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Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. * Familiarizes readers with the latest developments in the field * Includes identification problems for both dynamic and static systems * Addresses challenging civil engineering problems such as modal/model updating * Presents methods applicable to mechanical and aerospace engineering * Gives engineers and engineering students a concrete sense of implementation * Covers real-world case studies in civil engineering and beyond, such as: * structural health monitoring * seismic attenuation * finite-element model updating * hydraulic jump * artificial neural network for damage detection * air quality prediction * Includes other insightful daily-life examples * Companion website with MATLAB code downloads for independent practice * Written by a leading expert in the use of Bayesian methods for civil engineering problems This book is ideal for researchers and graduate students in civil and mechanical engineering or applied probability and statistics. Practicing engineers interested in the application of statistical methods to solve engineering problems will also find this to be a valuable text. MATLAB code and lecture materials for instructors available at www.wiley.com/go/yuen

Specifically written for graduate and undergraduate courses, this book provides the knowledge and skills necessary to lead enterprise engineering projects. The book describes a systematic methodology to design an enterprise system and presents an enterprise architecture that has three views of process, information, and organization. The text includes methods to analyze and design each view and then demonstrates how to integrate them to arrive at the overall enterprise design. The author also includes enterprise project templates in MS Word and Excel, a business process analyzer in Excel, PowerPoint® slides for each chapter, and solutions for the 200 problems that are presented in the book.

Granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. This innovative book presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. It explores key concepts and formalisms as well as applications. It also emphasizes the need to consider information granularity as an important design asset that helps in the construction of more realistic models of real-world systems and in facilitating collaborative pursuits of system modeling.

This book is professional reference that shows how mass integration techniques are used to maximize efficiency and sustainability and minimize the pollution of process systems and plants. Plant and unit operations professionals will save time and money by using the detailed tools and applications as part of their understanding of systematic process development. More generally it will be an important resource for those working with what commonly referred to as P2 (Pollution Prevention) technology. In addition to practitioners developing new systems and, more commonly, retrofitting old systems, the book will also find a significant market as a reference for researchers, and graduate level students taking courses in sustainable process systems/process integration/process synthesis and optimization/mass integration and related topics. The author is recognized as a foremost expert on mass integration for process efficiency and pollution prevention. No other reference is available on this fast developing topic.

Reorganized for easy use, this book provides an up-to-date reference to the tools, techniques, and science for predicting oil reservoir performance even in the most difficult fields. Topics covered in the handbook include: Processes to enhance production, Well modification to maximize oil and gas recovery, and Completion and evaluation of wells, well testing, and well surveys. This book provides solid information and insight for engineers and students alike on maximizing production from a field in order to obtain the best possible economic return. With this handbook, professionals will find a valuable reference for understanding the key relationships among the different operating variables. Examples contained in this reference demonstrate the performance of processes under forceful conditions through a wide variety of applications.

ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.

Optimization is a mathematical tool developed in the early 1960's used to find the most efficient and feasible solutions to an engineering problem.It can be used to find ideal shapes and physical configurations, ideal structural designs, maximum energy efficiency, and many other desired goals of engineering.This book is intended for use.