Geotechnical.Passion

Alam Singh - 470 pages - Language: ‎English - Publisher: ‎CBS Publishers; (March, 2014).

It covers fundamentals of soil mechanics and foundation engineering with a lot of solved problems routine laboratory tests and also objective-type question bank. It covers the syllabus generally prescribed at the Bachelors level the AMIE examination and the regional and central services competitions.

Debashis Moitra - 1088 pages - Language: ‎English - Publisher: ‎Universities Press (India); (September, 2018).

Geotechnical Engineering combines in a single book, the theoretical aspects (Soil Mechanics) as well as the applications (Foundation Engineering). The author, with his long experience of undergraduate teaching, felt the need for a comprehensive textbook on Geotechnical Engineering that would efficiently cater to all the basic requirements of undergraduate students and proceeded to fill the gap.

The lucid explanations in the book are strengthened by: • extensive use of figures and graphs, • citing of examples, even from fields outside Geotechnical Engineering, • for better understanding of the basic ideas and principles, • solved numerical problems carefully designed to strengthen understanding, • inclusion of emerging areas of Geotechnical Engineering such as geosynthesis, • geotechnical earthquake engineering and environmental geotechniques.

Nishantha Bandara, Manjriker Gunaratne - 145 pages - Language: ‎English - Publisher: ‎Momentum Press; (February, 2018).

Familiarity with geotechnical aspects of pavement engineering is essential for the practicing pavement engineer. When designing pavements, accurate characterization of the existing subgrade condition becomes a crucial task. In the past, traditional geotechnical exploration and testing methods have been used to characterize existing subgrade conditions. However, with the introduction of the Mechanistic-Empirical (ME) pavement design, there is a need for improved and more appropriate methods of subgrade characterization, for prediction of future pavement conditions with better accuracy. Hence, this handbook will present a useful tool for practicing pavement engineers.

This book focuses on rock slope stability, with sections on geological data collection, geotechnical data collection and analysis, surface water and groundwater effects, kinematic and kinetic stability analysis, rock slope stabilization techniques, and rock slope instrumentation and monitoring.

Because of the discontinuous nature of rock, the design of stable rock slopes is as much an art as it is applied engineering. Experience can only be achieved from the proper utilization of these theories of soil and rock mechanics, structural geology, and hydrology.

Rock Slope Stability is invaluable for engineering geologists, geotechnical engineers, mining engineers, civil engineers, and mine managers- as well as anyone else dedicated to engineering slopes that are stable and safe and that enable a financial return.

Michael Paluszek, Stephanie Thomas - 472 pages - Language: ‎English - Publisher :‎ Apress; 3rd edition (March, 2024).

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution.

This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.

What You Will Learn: Write code for machine learning, adaptive control, and estimation using MATLAB + Use MATLAB graphics and visualization tools for machine learning + Become familiar with neural nets + Build expert systems + Understand adaptive control + Gain knowledge of Kalman Filters.