Non destructive testing aimed at monitoring, structural identification and di- nostics is of strategic importance in many branches of civil and mechanical - gineering. This type of tests is widely practiced and directly affects topical issues regarding the design of new buildings and the repair and monitoring of existing ones. The load bearing capacity of a structure can now be evaluated using well established mechanical modelling methods aided by computing facilities of great capability. However, to ensure reliable results, models must be calibrated with - curate information on the characteristics of materials and structural components. To this end, non destructive techniques are a useful tool from several points of view. Particularly, by measuring structural response, they provide guidance on the validation of structural descriptions or of the mathematical models of material behaviour. Diagnostic engineering is a crucial area for the application of non destructive testing methods. Repeated tests over time can indicate the emergence of p- sible damage occurring during the structure's lifetime and provide quantitative estimates of the level of residual safety.

In the oil and gas industries, large companies are endeavoring to find and utilize efficient structural health monitoring methods in order to reduce maintenance costs and time. Through an examination of the vibration-based techniques, this title addresses theoretical, computational and experimental methods used within this trend.By providing comprehensive and up-to-date coverage of established and emerging processes, this book enables the reader to draw their own conclusions about the field of vibration-controlled damage detection in comparison with other available techniques. The chapters offer a balance between laboratory and practical applications, in addition to detailed case studies, strengths and weakness are drawn from a broad spectrum of information.

This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Proceedings of the NATO Advanced Study Institute, Sesimbra, Portugal, 3-15 May, 1998

This book is organized around the various sensing techniques used to achieve structural health monitoring. Its main focus is on sensors, signal and data reduction methods and inverse techniques, which enable the identification of the physical parameters, affected by the presence of the damage, on which a diagnostic is established. Structural Health Monitoring is not oriented by the type of applications or linked to special classes of problems, but rather presents broader families of techniques: vibration and modal analysis; optical fibre sensing; acousto-ultrasonics, using piezoelectric transducers; and electric and electromagnetic techniques. Each chapter has been written by specialists in the subject area who possess a broad range of practical experience. The book will be accessible to students and those new to the field, but the exhaustive overview of present research and development, as well as the numerous references provided, also make it required reading for experienced researchers and engineers.

The SEM Handbook of Experimental Structural Dynamics stands as a comprehensive overview and reference for its subject, applicable to workers in research, product design and manufacture, and practice. The Handbook is devoted primarily to the areas of structural mechanics served by the Society for Experimental Mechanics IMAC community, such as modal analysis, rotating machinery, structural health monitoring, shock and vibration, sensors and instrumentation, aeroelasticity, ground testing, finite element techniques, model updating, sensitivity analysis, verification and validation, experimental dynamics sub-structuring, quantification of margin and uncertainty, and testing of civil infrastructure. Chapters offer comprehensive, detailed coverage of decades of scientific and technologic advance and all demonstrate an experimental perspective. Several sections specifically discuss the various types of experimental testing and common practices utilized in the automotive, aerospace, and civil structures industries. · History of Experimental Structural Mechanics · DIC Methods - Dynamic Photogrammetry · LDV Methods · Applied Digital Signal Processing · Introduction to Spectral - Basic Measurements · Structural Measurements - FRF · Random and Shock Testing · Rotating System Analysis Methods · Sensors Signal Conditioning Instrumentation · Design of Modal Tests · Experimental Modal Methods · Experimental Modal Parameter Evaluation · Operating Modal Analysis Methods · Analytical Numerical Substructuring · Finite Element Model Correlation · Model Updating · Damping of Materials and Structures · Model Calibration and Validation in Structures · Uncertainty Quantification: UQ, QMU and Statistics · Nonlinear System Analysis Methods (Experimental) · Structural Health Monitoring and Damage Detection · Experimental Substructure Modeling · Modal Modeling · Response (Impedance) Modeling · Nonlinear Normal Mode Analysis Techniques (Analytical) · Modal Modeling with Nonlinear Connection Elements (Analytical) · Acoustics of Structural Systems (VibroAcoustics) · Automotive Structural Testing · Civil Structural Testing · Aerospace Perspective for Modeling and Validation · Sports Equipment Testing · Applied Math for Experimental Structural Mechanics Contributions present important theory behind relevant experimental methods as well as application and technology. Topical authors emphasize and dissect proven methods and offer detail beyond a simple review of the literature. Additionally, chapters cover practical needs of scientists and engineers who are new to the field. In most cases, neither the pertinent theory nor, in particular, the practical issues have been presented formally in current academic textbooks. Each chapter in the Handbook represents a ’must read’ for someone new to the subject or for someone returning to the field after an absence. Reference lists in each chapter consist of the seminal papers in the literature. This Handbook stands in parallel to the SEM Handbook of Experimental Solid Mechanics, where this Handbook focuses on experimental dynamics of structures at a macro-scale often involving multiple components and materials where the SEM Handbook of Experimental Solid Mechanics focuses on experimental mechanics of materials at a nano-scale and/or micro-scale.

A smart civil structure integrates smart materials, sensors, actuators, signal processors, communication networks, power sources, diagonal strategies, control strategies, repair strategies, and life-cycle management strategies. It should function optimally and safely in its environment and maintain structural integrity during strong winds, severe earthquakes, and other extreme events. This book extends from the fundamentals to the state-of-the-art. It covers the elements of smart civil structures, their integration, and their functions. The elements consist of smart materials, sensors, control devices, signal processors, and communication networks. Integration refers to multi-scale modelling and model updating, multi-type sensor placement, control theory, and collective placement of control devices and sensors. And the functions include structural health monitoring, structural vibration control, structural self-repairing, and structural energy harvesting, with emphasis on their synthesis to form truly smart civil structures. It suits civil engineering students, professionals, and researchers with its blend of principles and practice.