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This monograph presents an original approach to structural reliability from the perspective of statistical learning theory. It proposes new methods for solving the reliability problem utilizing the recent developments in computational learning theory, such as neural networks and support vector machines. It also demonstrates important issues on the management of samples in Monte Carlo simulation for structural reliability analysis purposes and examines the treatment of the structural reliability problem as a pattern recognition or classification task. This carefully written monograph is aiming at researchers and students in civil and mechanical engineering, especially in reliability engineering, structural analysis, or statistical learning.
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