Structural Health Monitoring With Emphasis On Computer Vision, Damage Indices, And Statistical Analysis

Structural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches. Effective use of traffic video synchronized with sensor measurements for decision-making is demonstrated. First, some of the computer vision methods and how they can be used for bridge monitoring are presented along with the most common issues and some practical solutions. Second, a conceptual damage index (Unit Influence Line) is formulated using synchronized computer images and sensor data for tracking the structural response under various load conditions. Third, a new index, Nd , is formulated and demonstrated to more effectively identify, localize and quantify damage. Commonly observed damage conditions on real bridges are simulated on a laboratory model for the demonstration of the computer vision method, UIL and the new index. This new method and the index, which are based on outlier detection from the UIL population, can very effectively handle large sets of monitoring data. The methods and techniques are demonstrated on the laboratory model for damage detection and all damage scenarios are identified successfully. Finally, the application of the proposed methods on a real life structure, which has a monitoring system, is presented. It is shown that these methods can be used efficiently for applications such as damage detection and load rating for decision-making. The results from this monitoring project on a movable bridge are demonstrated and presented along with the conclusions and recommendations for future work

Multi-sensor physical activity measurement in early childhood

The purpose of this dissertation was to develop, validate, and implement multi-sensor approaches for measuring physical activity and social/contextual covariates in 2-5 year-old children via wearable-, wireless communication-, and infrared-depth camera-based technologies. In Chapter 2, a three-phased study design was used to validate a method for estimating metered distances between wearable devices using accelerometer-derived Bluetooth signals. Results showed that distances, up to 20 meters, can be predicted between a single Bluetooth beacon and receiver using a Random Forest algorithm. When multiple Bluetooth beacons and receivers were used within the same environment, a moving average filter was required to recover observations lost due to noise. Overall, simulation and validation data suggest that accelerometer-derived Bluetooth signals can be used in studies of physical activity co-participation to 1) estimate metered distances between devices using a single beacon-receiver paradigm, as well as to 2) estimate the proportion of time that devices are proximal when using multiple beacons and receivers. Chapter 3 characterized the relationship between objectively measured physical activity and dyadic spatial proximities in 2 year-olds and their parents. Data revealed that the overall proportions of time that children and their parents spent in total physical activity were positively associated, and time series data revealed that this relationship remained consistent when analyzed hour-to-hour. Time spent engaged in sedentary behavior was also positively associated between children and parents; however, there was no association between child and parent moderate-vigorous physical activity volumes. Dyadic proximity results showed that girls spent more time in joint physical activity with their mothers than boys. Furthermore, children who engaged in >60 minutes of daily moderate-vigorous physical activity spent an additional 30 minutes in joint total physical activity with their mothers each day, on average, when compared to children who engaged in 60 minutes of daily moderate-vigorous physical activity participated in joint physical activity with their mothers across wider relative distances, on average, than did boys who engaged in physical activity at closer relative distances to their mothers. In Chapter 4, an original computer vision algorithm was applied to infrared-depth camera data for the purpose of converting three-dimensional videos into triaxial physical activity signals in young children. Physical activity data were collected in 2-5 year-old children during 20-minute semi-structured, indoor child-parent dyadic play sessions. Play session video data were converted into triaxial physical activity signals using a multi-phased computer vision algorithm for each child. Computer vision-derived triaxial physical activity cut points for 2-5 year-olds were calibrated against a direct observation reference system using a machine learning algorithm. Results revealed that triaxial activity signals, as measured by a dual-sensor camera, can be used to estimate both physical activity intensities and volumes in young children without the use of wearable technology. Collectively, these studies show that multi-sensor approaches to physical activity measurement are a valid means by which to measure physical activity and social/contextual covariates in young children using either wearable sensors or computer vision

Autonomous Vehicle and Smart Traffic

Long-term forecasting of technology has become extremely difficult due to the rapid realization of any suggested idea. Communication and software technologies can compensate for the problems that may arise during the transition period between idea generation and realization. However, this rapid process can cause problems for the automotive industry and transportation systems.Autonomous vehicles are currently a hot topic within the transportation sector. This development is related to the compatibility of vehicles of the near future with the development of the infrastructure on which these vehicles will be based. There are certain problems regarding the solutions that are currently being worked on, such as how autonomous should vehicles be, their control mechanisms, driving safety, energy requirements, and environmental use. The problem is not just about the design of autonomous vehicles. The user transportation systems of these vehicles also need problem-free solutions. The problem should not only be seen as financial because sociological effects are an important part of this feature.In this book, valuable research on the modeling, systems, transportation, technological necessity, and logistics of autonomous vehicles is presented. The content of the book will help researchers to create ideas for their future studies and to open up the discussion of autonomous vehicles

Use of Unmanned Aerial Systems in Civil Applications

Interest in drones has been exponentially growing in the last ten years and these machines are often presented as the optimal solution in a huge number of civil applications (monitoring, agriculture, emergency management etc). However the promises still do not match the data coming from the consumer market, suggesting that the only big field in which the use of small unmanned aerial vehicles is actually profitable is the video-makers’ one. This may be explained partly with the strong limits imposed by existing (and often "obsolete") national regulations, but also - and pheraps mainly - with the lack of real autonomy. The vast majority of vehicles on the market nowadays are infact autonomous only in the sense that they are able to follow a pre-determined list of latitude-longitude-altitude coordinates. The aim of this thesis is to demonstrate that complete autonomy for UAVs can be achieved only with a performing control, reliable and flexible planning platforms and strong perception capabilities; these topics are introduced and discussed by presenting the results of the main research activities performed by the candidate in the last three years which have resulted in 1) the design, integration and control of a test bed for validating and benchmarking visual-based algorithm for space applications; 2) the implementation of a cloud-based platform for multi-agent mission planning; 3) the on-board use of a multi-sensor fusion framework based on an Extended Kalman Filter architecture

Automatic estimation of excavator actual and relative cycle times in loading operations

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator\u27s actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine\u27s performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites

Signal processing measurement of the results of the up-down hop test using sensors

The advancement of mobile technology and sensor development has profoundly impacted several sectors, including physical therapy and rehabilitation sciences. This study focuses on measuring the Up-Down Hop Test findings with sensor technologies to improve clinical assessments and rehabilitation outcomes and contribute to the growth of sports science. By incorporating sensors into mobile devices, the study investigates novel techniques for objectively analyzing data from the Up-Down Hop Test, providing a complete understanding of the patient’s lower limb function and stability. Using sensors, such as accelerometers, gyroscopes, and magnetometers, allows for the accurate capturing of movement data, which is essential for assessing the efficiency of rehabilitation programs and establishing an individual’s readiness to resume physical activity. The study describes the limitations of traditional ways of evaluating Up-Down Hop Test results, which rely on subjective assessments and physical measurements. The study uses sensor technology to overcome these issues, suggesting a more ob jective and efficient assessment process. This method improves the accuracy and reliability of test findings and facilitates the formulation of tailored rehabilitation plans based on quantitative data analysis. A thorough literature analysis offers the study’s theoretical underpinning, em phasizing the importance of the Up-Down Hop Test in physical therapy and the po tential benefits of introducing sensor technology into its evaluation. Related works are discussed, contrasting various techniques and their clinical usefulness, highlight ing the need for trustworthy, objective, and cost-effective tools for assessing athletic performance and healing. The methodology section describes the methods used to execute and evaluate the sensor-based assessment of the Up-Down Hop Test, including sensor selection, data collection protocols, and analytic approaches. Pilot tests and statistical analysis have validated the suggested method’s effectiveness, proving its ability to provide a complete knowledge of test results and guide rehabilitation efforts. The study’s findings support the viability of using sensor technology to correctly quantify Up-Down Hop Test outcomes. It provides valuable insights into the healing process and aids evidence-based decision-making in physical therapy practices. This study adds to the expanding body of knowledge about using sophisticated technology in healthcare, recommending future directions for developing more effective and tailored rehabilitation treatments.RESUMO: O estudo sobre a utilização da tecnologia de sensores para aprimorar a avaliação do Up-Down Hop Test em fisioterapia e ciências da reabilitação representa um avanço significativo na forma como abordamos a reabilitação física e a ciência do desporto. A utilização de acelerómetros, giroscópios e magnetómetros para capturar dados precisos de movimento pode revolucionar a objetividade e eficiência das avaliações clínicas, contribuindo, em última análise, para planos de reabilitação mais personalizados e eficazes. O foco em superar as limitações das avaliações subjeti vas tradicionais com essas tecnologias aborda uma necessidade crítica de métodos mais confiáveis e quantitativos na avaliação da função e estabilidade dos membros inferiores. A análise minuciosa da literatura fornece uma base sólida para a importância da integração da tecnologia de sensores nas avaliações de fisioterapia. Destaca a necessidade de ferramentas objetivas, confiáveis e custo-eficazes na avaliação do desempenho atlético e recuperação, pavimentando o caminho para uma aceitação e implementação mais amplas dessas tecnologias em ambientes clínicos. A metodologia, incluindo a seleção de sensores apropriados, o estabelecimento de protocolos de coleta de dados e a utilização de abordagens analíticas para interpretar os dados, mostra uma abordagem bem pensada para a realização deste estudo. A validação da sua metodologia através de testes piloto e análise estatística reforça o potencial das avaliações baseadas em sensores em oferecer uma compreensão mais matizada dos resultados do Up-Down Hop Test. Os resultados que destacam a viabilidade e eficácia da utilização da tecnologia de sensores para a quantificação precisa dos resultados da reabilitação são incrivelmente promissores. Esta abordagem não só ajuda no monitoramento do processo de cura, mas também melhora a tomada de decisões baseada em evidências nas práticas de terapia. Ao contribuir para o crescente corpo de conhecimento sobre a aplicação de tecnologias avançadas na saúde, o seu estudo aponta para direções futuras empolgantes para o desenvolvimento de intervenções de reabilitação mais eficientes e personalizadas. Para contribuir ainda mais para este campo, poderia ser benéfico explorar a integração de algoritmos de aprendizagem automática com os dados coletados dos sensores para uma análise e modelagem preditiva ainda mais sofisticadas dos resultados da reabilitação. Além disso, investigar os impactos a longo prazo das avaliações baseadas em sensores na recuperação dos pacientes e o potencial para a integração dessas tecnologias em programas de reabilitação em casa poderia oferecer insights valiosos sobre a sua aplicabilidade e eficácia mais amplas