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This dissertation presents the results of a research effort aimed at improving the current occupant injury criteria typically used to assess occupant injury risk in crashes involving roadside hardware such as guardrail. These metrics attempt to derive the risk of injury based solely on the response of the vehicle during a collision event. The primary purpose of this research effort was to determine if real-world crash injury prediction could be improved by augmenting the current vehicle-based metrics with vehicle-specific structure and occupant restraint performance measures. Based on an analysis of the responses of 60 crash test dummies in full-scale crash tests, vehicle-based occupant risk criteria were not found to be an accurate measure of occupant risk and were unable to predict the variation in occupant risk for unbelted, belted, airbag only, or belt and airbag restrained occupants. Through the use of Event Data Recorder (EDR) data coupled with occupant injury data for 214 real-world crashes, age-adjusted injury risk curves were developed relating vehicle-based metrics to occupant injury in real-world frontal collisions. A comparison of these risk curves based on model fit statistics and an ROC curve analysis indicated that the more computationally intensive metrics that require knowledge of the entire crash pulse offer no statistically significant advantage over the simpler delta-V crash severity metric in discriminating between serious and non-serious occupant injury. This finding underscores the importance of developing an improved vehicle-based injury metric. Based on an analysis of 619 full-scale frontal crash tests, adjustments to delta-V that reflect the vehicle structure performance and occupant restraint performance are found to predict 4 times the variation of resultant occupant chest acceleration than delta-V alone. The combination of delta-V, ridedown efficiency, and the kinetic energy factor was found to provide the best prediction of the occupant chest kinematics. Real-world crash data was used to evaluate the developed modified delta-V metrics based on their ability to predict injury in real-world collisions. Although no statistically significant improvement in injury prediction was found, the modified models did show evidence of improvement over the traditional delta-V metric. / Ph. D.

One of the most important data elements recorded in the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) is the vehicle change in velocity, or ?V. ?V is the vector change in velocity experienced by a vehicle during a collision, and is widely used as a measure of collision severity in crash safety research. The ?V information in NASS/CDS is used by the U.S. National Highway Traffic Safety Administration (NHTSA) to determine research needs, regulatory priorities, design crash test procedures (e.g., test speed), and to determine countermeasure effectiveness. The WinSMASH crash reconstruction code is used to compute the ?V estimates in the NASS/CDS. However, the reconstruction accuracy of the current WinSMASH version has not previously been examined for side impacts. Given the importance of side impact crash modes and the widespread use of NASS/CDS data, an assessment of the program's reconstruction accuracy is warranted. The goal of this thesis is to quantify the accuracy of WinSMASH ?V estimations for side impact crashes, and to suggest possible means of improving side impact reconstruction accuracy. Crash tests provide a wealth of controlled crash response data against which to evaluate WinSMASH. Knowing the accuracy of WinSMASH in reconstructing crash tests, we can infer WinSMASH accuracy in reconstructing real-world side crashes. In this study, WinSMASH was compared to 70 NHTSA Moving Deformable Barrier (MDB) - to - vehicle side crash tests. Tested vehicles were primarily cars (as opposed to Light Trucks and Vans, or LTVs) from model years 1997 - 2001. For each test, the actual ?V was determined from test instrumentation and this ?V was compared to the WinSMASH-reconstructed ?V of the same test. WinSMASH was found to systemically over-predict struck vehicle resultant ?V by 12% at time of vehicle separation, and by 22% at time of maximum crush. A similar pattern was observed for the MDB ?V; WinSMASH over-predicted resultant MDB ?V by 6.6% at separation, and by 23% at maximum crush. Error in user-estimated reconstruction parameters, namely Principal Direction Of Force (PDOF) error and damage offset, was controlled for in this analysis. Analysis of the results indicates that this over-prediction of ?V is caused by over-estimation of the energy absorbed by struck vehicle damage. In turn, this ultimately stems from the vehicle stiffness parameters used by WinSMASH for this purpose. When WinSMASH was forced to use the correct amount of absorbed energy to reconstruct the crash tests, systemic over-prediction of ?V disappeared. WinSMASH accuracy when reconstructing side crash tests may be improved in two ways. First, providing WinSMASH with side stiffness parameters that are correlated to the correct amount of absorbed energy will correct the systemic over-prediction of absorbed energy when reconstructing NHTSA side crash tests. Second, providing some treatment of restitution in the reconstruction process will correct the under-prediction of ?V due to WinSMASH's assumption of zero restitution. At present, this under-prediction partially masks the over-prediction of ?V caused by over-prediction of absorbed energy. If the over-prediction of absorbed energy is corrected, proper treatment of restitution will correct much of the remaining error observed in WinSMASH reconstructions of NHTSA side crash tests. / Master of Science

Event data recorders (EDRs) are an invaluable data source that have begun to, and will increasingly, provide novel insight into motor vehicle crash characteristics. The "black boxes" in automobiles, EDRs directly measure precrash and crash kinematics. This data has the potential to eclipse the many traditional surrogate measures used in vehicle safety that often rely upon assumptions and simplifications of real world crashes. Although EDRs have been equipped in passenger vehicles for over two decades, the recent establishment of regulation has greatly affected the quantity, resolution, duration, and accuracy of the recorded data elements. Thus, there was not only a demand to reestablish confidence in the data, but a need to demonstrate the potential of the data. The objectives of the research presented in this dissertation were to (1) validate EDR data accuracy in full-frontal, side-impact moving deformable barrier, and small overlap crash tests; (2) evaluate EDR survivability beyond regulatory crash tests, (3) determine the seat belt accuracy of current databases, and (4) assess the merits of other vehicle-based crash severity metrics relative to delta-v. This dissertation firstly assessed the capabilities of EDRs. Chapter 2 demonstrated the accuracy of 176 crash tests, corresponding to 29 module types, 5 model years, 9 manufacturers, and 4 testing configurations from 2 regulatory agencies. Beyond accuracy, Chapter 3 established that EDRs are anecdotally capable of surviving extreme events of vehicle fire, vehicle immersion, and high delta; although the frequency of these events are very rare on U.S. highways. The studies in Chapters 4 and 5 evaluated specific applications intended to showcase the potential of EDR data. Even single value data elements from EDRs were shown to be advantageous. In particular, the seat belt use status may become a useful tool to supplement crash investigators, especially in low severity crashes that provide little forensic evidence. Moreover, time-series data from EDRs broadens the number of available vehicle-based crash severity metrics that can be utilized. In particular, EDR data was used to calculate vehicle pulse index (VPI), which was shown to have modestly increased predictive abilities of serious injury compared to the widely used delta-v among belted occupants. Ultimately, this work has strong implications for EDR users, regulatory agencies, and future technologies. / Ph. D.

Esta dissertação propõe o desenvolvimento de uma rede neural artificial (RNA) direcionada a ambientes foundation fieldbus para realização do cálculo de vazão em dutos fechados. Para tanto, a metodologia proposta utiliza-se de medidas de pressão, temperatura e pressão diferencial, as quais normalmente estão disponíveis em plantas industriais. A principal motivação do emprego das redes neurais reside no seu baixo custo e simplicidade de implementação, o que possibilita o emprego de apenas blocos fieldbus padrões tornando a metodologia independente do fabricante. Foi utilizada uma rede perceptron multicamadas com algoritmo de treinamento backpropagation de Levenberg-Marquardt. O treinamento foi realizado numa programação elaborada para o software Matlab TM. A arquitetura da rede neural foi determinada por métodos empíricos variando-se o número de neurônios e de camadas neurais até se atingir um erro aceitável na prática. Após esses treinamentos foi desenvolvida uma programação para realizar os cálculos de vazão em um ambiente foundation fieldbus utilizando-se para tanto o software DeltaV TM do fabricante Emerson Process Management. Foram obtidos resultados com erro relativo médio de valor de vazão em torno de 1.43% para um primeiro cenário utilizando uma placa de orifício e ar como fluido, e de 0,073% para um segundo cenário utilizando uma placa de orifício e gás natural como fluido, com relação aos valores obtidos através do instrumento multivariável 3095MV TM do fabricante Rosemount. Os valores de erro encontrados validam o método desenvolvido nessa dissertação. / This dissertation proposes the development of an artificial neural network (ANN) directed to foundation fieldbus environment for calculation of flow in closed ducts. The proposed methodology uses measurements of pressure, temperature and differential pressure, which are usually available in industrial plants. The main motivation of the use of neural networks lies in their low cost and simplicity of implementation, which allows the use of standard fieldbus blocks by just making the method independent of the manufacturer. It was used a multilayer perceptron network with backpropagation training and algorithm from Levenberg-Marquardt. The training was programmed in the software Matlab TM. The architecture of the ANN was determined by empirical methods by varying the number of neurons and neural layers until it reaches an acceptable error. After such trainings, it was developed a program to perform the flow calculations in an foundation fieldbus environment using Emerson Process Management\'s DeltaV TM software. The results were obtained with an average relative error of flow rate of 1.43% for the first scenario using an orifice plate and air as a process fluid, and 0.073% for a second scenario using an orifice plate and natural gas as the fluid related to the values obtained from Rosemount 3095MV TM multivariable instrument. The values of error found validate the method developed in this dissertation.

Esta dissertação propõe o desenvolvimento de uma rede neural artificial (RNA) direcionada a ambientes foundation fieldbus para realização do cálculo de vazão em dutos fechados. Para tanto, a metodologia proposta utiliza-se de medidas de pressão, temperatura e pressão diferencial, as quais normalmente estão disponíveis em plantas industriais. A principal motivação do emprego das redes neurais reside no seu baixo custo e simplicidade de implementação, o que possibilita o emprego de apenas blocos fieldbus padrões tornando a metodologia independente do fabricante. Foi utilizada uma rede perceptron multicamadas com algoritmo de treinamento backpropagation de Levenberg-Marquardt. O treinamento foi realizado numa programação elaborada para o software Matlab TM. A arquitetura da rede neural foi determinada por métodos empíricos variando-se o número de neurônios e de camadas neurais até se atingir um erro aceitável na prática. Após esses treinamentos foi desenvolvida uma programação para realizar os cálculos de vazão em um ambiente foundation fieldbus utilizando-se para tanto o software DeltaV TM do fabricante Emerson Process Management. Foram obtidos resultados com erro relativo médio de valor de vazão em torno de 1.43% para um primeiro cenário utilizando uma placa de orifício e ar como fluido, e de 0,073% para um segundo cenário utilizando uma placa de orifício e gás natural como fluido, com relação aos valores obtidos através do instrumento multivariável 3095MV TM do fabricante Rosemount. Os valores de erro encontrados validam o método desenvolvido nessa dissertação. / This dissertation proposes the development of an artificial neural network (ANN) directed to foundation fieldbus environment for calculation of flow in closed ducts. The proposed methodology uses measurements of pressure, temperature and differential pressure, which are usually available in industrial plants. The main motivation of the use of neural networks lies in their low cost and simplicity of implementation, which allows the use of standard fieldbus blocks by just making the method independent of the manufacturer. It was used a multilayer perceptron network with backpropagation training and algorithm from Levenberg-Marquardt. The training was programmed in the software Matlab TM. The architecture of the ANN was determined by empirical methods by varying the number of neurons and neural layers until it reaches an acceptable error. After such trainings, it was developed a program to perform the flow calculations in an foundation fieldbus environment using Emerson Process Management\'s DeltaV TM software. The results were obtained with an average relative error of flow rate of 1.43% for the first scenario using an orifice plate and air as a process fluid, and 0.073% for a second scenario using an orifice plate and natural gas as the fluid related to the values obtained from Rosemount 3095MV TM multivariable instrument. The values of error found validate the method developed in this dissertation.