The field of monitoring is one of rapid development. Advances in sensor technologies, in data communication paradigms and data processing algorithms all influence the possibilities of Structural Health Monitoring, damage detection, traffic monitoring and other implementations of monitoring systems. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining the serviceability and deterioration of bridges. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening work. Many bridges constitute a bottleneck in the transport network they serve with few or no alternative routes. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and three appended papers. The thesis presents advances in sensor technology, damage identification algorithms and Bridge Weigh-In-Motion techniques. Two case studies are carried out. In the first a bridge and traffic monitoring system is implemented in a highway suspension bridge to study the cause of unexpected wear in the bridge bearings. In the second a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. / QC 20111122

Collegiate and elite female athletes have been identified as a subpopulation at heightened risk for disordered eating and pathogenic weight management practices. It was hypothesized that this increases risk may be related to sport specific pressures (such as team conducted weigh-ins), or the use and frequency of self-weighing. It appears that mandatory, team conducted weigh-ins are not salient to female athletes in regards to experiencing internalization, body image concerns, dietary restraint, negative affect, and bulimic symptomatology. Results, however, indicate that frequency of engagement in self-weighing may be influential in the engagement of disordered eating symptoms. Specifically, athletes who weighed themselves three or more times per week reported significantly more internalization of general societal ideals and athletic body ideals. For body image concerns, athletes who weighed three or more times per week reported being more concerned with their body size/shape than all others. With respect to dietary behaviors, athletes who weighed themselves three or more times per week reported engaging in significantly more caloric restriction than did those who weighed less frequently. For negative affect, the athletes who weighed themselves three or more times per week reported significantly higher levels of both anger and guilt. Finally for bulimic symptomatology, athletes who weighed themselves three or more times a week had significantly higher levels than those who weighed once or twice or not at all.

Submitted by Edisangela Bastos (edisangela@ufpa.br) on 2015-01-27T18:33:19Z No. of bitstreams: 2 license_rdf: 22974 bytes, checksum: 99c771d9f0b9c46790009b9874d49253 (MD5) Dissertacao_PesagemMovimentoCaracterizacao.pdf: 5817774 bytes, checksum: bffe4c7106d0747f52959b54504702a0 (MD5) / Rejected by Edisangela Bastos (edisangela@ufpa.br), reason: on 2015-01-29T18:13:44Z (GMT) / Submitted by Edisangela Bastos (edisangela@ufpa.br) on 2015-01-30T17:50:00Z No. of bitstreams: 2 license_rdf: 22974 bytes, checksum: 99c771d9f0b9c46790009b9874d49253 (MD5) Dissertacao_PesagemMovimentoCaracterizacao.pdf: 5817774 bytes, checksum: bffe4c7106d0747f52959b54504702a0 (MD5) / Approved for entry into archive by Albirene Aires (albireneufpa@gmail.com) on 2015-02-02T12:06:43Z (GMT) No. of bitstreams: 2 license_rdf: 22974 bytes, checksum: 99c771d9f0b9c46790009b9874d49253 (MD5) Dissertacao_PesagemMovimentoCaracterizacao.pdf: 5817774 bytes, checksum: bffe4c7106d0747f52959b54504702a0 (MD5) / Made available in DSpace on 2015-02-02T12:06:43Z (GMT). No. of bitstreams: 2 license_rdf: 22974 bytes, checksum: 99c771d9f0b9c46790009b9874d49253 (MD5) Dissertacao_PesagemMovimentoCaracterizacao.pdf: 5817774 bytes, checksum: bffe4c7106d0747f52959b54504702a0 (MD5) Previous issue date: 2014 / Nesta dissertação é apresentado o desenvolvimento de algoritmos para aplicação do método Bridge-Weigh In Motion (B-WIM) para a pesagem em movimento de trens e para a caracterização do tráfego ferroviário, permitindo-se obter informações sobre a velocidade de passagem dos trens, número e espaçamento entre eixos. Os sistemas B-WIM a partir de uma simples instrumentação permitem determinar as cargas por eixo de veículos em movimento, eliminando o efeito dinâmico. Foram implementados os algoritmos para a determinação dos valores referentes a geometria do trem e das cargas, que foi validado a partir de um exemplo teórico, onde se simulou a passagem de um trem de características conhecidas sobre a ponte e as cargas por eixos foram determinadas com 100% de exatidão. Além disso, foi feito um exemplo numérico em elementos finitos, de um viaduto em concreto armado para aplicação do método, onde foi feita a determinação das cargas por eixo para diferentes velocidades de passagem do trem. A fim de reduzir o tempo de processamento nas análises do exemplo numérico, foi desenvolvido um algoritmo para a geração de cargas nodais no modelo numérico que reduziram o tempo de processamento em até 96% quando comparado com a análise de múltiplos passos (“Multi-Step”), que simula automaticamente a passagem do trem sobre a estrutura. Finalmente, o método foi testado em um caso real a partir de monitorações realizadas em um viaduto de concreto armado da Estrada de Ferro Carajás. Apesar de não ter sido possível a determinação das cargas por eixo da locomotiva, foi possível medir precisamente o peso bruto total da locomotiva quando se utilizou o modelo constitutivo de Collins & Mitchell (1991) para o concreto. / This Master’s Thesis presents the development of algorithms for application of Bridge- Weigh In Motion (B-WIM) for weighing moving trains and method for the characterization of rail traffic, allowing up to obtain information about the speed of passage of trains , number and axle spacing. The B-WIM systems from a simple instrumentation for determining the axle loads of moving vehicles, eliminating the dynamic effect. The algorithms for determining the values related the geometry of the train and axle loads were implemented, which was validated from a theoretical example, where we simulated the passage of a train of known characteristics over the bridge and axle loads were determined 100% accuracy. In addition, a numerical example was done in finite element method, of a reinforced concrete viaduct for application of the method, where was determine axle loads for different speeds of train passage. In order to reduce the processing time of the analysis in the numerical example, an algorithm for generating the numerical model nodal loads was implemented that have reduced processing time by 96% compared to the analysis of multi-step, which automatically simulates the passage of the train over the structure. Finally, the method was tested on a real case from monitoring tests realized on a reinforced concrete viaduct in the Carajás Railroad. Although it was not possible to determine the axle load locomotive, it was possible to measure accurately the gross weight of the locomotive when using the constitutive model of Collins and Mitchell (1991) for concrete.

The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.

The collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.

Kroppsuppfattning påverkar vår livskvalitet. Utöver att prestera på tävling behöver tyngdlyftare och styrkelyftare delta på en invägning, där deras kroppsvikt avgör vilken viktklass de tävlar i. Syftet med studien var att undersöka om det fanns ett samband mellan lyftares kroppsuppfattning och upplevelse av att vara aktiv och tävlande inom tyngdlyftning och/eller styrkelyft. Metod: 210 svenska tyngdlyftare och styrkelyftare med tävlingserfarenhet i åldrarna 15–70 år deltog i studien. Deltagarna svarade på en digital enkät som inkluderade ett av författaren nyskapat instrument om förekomst av beteenden och upplevelser av att tävla i en viktklassidrott, samt Body Esteem Scale for Adolescents and Adults om kroppsuppfattning. Resultat: Signifikant positiva korrelationer av måttlig styrka påvisades mellan kroppsuppfattning och tävlingsupplevelse, samt mellan kroppsuppfattning och viktupplevelse. Svag positiv korrelation påvisades mellan kroppsuppfattning och invägningsupplevelse. 60 % av studiedeltagarna rapporterade positiv kroppsuppfattning och 40 % rapporterade blandad/splittrad eller negativ kroppsuppfattning. Slutsats: Studiens resultat indikerar att kroppsuppfattning och upplevelse av att tävla, upplevelse av sin kroppsvikt och att tävla i viktklass(er), samt upplevelse av invägning har ett samband. Då 40 % av populationen rapporterade blandad/splittrad eller negativ kroppsuppfattning tyder resultaten på praktiska förbättringsmöjligheter där idrottares kroppsuppfattning kan förbättras. / Body image affects our quality of life. In addition to performing in competition, weightlifters and powerlifters need to participate in a weigh-in, where their body weight determines which weight class they compete in. The purpose of the study was to investigate whether there was a relationship between lifters’ body image and experience of being active and competing in weightlifting and/or powerlifting. Method: 210 Swedish weightlifters and powerlifters with competition experience aged 15–70 years participated in the study. The participants responded to a digital questionnaire that included an instrument newly created by the author on the prevalence of behaviours and experiences of competing in a weight class sport, as well as the Body Esteem Scale for Adolescents and Adults. Results: Positive correlations of statistical significance and of moderate strength were found between body image and competition experience, as well as between body image and weight experience. Weak positive correlation was demonstrated between body image and weigh-in experience. 60 % of the participants reported positive body image and 40 % reported mixed/split or negative body image. Conclusion: The results of the study indicate a relationship between body image and experience of competing, experience of one's body weight and competing in weight class(es), as well as experience of weigh-ins. As 40 % of the population reported mixed/split or negative body image, the results indicate practical opportunities where athletes’ body image can be improved.