Problem Solving In Foundation Engineering Using Foundationpro

Author: Mohammad Yamin
Publisher: Springer
ISBN: 3319176501
Size: 76.16 MB
Format: PDF, Docs
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This book is at once a supplement to traditional foundation engineering textbooks and an independent problem-solving learning tool. The book is written primarily for university students majoring in civil or construction engineering taking foundation analysis and design courses to encourage them to solve design problems. Its main aim is to stimulate problem solving capability and foster self-directed learning. It also explains the use of the foundationPro software, available at no cost, and includes a set of foundation engineering applications. Taking a unique approach, Dr. Yamin summarizes the general step-by-step procedure to solve various foundation engineering problems, illustrates traditional applications of these steps with longhand solutions, and presents the foundation Pro solutions. The special structure of the book allows it to be used in undergraduate and graduate foundation design and analysis courses in civil and construction engineering. The book stands as valuable resource for students, faculty and practicing professional engineers. This book also: Maximizes reader understanding of the basic principles of foundation engineering: shallow foundations on homogeneous soils, single piles, single drilled shafts, and mechanically stabilized earth walls (MSE) Examines bearing capacity and settlement analyses of shallow foundations considering varying elastic moduli of soil and foundation rigidity, piles, and drilled shafts Examines internal and external stabilities of mechanically stabilized earth walls with varying horizontal spacing between reinforcing strips with depth Summarizes the step-by-step procedure needed to solve foundation engineering problems in an easy and systematic way including all necessary equations and charts

The Rise Of American Research Universities

Author: Hugh Davis Graham
Publisher: JHU Press
ISBN: 9780801854255
Size: 28.33 MB
Format: PDF, Mobi
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Before the Second World War, few universities in the United States had earned high respect among the international community of scholars and scientists. Since 1945, however, the distinctive attributes of American higher education—decentralized administration, pluralistic and research-minded faculties, and intense competition for government funding—have become world standard. Whether measured by Nobel and other prizes, international applications for student admissions and faculty appointments, or the results of academic surveys, America's top research universities are the best in the world. The Rise of American Research Universities provides a fresh historical interpretation of their ascendancy and a fresh, comprehensive estimate of their scholarly achievement. Hugh Davis Graham and Nancy Diamond question traditional methods of rating the reputation and performance of universities; they offer instead an empirical analysis of faculty productivity based on research grants received, published research, and peer approval of that work. Comparing the research achievements of faculty at more than 200 institutions, they differ with most studies of higher education in measuring performance in every academic field—from medicine to humanities—and in analyzing data on research activity in terms of institutional size. In this important and timely work, Graham and Diamond reassess the success of American universities as research institutions and the role of public funding in their developmentfrom the expansionist "golden years" of the 1950s and '60s, through the austerity measures of the 1970s and the entrepreneurial ethos of the 1980s, to the budget crises universities face in the 1990s.

Handbook On Neural Information Processing

Author: Monica Bianchini
Publisher: Springer Science & Business Media
ISBN: 3642366570
Size: 60.84 MB
Format: PDF, ePub
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This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.