in helping guide osteoporosis treatment, especially in challenging and atypical cases. Presentation: 6/3/2024

An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy. Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity. These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95. This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Trabecular Bone Score to Enhance Fracture Risk Prediction and Treatment Strategies in Osteoporosis.

The Trabecular Bone Score (TBS), a gray-level textural assessment derived from dual-energy X-ray absorptiometry images, serves as a validated index of trabecular bone microarchitecture. Over the past decade, significant evidence has highlighted the usefulness of TBS in primary and secondary osteoporosis, leading to its integration with the Fracture Risk Assessment Tool (FRAX) and bone mineral density (BMD) T-score adjustments. This review explores the role of TBS in fracture prediction, treatment initiation, and monitoring. Studies confirm that TBS enhances fracture risk prediction in both primary and secondary osteoporosis when combined with BMD and clinical risk factors. Evidence also suggests that including TBS alongside BMD and FRAX offers significant potential for treatment stratification, considering the overall skeletal profile, such as bone mass, bone quality, and clinical risk factors. Consequently, TBS has become a standard part of clinical care worldwide. Future enhancements hope to adjust for soft tissue thickness, broadening the applicability of TBS across diverse body types and pediatric populations.

How accurately do finite element models predict the fall impact response of ex vivo specimens augmented by prophylactic intramedullary nailing?

Hip fracture prevention approaches like prophylactic augmentation devices have been proposed to strengthen the femur and prevent hip fracture in a fall scenario. The aim of this study was to validate the finite element model (FEM) of specimens augmented by prophylactic intramedullary nailing in a simulated sideways fall impact against ex vivo experimental data. A dynamic inertia-driven sideways fall simulator was used to test six cadaveric specimens (3 females, 3 males, age 63-83 years) prophylactically implanted with an intramedullary nailing system used to augment the femur. Impact force measurements, pelvic deformation, effective pelvic stiffness, and fracture outcomes were compared between the ex vivo experiments and the FEMs. The FEMs over-predicted the effective pelvic stiffness for most specimens and showed variability in terms of under- and over-predicting peak impact force and pelvis compression depending on the specimen. A significant correlation was found for time to peak impact force when comparing ex vivo and FEM data. No femoral fractures were found in the ex vivo experiments, but two specimens sustained pelvic fractures. These two pelvis fractures were correctly identified by the FEMs, but the FEMs made three additional false-positive fracture identifications. These validation results highlight current limitations of these sideways fall impact models specific to the inclusion of an orthopaedic implant. These FEMs present a conservative strategy for fracture prediction in future applications. Further evaluation of the modelling approaches used for the bone-implant interface is recommended for modelling augmented specimens, alongside the importance of maintaining well-controlled experimental conditions.

Extension of the GISSMO fracture model for thin-walled structures under combined tensile and bending loads

Ductile fracture prediction for thin-walled structures requires computationally efficient simulation tools able to approximately represent the key effects that occur on the sub-thickness scale. Unfortunately, classical shell elements, typically used to model deformation and failure of thin components, are inherently in a state of plane stress and therefore unable to capture the through-thickness stress distribution. This is consequential considering recent studies that demonstrated the differences between fracture in bending vs. in-plane tension. A laterally constrained thin metal plate under in-plane tension is likely to fracture under significantly lower plastic strain than the same plate under bending (with fracture initiating on the tensile side), even though both these conditions are examples of plane strain tension. Under in-plane tension fracture is preceded by a through-thickness neck, and therefore higher stress triaxiality than in the case of plane strain bending, where necking is absent. To account for these differences, we propose an extension of the GISSMO model, which relies on a simple fracture criterion based on stress-dependent fracture strain defined by the user (i.e. fracture locus). The fracture strain is typically determined experimentally using a combination of in-plane tensile tests under varying degree of lateral constraint and shear tests. The proposed extension involves defining a separate fracture locus for bending, also determined experimentally using bending tests. Fracture occurs when the equivalent plastic strain reaches its critical level represented by interpolation between the two bounding cases, i.e. bending and in-plane tension, with a bending index Ω used as an interpolation parameter.

Visualized analysis of microscale rock mechanism research: A bibliometric data mining approach

Rock mechanics is an indispensable discipline in diverse sectors, from resource retrieval to disaster mitigation. Diving deeper into this field, particularly into microscale rock mechanics, offers strategic insights and potential advancements for rock engineering practice. The objective of this research is to map the scientific production tied to microscale rock mechanics to date. In doing so, we perform a bibliometric analysis to look over and discuss the performance of the related literature. The conceptual fabric of microscale rock mechanics research, constituted by four central themes, was revealed and visualized through a text mining analysis. These areas include (1) modeling and simulation of fluid flow and transport within porous media, (2) characterizing fracture and failure in rocks, (3) understanding the deformation mechanism in response to geological processes, and (4) studying the mechanical properties of rocks subjected to extreme conditions. Lastly, an upcoming research agenda for microscale rock mechanics was proposed, centered on addressing three identified research gaps: (I) the integration of geological processes to characterize mineral properties, (II) the augmentation of fracture predictions achieved through multiscale modeling of rock heterogeneity, and (III) the exploitation of Artificial Intelligence technologies for anticipating complex fracturing scenarios. This comprehensive approach promises to enrich our understanding of rock mechanics and its applications.

Sprain energy consequences for damage localization and fracture mechanics

The 2023 smooth Lagrangian Crack-Band Model (slCBM), inspired by the 2020 invention of the gap test, prevented spurious damage localization during fracture growth by introducing the second gradient of the displacement field vector, named the "sprain," as the localization limiter. The key idea was that, in the finite element implementation, the displacement vector and its gradient should be treated as independent fields with the lowest ([Formula: see text]) continuity, constrained by a second-order Lagrange multiplier tensor. Coupled with a realistic constitutive law for triaxial softening damage, such as microplane model M7, the known limitations of the classical Crack Band Model were eliminated. Here, we show that the slCBM closely reproduces the size effect revealed by the gap test at various crack-parallel stresses. To describe it, we present an approximate corrective formula, although a strong loading-path dependence limits its applicability. Except for the rare case of zero crack-parallel stresses, the fracture predictions of the line crack models (linear elastic fracture mechanics, phase-field, extended finite element method (XFEM), cohesive crack models) can be as much as 100% in error. We argue that the localization limiter concept must be extended by including the resistance to material rotation gradients. We also show that, without this resistance, the existing strain-gradient damage theories may predict a wrong fracture pattern and have, for Mode II and III fractures, a load capacity error as much as 55%. Finally, we argue that the crack-parallel stress effect must occur in all materials, ranging from concrete to atomistically sharp cracks in crystals.

Tensile fracture prediction of 3D-printed V-notched PLA specimens: Application of VIMC-MEMC in conjunction with brittle fracture criteria

This study investigates the fracture behavior of additively manufactured round-tip V-notched diagonally loaded square plate (RV-DLSP) specimens with different notch opening angles and tip radii produced from the Polylactic acid (PLA) with [0/90/45/-45]s raster orientation using the fused deposition modeling (FDM) technique. PLA is known for its ductile behavior, making it a suitable material for various engineering applications. Also, the layer-wise manufacturing process in FDM technique makes the PLA specimens be actually anisotropic. To take the nonlinearity and anisotropy of PLA simultaneously into consideration in prediction of the fracture loads of RV-DLSP specimens, the present study employs the Virtual Isotropic Material Concept (VIMC) and modified Equivalent Material Concept (MEMC) in a two-level strategy of simplifying PLA to an isotropic linear elastic virtual material. Then, the mean stress (MS) and maximum tangential stress (MTS) criteria are utilized to estimate the fracture loads. The theoretical estimations reveal that the VIMC-MEMC-RV-MS criterion is the most accurate model, demonstrating its effectiveness in predicting the fracture behavior of RV-DLSP specimens. Additionally, the VIMC-MEMC-RV-MTS criterion shows commendable accuracy, particularly for the specimens with 30 (deg.) notch opening angle. The scanning electron microscopy (SEM) analysis of the fracture surfaces provides further insights into the fracture mechanisms of RV-DLSP specimens. Notably, distinct fracture patterns are observed based on variations in the notch geometry. Specimens with smaller notch tip radii exhibit fiber cleavage, while those with larger radii display greater fiber interpenetration. These SEM observations are consistent with the fracture load data, which indicates higher fracture loads with increasing the notch opening angle and tip radius. By integrating VIMC and MEMC with the two fracture criteria, accurate predictions of the notch fracture toughness can be achieved, facilitating the design and optimization of 3D-printed PLA components against fracture.

Determination of distal fibula fracture for prediction of ankle injuries

Objective This study quantified the local fracture tolerance of the distal fibula. The purpose of this data is to refine the understanding of ankle fracture tolerance in the population and enhance injury prediction by computational tools. Methods Fracture patterns of the fibula were obtained from the US National Highway Traffic Safety Administration (NHTSA) Crash Injury Research Engineering Network (CIREN). Of 143 cases of ankle injury, 120 included fibula fracture, many of which accompanied by radiology images. The most common fibula fracture type was a Weber C fracture, which was then used as the testing target to replicate with postmortem human surrogates. Isolated distal fibulae (male and female, across a wide anthropometric range) were subjected to quasi-static lateral-medial four-point bending superimposed on axial precompression. Results Of the 20 specimens tested, 17 fractured in compression and bending, two fractured in compression, bending, and shear, and one did not fracture upon the imposed displacement. Fractures occurring outside of the target testing span were treated as right-censored data points. Fracture patterns varied among specimens, with oblique fractures being most common, followed by segmental fractures. At failure, compressive force ranged from 77 N to 370 N and bending moment from 17 Nm to 47 Nm. Conclusions From the 19 fractured specimens, the variety of fracture patterns observed were generally consistent with the range of fibula fracture types and locations commonly observed in the field. While comparative studies with fibula specimens are largely absent from literature, comparison of this study to other long bones (radius and ulna) show that bending moment at fracture is similar. To the author’s knowledge, this is the first study to quantify the fracture tolerance of isolated distal fibulae under loading conditions representative of ankle injury mechanisms in motor vehicle collisions. By combining the results of this study with complementary results from parallel tibia-focused experiments (currently in-press), these results will aid development of tissue-level lower leg fracture prediction of human body models, thus enhancing prediction of ankle injury during safety assessment simulations. Specifically, the fracture tolerance information might generate an injury risk function to guide tissue-level injury prediction with human body models.