Oasys Greta v19.3.5.0 [Size: 102 MB] ...
Oasys Greta is a tool for civil and geotechnical engineers that provides comprehensive stability analysis for gravity retaining walls, and forms part of our Geotechnical Suite Pro and Geotechnical Suite bundles.

For the stability analysis of gravity retaining wall design, Oasys Greta provides users with the ability to validate and standardise in-house calculations across projects.

Civil and geotechnical engineers responsible for routine gravity retaining wall design use Oasys Greta as it provides a validated, robust and user-friendly method of calculations, ensuring acceptable and appropriate QA and QC standards are met.

Oasys Safe v19.1.1.31 [Size: 69 MB] ... Solve your geotechnical problem with powerful two-dimensional geotechnical finite element design software. Whether you need to do two-dimensional finite element computations in plane stress, plane strain, or axial symmetry, Safe takes care of it. The smart geotechnical finite element analysis software identifies pore pressures and effective stresses separately and includes gravitational loads and initial stresses.

Features: Analysis of plane stress, plane strain or axially symmetric problems. General loading of linear elastic axisymmetric structures can be carried out using a Fourier series technique. + Linear or non-linear behaviour. + Gravity loading. + Pore pressures and effective stresses are identified separately, allowing computation for drained or undrained conditions, and time dependent consolidation. + Incremental loading and changes of material properties, permitting the formation of excavations, embankments etc. + Intermediate results can be stored and inspected before the problem is continued further. + Pressure (distributed) and line loading. + Fixed or spring restraints. + 8 noded quadrilateral elements. These can be curvilinear. + Consolidation problems. + Wells. + Seepage and flow. + Extensive graphical input options and in-built mesh generation facility.

Oasys Slope v21.0.40.0 [Size: 120.6 MB] ...
Oasys Slope is a tool for civil and geotechnical engineers which provides an easy and accurate way to study a slip surface, finding factors of safety against failure, and to check suggested improvements from reinforcement.

Slope is a core component of our Ground Movement Bundle and Geotechnical Suite, both in Pro and Basic versions. Oasys Slope empowers civil and geotechnical engineers to study and check their projects involving slope stability analyses, a key part of linear infrastructure design involving cuttings for permanent works, for example.

Oasys Slope empowers civil and geotechnical engineers to study and check their projects involving slope stability analyses, a key part of linear infrastructure design involving cuttings for permanent works, for example.

Civil and geotechnical engineers responsible for studying the global stability of reinforced earth structures, cuttings and more use Oasys Slope as it provides a validated, robust and user-friendly method of calculations, ensuring acceptable and appropriate QA and QC standards are met.

Oasys XDisp v20.2.3.0 [Size: 121 MB] ... Oasys XDisp Pro is used to undertake and produce building, utilities and rail damage assessments, can undertake a multi-stage construction analysis without the need for multiple models, and is a core component of the Ground Movement Bundle Pro and the Geotechnical Suite Pro.

Oasys XDisp Pro is used by geotechnical and civil engineers on projects that require the need to control, limit and mitigate excessive ground displacements which may have a detrimental effect on existing or future builds.

Geotechnical and civil engineers overseeing ground movement assessments such as part of linear infrastructure design use Oasys XDisp Pro as it provides a validated, robust and user-friendly method of calculations, ensuring acceptable and appropriate QA and QC standards are met.

Combined with Oasys PDisp, geotechnical engineers can undertake comprehensive building and utilities damage assessments, including multi-stage construction analysis.

Igor Griva, Stephen G. Nash, Ariela Sofer ... 764 pages - Publisher: Society for Industrial Mathematics; 2nd edition (December, 2008) ... Language: English - ISBN-10: 0898716616 - ISBN-13: 978-0898716610.

This book introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines. Part I of Linear and Nonlinear Optimization, Second Edition provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. Part II on linear programming and Part III on unconstrained optimization can be used together or separately, and Part IV on nonlinear optimization can be taught without having studied the material in Part II. In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics. Three appendices provide information on linear algebra, other fundamentals, and software packages for optimization problems. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.

Audience: This book is primarily intended for use in linear and nonlinear optimization courses for advanced undergraduate and graduate students. It is also appropriate as a tutorial for researchers and practitioners who need to understand the modern algorithms of linear and nonlinear optimization to apply them to problems in science and engineering. Contents: Preface; Part I: Basics; Chapter 1: Optimization Models; Chapter 2: Fundamentals of Optimization; Chapter 3: Representation of Linear Constraints; Part II: Linear Programming; Chapter 4: Geometry of Linear Programming; Chapter 5: The Simplex Method; Chapter 6: Duality and Sensitivity; Chapter 7: Enhancements of the Simplex Method; Chapter 8: Network Problems; Chapter 9: Computational Complexity of Linear Programming; Chapter 10: Interior-Point Methods of Linear Programming; Part III: Unconstrained Optimization; Chapter 11: Basics of Unconstrained Optimization; Chapter 12: Methods for Unconstrained Optimization; Chapter 13: Low-Storage Methods for Unconstrained Problems; Part IV: Nonlinear Optimization; Chapter 14: Optimality Conditions for Constrained Problems; Chapter 15: Feasible-Point Methods; Chapter 16: Penalty and Barrier Methods; Part V: Appendices; Appendix A: Topics from Linear Algebra; Appendix B: Other Fundamentals; Appendix C: Software; Bibliography; Index

Wenyu Sun, Ya-Xiang Yuan ... 688 pages - Publisher: Springer; (May, 2006) ... Language: English - ISBN-10: 0387249753 - ISBN-13: 978-0387249759.

This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea’s motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference.

Dusan Teodorovic, Milan Janic ... 900 pages - Publisher: Butterworth-Heinemann; (October, 2016) - Language: ‎English - ISBN-10: 0128038187 - ISBN-13: 978-0128038185.

Transportation Engineering: Theory, Practice and Modeling is a guide for integrating multi-modal transportation networks and assessing their potential cost and impact on society and the environment. Clear and rigorous in its coverage, the authors begin with an exposition of theory related to traffic engineering and control, transportation planning, and an evaluation of transportation alternatives that is followed by models and methods for predicting travel and freight transportation demand, analyzing existing and planning new transportation networks, and developing traffic control tactics and strategies. Written by an author team with over thirty years of experience in both research and teaching, the book incorporates both theory and practice to facilitate greener solutions.

Richard W. Hamming ... Publisher: ‎Dover Publications; (November, 2004) - Language:‎ English - 880 pages - ISBN-10:‎ 0486439453 - ISBN-13:‎ 978-0486439457.

Understanding calculus is vital to the creative applications of mathematics in numerous areas. This text focuses on the most widely used applications of mathematical methods, including those related to other important fields such as probability and statistics. The four-part treatment begins with algebra and analytic geometry and proceeds to an exploration of the calculus of algebraic functions and transcendental functions and applications. In addition to three helpful appendixes, the text features answers to some of the exercises. Appropriate for advanced undergraduates and graduate students, it is also a practical reference for professionals. 1985 edition. 310 figures. 18 tables.

Jon Krohn, Grant Beyleveld, Aglaé Bassens ... Publisher: ‎ Addison-Wesley Professional; (September, 2019) - Language: ‎ English - 416 pages - ISBN-10: ‎ 0135116694 - ISBN-13: ‎ 978-0135116692.

Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience.

Part I’s high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and illuminated with hands-on Python code. Theory is supported with practical “run-throughs” available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming. To help readers accomplish more in less time, the authors feature several of today’s most widely-used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch, and the recently-released high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.

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