Wael A. Altabey - Böcker

Fundamentals of Machine Component Design bridges theory and practice to provide readers with a thorough understanding of best practices for machine component design and application. Load and stress analysis, fatigue, fracture, and other mechanical behaviors that can result in the failure of a machine component are discussed in the early chapters before the book moves on to cover different connections (welded and bolted) prevalent in machine components, and then individual components such as gears, shafts, bearings, springs, pressure vessels, brakes, clutches, keys and couplings, and more. The book ends with chapters outlining different design methods as well as design problems for readers to practice with. Solutions are also provided.Covers the design of shafts, power screws, bolts, welded connections, springs, and pressure vessels, as well as transmitted power elements such as belts, chains, gears, and wire ropesOutlines finite element methods and other techniques that can be used for effectively designing machine componentsDiscusses contact and sliding bearings, keys and couplings, gears (helical, spur, bevel, and worm), and moreIncludes solved problems to help readers refine their skills

Structural Health Monitoring of Composite Structures with Artificial Intelligence offers a comprehensive review of the application of AI in the SHM of composite structures. The book’s objective is to bridge the gap between traditional SHM approaches and the integration of advanced AI techniques, providing readers with foundational knowledge on composite materials, manufacturing processes, mechanical behavior, and state-of-the-art SHM methodologies. The book is timely and significant as composite structures are witnessing increasing use in critical infrastructure, aerospace, and industry, where real-time, intelligent monitoring is crucial for safety and maintenance. It covers new developments such as the use of machine learning, deep learning, and artificial neural networks in SHM, and illustrates the integration of non-destructive testing (NDT) methods with AI algorithms. The content structure follows logical progression: from basic material concepts, design, manufacturing and fabrication, through damage and failure modes, advanced SHM monitoring, probabilistic design concepts, and AI based schemes for SHM. A full chapter is also dedicated to applied case studies in pipelines, ducts and plates) using analytical, numerical, and simulation tools (ANSYS, MATLAB). The book’s key audience will include postgraduate students, academic researchers, and practicing engineers, who are working in civil, mechanical, materials, and aerospace engineering.Provides comprehensive integration of the fundamentals of composite materials, SHM, NDT, and AI all in one resourceIncludes detailed practical case studies using real-world scenarios in pipelines, plates and wind turbine bladesUp-to-date inclusion of machine learning, deep learning, and neural networks in the SHM contextContains comparative analysis of SHM/NDT methods and AI schemesContains explicit treatment of risk management and reliability assessment using AI and data fusion techniques

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

As transport networks become more congested, there is a growing need to adopt policies that manage demand and make full use of existing assets. Advances in information technology are now such that intelligent transportation systems (ITS) offer real potential to meet this challenge by monitoring current conditions, predicting what might happen in the future, and providing the means to manage transport proactively and on an area-wide basis. Modeling and Simulation of Intelligent Transportation Systems provides engineers, professionals, and researchers an intuitive appreciation for ITS theory, related sensor technologies, and other practical applications, including traffic management, safety, design optimization, and sustainability.Provides the theory and practical applications of Intelligent Transport Theory which will be helpful as highway construction recedes as a sustainable long-term solution.Includes several case studies that illustrate the concepts presented throughout.

Over the past two decades, the use of finite element method as a design tool has grown rapidly. Easy to use commercial software, such as ANSYS, have become common tools in the hands of students as well as practicing engineers. The objective of this book is to demonstrate the use of one of the most commonly used Finite Element Analysis software, ANSYS, for linear static, dynamic, and thermal analysis through a series of tutorials and examples. Some of the topics covered in these tutorials include development of beam, frames, and Grid Equations; 2-D elasticity problems; dynamic analysis; composites, and heat transfer problems. These simple, yet, fundamental tutorials are expected to assist the users with the better understanding of finite element modeling, how to control modeling errors, and the use of the FEM in designing complex load bearing components and structures. These tutorials would supplement a course in basic finite element or can be used by practicing engineers who may not have the advanced training in finite element analysis.

Over the past two decades, the use of finite element method as a design tool has grown rapidly. Easy to use commercial software, such as ANSYS, have become common tools in the hands of students as well as practicing engineers. The objective of this book is to demonstrate the use of one of the most commonly used Finite Element Analysis software, ANSYS, for linear static, dynamic, and thermal analysis through a series of tutorials and examples. Some of the topics covered in these tutorials include development of beam, frames, and Grid Equations; 2-D elasticity problems; dynamic analysis; composites, and heat transfer problems. These simple, yet, fundamental tutorials are expected to assist the users with the better understanding of finite element modeling, how to control modeling errors, and the use of the FEM in designing complex load bearing components and structures. These tutorials would supplement a course in basic finite element or can be used by practicing engineers who may not have the advanced training in finite element analysis.

Probability and Optimization for Engineers covers the fundamentals of probabilistic design and optimum design and methods for high performance design. It presents the principle of finite element method in probabilistic and optimum design with solved specific interactive problems of finite element analysis using ANSYS. It explains artificial intelligence for optimum design using AI algorithms including machine learning, deep learning, artificial neural networks, Bayesian optimum design, Bayesian machine learning optimization, and genetic algorithms.  Probabilistic and optimum design course with easy, visual, and applied methods.Solved examples, assignments, problems and tutorials.Educational resource for engineering students and for practicing engineers.