Safety evaluation by definition involves many complex factors and thus covers a wide range of topics. In order to focus the content of the workshop the subject matter was specific to the state of the art and the recent developments in nonlinear and time-variant methods employing identification procedures. Participants in the workshop represented a wide range of expertise. They were se lected in order to cover the state of the art of knowledge in fault-detection and dam age assessment, system identification, signal processing, mathematical and physical modelling and applications of techniques such as fuzzy logic and neural networks. The emphasis was placed on the exploitation and understanding of nonlinearity arising from structural or material faults. Figure 1 indicates the range of topics covered in the workshop. Since no unique or general approach yet exists for treating nonlinearity in the field of safety evaluation, many of the topics presented were problem specific. In order to assist the reader in selecting the material of primary interest a matrix of the topics covered by each participant is shown in Table 1. This table relates the authors to the subject matter, providing a guide through the diverse range of topics presented at the workshop.

With the expansion of new technologies, materials, and the design of complex systems, the expectations of society upon engineers are becoming larger than ever. Engineers make critical decisions with potentially high adverse consequences. The current political, societal, and financial climate requires engineers to formally consider the factors of uncertainty (e.g., floods, earthquakes, winds, environmental risks) in their decisions at all levels. Uncertainty Modeling and Analysis in Civil Engineering provides a thorough report on the immediate state of uncertainty modeling and analytical methods for civil engineering systems, presenting a toolbox for solving problems in real-world situations. Topics include Neural networks Genetic algorithms Numerical modeling Fuzzy sets and operations Reliability and risk analysis Systems control Uncertainty in probability estimates This compendium is a considerable reference for civil engineers as well as for engineers in other disciplines, computer scientists, general scientists, and students.

Providing quality research for the reader, this title encompasses all the recent developments in smart sensor technology for health monitoring in aerospace structures, providing a valuable introduction to damage detection techniques. Focussing on engineering applications, all chapters are written by smart structures and materials experts from aerospace manufacturers and research/academic institutions. This key reference: Discusses the most important aspects related to smart technologies for damage detection; this includes not only monitoring techniques but also aspects related to specifications, design parameters, assessment and qualification routes. Presents real case studies and applications; this includes in-flight tests; the work presented goes far beyond academic research applications. Displays a balance between theoretical developments and engineering applications

Examining the effect of age, environment, design and maintenance, these papers discuss how legislation and regulation in the management of systems deals with the topic of safety and reliability.

Artificial Intelligence (AI) has started the evolution in computer science. It is in good health, as many companies qualify their novelties as ‘smart’ or ‘intelligent’. The term ‘society of knowledge’ draws society nearer to the future and is a symbol of breakthrough. From this perspective, AI has reached maturity and has exploded into an endless set of sub-areas, getting in touch with all other disciplines, such as situation assessment, analysis and interpretation of music, management of environmental and biological systems, planning trains, routing of communication networks, assisting medical diagnosis or powering auctions. The wide variety of Artificial Intelligence application areas has meant that AI researchers often become scattered in different micro specialized fields. There are few occasions where the AI research community joins together, while computer scientists and engineers can find a lot of interesting ideas from the cross fertilization of results coming from all of these application areas. This book provides a representative selection of papers promoting synergies in the research community and includes papers on: Neural Networks, Computer Vision, Applications, Machine Learning, Reasoning, Planning and Robotics and Multi-Agent Systems. All of the papers collected in this volume would be of interest to any computer scientist or engineer interested in AI.

The capability to design quality software and implement modern information systems is at the core of economic growth in the 21st century. This book aims to review and analyze software engineering technologies, focusing on the evolution of design and implementation platforms as well as on novel computer systems.