Coseismic and postseismic deformation from the 2007 Bengkulu earthquake based on GPS Data

On September 12, 2007, a M8.5 megathrust earthquake occurred along the Sunda trench near Bengkulu, West Sumatra. GPS data in Sumatra have indicated the coseismic and postseismic deformations resulting from this earthquake. Our estimate of coseismic displacements suggests that the earthquake displaced up to ~1.8m at GPS stations located north of the epicenter. Moreover, our principal strain estimation in the region suggests that the maximum coseismic extensional strain is ~40 ppm. Our analysis of GPS data in the region suggests that the postseismic decay of the 2007 Bengkulu earthquake was 46 days, estimated using a logarithmic function.

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

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 characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for $$\varvec{{\varepsilon }}_{\boldsymbol{p1}}$$ ε p 1 and $$\varvec{{\varepsilon }}_{\boldsymbol{p2}}$$ ε p 2 , respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

Experimental Study on the Mechanical Behavior and Failure Characteristics of Layered Coal at Medium Strain Rates

The study of the mechanical properties and failure behaviors for coal with different bedding structures at various medium strain rates is of great importance for revealing the mechanism of rock burst. In our study, we systematically analyze the uniaxial compressive strength (UCS), acoustic emission (AE) characteristics, failure pattern, and risk of rock burst on coal specimens with two bedding orientations under ranged in strain rates from 10−4 s−1 to 10−2 s−1. The results reflect that and the bedding direction and the strain rates significantly affect the UCS and failure modes of coal specimens. The UCS of coal specimens with loading directions perpendicular to bedding planes (horizontal bedding) increases logarithmically with increasing strain rate while the UCS increases first and then decreases of coal specimens with loading directions parallel to bedding planes (vertical bedding). The AE cumulative energy of the specimens with horizontal bedding is an order of magnitude higher than that of specimens with vertical bedding. However, it is independent of the strain rates. The energy release rates of these two types of bedded coal specimens increase in a power function as the strain rate increases. The coal specimens with horizontal bedding show violent failure followed by the ejection of fragments, indicating a high risk of rock burst. On the other hand, the coal specimens with vertical bedding exhibit a tensile splitting failure with a low risk of rock burst. Strain localization is a precursor of coal failure, and the concentration area of local principal strain is highly consistent with the initial damage area, and the area where the principal strain gradient is significantly increased corresponds to the fracture initiation area.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Biomechanical evaluation of the human mandible after temporomandibular joint replacement under different biting conditions

Temporomandibular joint (TMJ) replacement with an implant is only used when all other conservative treatments fail. Despite the promising short-term results, the long-term implications of TMJ replacement in masticatory function are not fully understood. Previous human and animal studies have shown that perturbations to the normal masticatory function can lead to morphological and functional changes in the craniomaxillofacial system. A clearer understanding of the biomechanical implications of TMJ replacement in masticatory function may help identify design shortcomings that hinder their long-term success. In this study, patient-specific finite element models of the intact and implanted mandible were developed and simulated under four different biting tasks. In addition, the impact of re-attaching of the lateral pterygoid was also evaluated. The biomechanics of both models was compared regarding both mandibular displacements and principal strain patterns. The results show an excessive mediolateral and anteroposterior displacement of the TMJ implant compared to the intact joint in three biting tasks, namely incisor (INC), left moral (LML), and right molar (RML) biting. The main differences in principal strain distributions were found across the entire mandible, most notably from the symphysis to the ramus of the implanted side. Furthermore, the re-attachment of the lateral pterygoid seems to increase joint anteroposterior displacement in both INC, LML and RML biting while reducing it during LGF. Accordingly, any new TMJ implant design must consider stabilising both mediolateral and anteroposterior movement of the condyle during biting activities and promoting a more natural load transmission along the entire mandible.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Comparison of dynamic response and maximum principal strain of diagnosed concussion in professional men’s rugby league

Rugby league has been identified as a contact sport with a high incidence of concussion. Research has been conducted to describe incidence and mechanisms of concussion in rugby league, however the risks associated with injury events (shoulder, hip, head to head) are unknown. The purpose of this study was to describe the common injury events leading to concussion in the National Rugby League and compare these events through analysis of dynamic response and brain tissue deformation. Twenty-seven impact videos of concussive injuries were physically reconstructed to obtain linear and rotational accelerations of the head. Dynamic response data were input into the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strain (MPS). Head-to-head events produced a short duration event with an average peak linear and peak rotational acceleration of 205 g and 15,890 rad/s2, respectively, which were significantly greater than the longer duration hip-to-head (24.7 g and 2650 rad/s2) and shoulder-to-head (24.2 g and 3280 rad/s2) impacts. There were no differences in MPS between events. These results suggest that risk of strain to the brain may be produced by short and long duration acceleration events. Thus, both of these accelerations need to be accounted for in the development of improved head and body protection in rugby. In addition, this data demonstrates that these events cause a risk of concussion requiring efforts to limit or modify how energy is transferred to the head.