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Long Term Durability of Structural Materials (Hardcover): P.J.M. Monteiro, K.P. Chong, J. Larsen-Basse, K. Komvopoulos Long Term Durability of Structural Materials (Hardcover)
P.J.M. Monteiro, K.P. Chong, J. Larsen-Basse, K. Komvopoulos
R4,215 Discovery Miles 42 150 Ships in 12 - 17 working days

""Long Term Durability of Structural Materials"" features proceedings of the workshop held at Berkeley, CA in October, 2000. It brought together engineers and scientists, who have received grants from the initiative NSF 98-42, to share their results on the study of long-term durability of materials and structures.
The major objective was to develop new methods for accelerated short-term laboratory or in-situ tests which allow accurate, reliable, predictions of the long-term performance of materials, machines and structures. To achieve this goal it was important to understand the fundamental nature of the deterioration and damage processes in materials and to develop innovative ways to model the behavior of these processes as they affect the life and long-term performance of components, machines and structures.
The researchers discussed their approach to include size effects in scaling up from laboratory specimens to actual structures. Accelerated testing and durability modeling techniques developed were validated by comparing their results with performance under actual operating conditions. The main mechanism of the deterioration discussed included environmental effects and/or exposure to loads, speeds and other operating conditions that are not fully anticipated in the original design. A broad range of deterioration damage, such as fatigue, overload, ultraviolet damage, corrosion, and wear was presented.
A broad range of materials of interest was also discussed, including the full spectrum of construction materials, metals, ceramics, polymers, composites, and coatings. Emphasis was placed on scale-dependence and history of fabrication on resulting mechanical behavior of materials.

An Introduction to Optimization (Hardcover, 5th edition): Edwin K. P. Chong, Wu-sheng Lu, Stanislaw H. Żak An Introduction to Optimization (Hardcover, 5th edition)
Edwin K. P. Chong, Wu-sheng Lu, Stanislaw H. Żak
R2,888 Discovery Miles 28 880 Ships in 9 - 15 working days

Accessible introductory textbook on optimization theory and methods, with an emphasis on engineering design, featuring MATLAB exercises and worked examples Fully updated to reflect modern developments in the field, the Fifth Edition of An Introduction to Optimization fills the need for an accessible, yet rigorous, introduction to optimization theory and methods, featuring innovative coverage and a straightforward approach. The book begins with a review of basic definitions and notations while also providing the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization, and applications of optimization in machine learning. Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and practice problems that reinforce the discussed theory and algorithms. The Fifth Edition features a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games, projected gradient algorithms, machine learning, and numerous new exercises at the end of each chapter. An Introduction to Optimization includes information on: The mathematical definitions, notations, and relations from linear algebra, geometry, and calculus used in optimization Optimization algorithms, covering one-dimensional search, randomized search, and gradient, Newton, conjugate direction, and quasi-Newton methods Linear programming methods, covering the simplex algorithm, interior point methods, and duality Nonlinear constrained optimization, covering theory and algorithms, convex optimization, and Lagrangian duality Applications of optimization in machine learning, including neural network training, classification, stochastic gradient descent, linear regression, logistic regression, support vector machines, and clustering. An Introduction to Optimization is an ideal textbook for a one- or two-semester senior undergraduate or beginning graduate course in optimization theory and methods. The text is also of value for researchers and professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

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