Search Results

Abstract For taking counter measures in advance to prevent accidental risks, it is of significance to explore the causes and evolutionary mechanism of ship collisions. This article collects 70 ship collision accidents in Zhejiang coastal waters, where 60 cases are used for modeling while 10 cases are used for verification (testing). By analyzing influencing factors (IFs) and causal chains of accidents, a Bayesian network (BN) model with 19 causal nodes and 1 consequential node is constructed. Parameters of the BN model, namely the conditional probability tables (CPTs), are determined by mathematical statistics methods and Bayesian formulas. Regarding each testing case, the BN model’s prediction on probability of occurrence is above 80% (approaching 100% indicates the certainty of occurrence), which verifies the availability of the model. Causal analysis based on the backward reasoning process shows that H (Human error) is the main IF resulting in ship collisions.

Abstract Compared to other age groups, older adults are at more significant risk of hip fracture when they fall. In addition to the higher risk of falls for the elderly, fear of falls can reduce this population’s outdoor activity. Various preventive solutions have been proposed to reduce the risk of hip fractures ranging from wearable hip protectors to indoor flooring systems. A previously developed rubberized asphalt mixture demonstrated the potential to reduce the risk of head injury. In the current study, the capability of the rubberized asphalt sample was evaluated for the risk of hip fracture for an average elderly male and an average elderly female. A previously developed human body model was positioned in a fall configuration that would give the highest impact forces toward regular asphalt.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract In order to enhance the path-tracking accuracy and adaptability of the electric flatbed vehicle (EFV) in the soft-target vehicle test system, an improved controller is designed based on the linear quadratic regulator (LQR) algorithm. First, the LQR feedback controller is designed based on the EFV dynamics tracking error model, and the genetic algorithm is utilized to obtain the optimal weight coefficient matrix for different speeds. Second, a weight coefficient matrix compensation strategy is proposed to address the changes in the relationship between the vehicle–road position and attitude caused by external disturbances and the state of EFV. An offline parameter table is created to reduce the computational load on the microcontroller of EFV and enhance real-time path-tracking performance. Furthermore, an adaptive preview time control strategy is added to reduce the overshooting caused by control delay. This strategy is based on road curvature and traveling speed.

Abstract In this study, a novel assessment approach of in-vehicle speech intelligibility is presented using psychometric curves. Speech recognition performance scores were modeled at an individual listener level for a set of speech recognition data previously collected under a variety of in-vehicle listening scenarios. The model coupled an objective metric of binaural speech intelligibility (i.e., the acoustic factors) with a psychometric curve indicating the listener’s speech recognition efficiency (i.e., the listener factors). In separate analyses, two objective metrics were used with one designed to capture spatial release from masking and the other designed to capture binaural loudness. The proposed approach is in contrast to the traditional approach of relying on the speech recognition threshold, the speech level at 50% recognition performance averaged across listeners, as the metric for in-vehicle speech intelligibility.

Abstract Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates.

Abstract The sinking and trimming of the hull in the channel would directly affect the handling and navigation safety of the ship. In view of the ship sinking, a series of empirical formulas to estimate the subsidence have been put forward for vessel in spacious shallow water areas. However, most of the equations are based on seagoing vessels. They are not suitable for inland ships with small scales, shallow drafts, and narrow navigation width. Till now, research on ship squat in intermediate channel has not yielded more practical results. Here, a generalized physical model is used to study the sinking of 500t class ships in restricted intermediate channel under different channel widths, water depths, and speeds. The main factors affecting the squat are analyzed, the empirical relation is compared with the measured squat. The Barrass equation is modified, and the calculation relation of the settlement suitable for inland river ships is proposed.

Abstract A research program has been launched in Iran to develop an evaluation method for comparing the safety performance of vehicles in real-world collisions with crash test results. The goal of this research program is to flag vehicle models whose safety performance in real-world accidents does not match their crash test results. As part of this research program, a metric is needed to evaluate the severity of side impacts in crash tests and real-world accidents. In this work, several vehicle-based metrics were analyzed and calculated for a dataset of more than 500 side impact tests from the NHTSA crash test database. The correlation between the metric values and the dummy injury criteria was studied to find the most appropriate metric with the strongest correlation coefficient values with the dummy injury criteria.

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.

Abstract Computational and experimental studies have been undertaken to investigate injurious head-first impacts (HFI), which can occur during automotive rollovers. Recent studies assume a torso surrogate mass (TSM) boundary condition, wherein the first or first two thoracic vertebrae are potted and constrained to only move in the vertical loading direction. The TSM boundary condition has not been compared with a full body (FB) model computationally or experimentally for HFI. In this study, the Global Human Body Models Consortium 50th percentile male detailed human body model (M50-O, Version 6.0) was applied to compare the kinematic, kinetic, and injury response of an HFI with a TSM boundary condition (M50-TSM), and a full body boundary condition (M50-FB). Impacts (to M50-TSM and M50-FB) were simulated between the head and a rigid plate using a commercial FE code (LS-DYNA).

Abstract Rib fractures are associated with high rates of morbidity and mortality. Improved methods to assess rib bone quality are needed to identify at-risk populations. Quantitative computed tomography (QCT) can be used to calculate volumetric bone mineral density (vBMD) and bone mineral content (BMC), which may be related to rib fracture risk. The objective of this study was to determine if vBMD and BMC from QCT predict human rib structural properties. 127 mid-level (5th–7th) ribs were obtained from adult female (n = 67) and male (n = 60) postmortem human subjects (PMHS). Isolated rib QCT scans were performed to calculate vBMD and BMC.

Abstract Traumatic brain injury is a leading cause of global death and disability. Clinically relevant large animal models are a vital tool for understanding the biomechanics of injury, providing validation data for computation models, and advancing clinical translation of laboratory findings. It is well-established that large angular accelerations of the head can cause TBI, but the effect of head impact on the extent and severity of brain pathology remains unclear. Clinically, most TBIs occur with direct head impact, as opposed to inertial injuries where the head is accelerated without direct impact. There are currently no active large animal models of impact TBI. Sheep may provide a valuable model for studying TBI biomechanics, with relatively large brains that are similar in structure to that of humans. The aim of this project is to develop an ovine model of impact TBI to study the relationships between impact mechanics and brain pathology.

Abstract Objective: This study aimed to optimize restraint systems and improve safety equity by using parametric human body models (HBMs) and vehicle models accounting for variations in occupant size and shape as well as vehicle type. Methodology: A diverse set of finite element (FE) HBMs were developed by morphing the GHBMC midsize male simplified model into statistically predicted skeleton and body shape geometries with varied age, stature, and body mass index (BMI). A parametric vehicle model was equipped with driver, front passenger, knee, and curtain airbags along with seat belts with pretensioner(s) and load limiter and has been validated against US-NCAP results from four vehicles (Corolla, Accord, RAV4, F150). Ten student groups were formed for this study, and each group picked a vehicle model, occupant side (driver vs. passenger), and an occupant model among the 60 HBMs.

Eighteen research posters were prepared and presented by student authors at the 18th Annual Injury Biomechanics Symposium. The posters covered a wide breadth of works-in-progress and recently completed projects.

Abstract Letter from the Special Issue Editors

Abstract In this study, a parametric thoracic spine (T-spine) model was developed to account for morphological variations among the adult population. A total of 84 CT scans were collected, and the subjects were evenly distributed among age groups and both sexes. CT segmentation, landmarking, and mesh morphing were performed to map a template mesh onto the T-spine vertebrae for each sampled subject. Generalized procrustes analysis (GPA), principal component analysis (PCA), and linear regression analysis were then performed to investigate the morphological variations and develop prediction models. A total of 13 statistical models, including 12 T-spine vertebrae and a spinal curvature model, were combined to predict a full T-spine 3D geometry with any combination of age, sex, stature, and body mass index (BMI). A leave-one-out root mean square error (RMSE) analysis was conducted for each node of the mesh predicted by the statistical model for every T-spine vertebra.

Abstract Introduction: The use of less lethal impact munitions (LLIMs) by law enforcement has increased in frequency, especially following nationwide protests regarding police brutality and racial injustice in the summer of 2020. There are several reports of the projectiles causing severe injuries when they penetrate the skin including pulmonary contusions, bone fractures, liver lacerations, and, in some cases, death. The penetration threshold of skin in different body regions is due to differences in the underlying structure (varying degree of muscle, adipose tissue, and presence or absence of bone). Objective: The objective of this study was to further investigate what factors affected the likelihood of skin penetration in various body regions and to develop corresponding penetration risk curves.

Abstract Compressive impacts on the cervical spine can result in bony fractures. Bone fragments displaced into the spinal canal produce spinal canal occlusion, increasing the potential for spinal cord injury (SCI). Human body models (HBMs) provide an opportunity to investigate SCI but currently need to be improved in their ability to model compression fractures and the resulting material flow. Previous work to improve fracture prediction included the development of an anisotropic material model for the bone (hard tissues) of the vertebrae assessed in a functional spinal unit (FSU) model. In the FSU model, bony failure was modeled with strain-based element erosion, with a limitation that material that could occlude the spinal canal during compression was removed when an element was eroded.