The economic and technologic development experienced by the society in the last decades has caused the demand of a new type of faster and more comfortable transport. This type of demand has been covered by the air transport, the road transport and the railway transport. This situation where the society demands an improvement in her quality of life is the best situation for the birth of the high speed trains. Different studies carried out in the transport field have demonstrated that for distances between four hundred and one thousand of kilometres, the high speed trains provide a lower travelling times than the rest of the transports. These types of high speed trains have increased the axle loads and the average speeds, thus generally a dynamic analysis is required by the ERRI in all the railway bridges when the train speed is higher than 200 Km/h. Besides, when the train speed is going to be higher than 200 Km/h, the vibrations induced in the bridge can reduce the service life of the vehicles and structure, and generally, this fact leads to become the dynamic effect in the principal factor to take into account in order to design the structure. Therefore, an important knowledge in railway bridges dynamic is required to not to oversize the structures with the consequent economic cost. The purpose of this thesis is to study the possibility of accurately predicting the dynamic response of an existing railway bridge, subjected to the high speed train Gröna Tåget, implementing a simplified 2D finite element model with the aid of the program Abaqus. The bridge chosen is the Lögdeälv Bridge, a two spans composite bridge, located along the Bothnia Line (the new Swedish high-speed line), between the localities of Nordmaling and Rundvik. The measured eigenfrequencies due to bending modes of vibration are used for updating the model and then, these frequencies and the accelerations measured are used to compare and validate the different 2D updated models. The parameters used to update the models are; the damping coefficient of the structure, the mass and the stiffness of the bridge, and the supports stiffness. Finally it is concluded that the best model is achieved when the rotational support stiffness is modified in the two extremes supporters of the bridge.

Finite Element Model Updating has recently arisen as an issue of vast importance on the design, construction and maintenance of structures in civil engineering. Many algorithms have been proposed, developed and enhanced in order to accomplish the demands of the updating process, mainly to achieve computationally efficient programs and greater results.The present Master Thesis proposes two new algorithms to be used in Finite Element Model Updating: the Bees Algorithms (BA) and the Artificial Bee Colony algorithm (ABC). Both were first proposed in 2005, are based on the foraging behaviour of bees and have been proved to be efficient algorithms in other fields. The objective of this Master Thesis is, thus, to implement and to test these two newalgorithms in Finite Element Model Updating for a cantilever beam. The Finite Element Model and the algorithms are programmed, followed by the extraction of the experimental frequencies and the updating process. Results, comparison of these two methods and conclusions are given at the end of this report, as well as suggestions for further work.

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The National Library of Medicine (NLM), an institute in the National Institutes of Health (NIH), maintains a collection of 17,000 digitized spine X-ray images obtained from the second National Health and Nutrition Examination Survey (NHANES II). Research effort has been devoted to develop a web-accessible retrieval system that allows retrieval of images from the NHANES II database on relevant and frequently found pathologies. A comprehensive and successful image retrieval system requires effective image representation and matching methods, relevance feedback algorithms to incorporate user opinions, and efficient indexing schemes for fast access to image databases. This dissertation studies and develops approaches for all of the above areas within the context of content-Based Image Retrieval (CBIR) of spine X-ray images from the NHANES II collection. Shape is an important characteristic for describing pertinent pathologies in various types of medical images, including spine X-ray images. Retrieving images with shapes similar to a specific user query can be useful for finding pathologies exhibited in images in large survey collections. In this work, vertebral outlines are extracted for image retrieval using shape matching methods to detect the presence of anterior osteophytes. The Multiple Open Triangle (MOT) shape representation method is proposed for partial shape matching (PSM), and a Corner-Guided Dynamic Programming (DP) strategy is developed to search partial intervals for matching comparison based on a 9-point model marked by a board-certified radiologist. The MOT method demonstrates higher retrieval accuracy compared to other approaches and the retrieval speed is improved significantly through the use of Corner-Guided DP. Computer-calculated low-level image features fall short when imitating high-level human visual perception. Relevance Feedback (RF) attempts to bridge the gap between the two by analyzing and employing user feedback. The need for overcoming this gap is more evident in medical image retrieval. Existing RF approaches are analyzed and a weight-updating formula for RF is developed. A hybrid retrieval approach is proposed that utilizes both CBIR with RF and RF history. This hybrid approach uses short-term memory to store the feedback history, which contributes to the retrieval results and helps select images for user feedback. An approximate 20% average increase in retrieval recall percentage is achieved within two RF iterations. Efficient indexing methods are desired for fast database access. An agglomerative clustering algorithm is adopted to pre-index the database based on pre-calculated pair-wise distances between indexed parts. Retrieval with this pre-indexing procedure is shown to offer faster retrieval and maintain a comparable recall percentage.

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