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Many nondestructive techniques for structural health monitoring are subjective and based on visual observations of degradation. In addition, dynamic properties of structures are already used to obtain quantitative structural health data. However, most current data collection is limited to localized damage on the structure, rather than global response. Recent research involves the use of commercially available digital video cameras, or virtual visual sensors, to observe structural dynamic behavior. This project focuses on the determination of natural vibration frequencies by monitoring the intensity value of a single pixel coordinate over the course of a few seconds of a video of structural vibration, and then applying a fast Fourier transform to extract signal frequencies. Natural frequencies can be used to observe changes in stiffness properties of materials and structural systems that may relate to deterioration. The focus here is primarily on the development and application of the virtual visual sensor technique to wood structures to obtain information relevant to objective structural health monitoring. The experiments focus on verification of the method to extract natural frequencies in various scenarios, using the natural color gradients in wood structures to observe the motion. Additionally, effects of moisture content and simulated damage on natural frequencies are observed on simply-supported beams of dimensional lumber. Applications are also made to an in-place US Forest Service pedestrian bridge. Results show comparable accuracy in determining vibrational frequencies with virtual visual sensors and accelerometer measurements, successful observation of vibrational frequencies in a timber bridge, and good use of naturally occurring color gradients in both laboratory and field tests. Moisture content and simulated damage had measureable effects on natural frequencies using conventional accelerometers and virtual visual sensors. Digital video cameras show potential to be a promising tool for structural health monitoring of timber structures.
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.
Structural health monitoring (SHM) is a new engineering field with a growing tendency, based on technology development focused on data acquisition and analysis, to prevent possible damage in man-made structures and land's natural faults. The data are obtained from sensors and monitoring systems that allow detecting damages on structures, space vehicles, and land natural faults, to model their behavior under adverse scenarios, in order to search the detection of anomalies. Currently, there are many SHM systems with sensors based on different technologies like optical fiber, video cameras, optical scanners, wireless networks, and piezoelectric transducers, among others. In this context, the present book includes selected chapters with theoretical models and applications, to preserve infrastructure and prevent loss of human lives.
The book presents recent advances regarding the inspection and monitoring of engineering structures; including bridges, buildings, aircraft and space structures, nuclear reactors and defense platforms. Among the techniques covered are UAV photogrammetry, strain monitoring, infrared detection, acoustic emission testing, residual stress measurements, fiber optical sensing, thermographic inspection, vibration analysis, piezoelectric sensing and ultrasonic testing. Keywords: Bridges, Buildings, Aircraft Structures, Space Structures, Nuclear Reactors, Defense Platforms, UAV Photogrammetry, Strain Monitoring, Infrared Detection, Acoustic Emission Testing, Residual Stress Measurements, Fiber Optical Sensing, Thermographic Inspection, Vibration Analysis, Piezoelectric Sensing, Ultrasonic Testing, Impact Damage, Anaerobic Reactor Performance, Geomembranes, Ossointegrated Implants, Fatigue Crack Growth, Accelerometer, Nonlinear Cable Bracing, Timber Utility Poles, Steel Pipes, Loosened Bolts on Pipes, IMU-based Motion Capture, CFRP Composites, Maglev Guideway Girder, Cable-Pylon Anchorage, Deep Learning Techniques.
Structural Health Monitoring with Piezoelectric Wafer Active Sensors, Second Edition provides an authoritative theoretical and experimental guide to this fast-paced, interdisciplinary area with exciting applications across a range of industries. The book begins with a detailed yet digestible consolidation of the fundamental theory relating to structural health monitoring (SHM). Coverage of fracture and failure basics, relevant piezoelectric material properties, vibration modes in different structures, and different wave types provide all the background needed to understand SHM and apply it to real-world structural challenges. Moving from theory to experimental practice, the book then provides the most comprehensive coverage available on using piezoelectric wafer active sensors (PWAS) to detect and quantify damage in structures. Updates to this edition include circular and straight-crested Lamb waves from first principle, and the interaction between PWAS and Lamb waves in 1-D and 2-D geometries. Effective shear stress is described, and tuning expressions between PWAS and Lamb waves has been extended to cover axisymmetric geometries with a complete Hankel-transform-based derivation. New chapters have been added including hands-on SHM case studies of PWAS stress, strain, vibration, and wave sensing applications, along with new sections covering essential aspects of vibration and wave propagation in axisymmetric geometries. Comprehensive coverage of underlying theory such as piezoelectricity, vibration, and wave propagation alongside experimental techniques Includes step-by-step guidance on the use of piezoelectric wafer active sensors (PWAS) to detect and quantify damage in structures, including clear information on how to interpret sensor signal patterns Updates to this edition include a new chapter on composites and new sections on advances in vibration and wave theory, bringing this established reference in line with the cutting edge in this emerging area
Structural health monitoring (SHM) has become a viable tool to provide owners of structures and mechanical systems with quantitative and objective data for maintenance and repair. Traditionally, discrete contact sensors such as strain gages or accelerometers have been used for SHM. However, distributed remote sensors could be advantageous since they don't require cabling and can cover an area rather than just a few discrete points. Along this line a novel monitoring methodology based on video analysis is proposed. By employing commercially available digital cameras combined with efficient signal processing methods, measurement of natural frequencies using a computationally less demanding algorithm was possible. In this dissertation, the basic concept of the proposed so-called Eulerian-based virtual visual sensors (VVS) is first introduced. In order to improve the signal-to-noise ratio (SNR), the application of oversampling as well as two different targets were considered. The proposed methodology was evaluated on a set of laboratory experiments to demonstrate the accuracy of the considered approach. In-service monitoring examples of different bridges are further provided to show the practical aspects. A discussion of further work to improve the methodology is also discussed.
Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste. The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike. Presents convincing “success stories that encourage application of AI-powered tools to civil engineering Provides a wealth of valuable technical information to address and resolve many challenging construction problems Illustrates the most recent shifts in thinking and practice for sustainable construction
Structural Health Monitoring (SHM) and condition assessment deal with inspecting the health and integrity of the monitored systems. Although robust damage detection methods have been proposed in the recent two decades, there are intensive ongoing investigations to tackle the practical and technical open challenges, such as smart sensing, sparse sensor measurements and real-time damage detection. This thesis is devoted to discussing pattern recognition and damage detection methods using vibration, acoustic and vision perspectives.First, a machine learning-based approach is proposed for object classification by texture analysis. Bag-of-Words (BoW) and Support Vector Machine (SVM) techniques are used to extract the features and train an identifier, respectively. The method is particularly exploited for tie/ballast image classification at Rail Defect Facility of UC San Diego by mounting a high-speed camera on a cart moving with walking speed. Second, a deterministic vibration-based method is proposed for damage quantification in the structures, using sparse sensor measurements. The estimated damages are then further tuned by repeating the proposed approach to reach more accurate results. The method is employed for damage detection in lab-scale and full-scale building structures. Defect imaging in plates using data-driven Matched Filed Processing (MFP) is the last concept discussed in this thesis. Under the Born approximation, difference between the responses of the damaged and pristine plates is computed as the data set containing the defect's acoustic signature, and conventional and adaptive beamformers are used to perform the MFP and localize the defect. The method is employed for damage detection in an aluminum plate.
This book constitutes the refereed post-conference proceedings of the 8th International Conference on Digital Heritage, EuroMed 2020, held virtually in November 2020. The 37 revised project papers and 30 revised short papers presented were carefully reviewed and selected from 326 submissions. The papers are on topics such as digital data acquisition technologies in CH/2D and 3D data capture methodologies and data processing; remote sensing for archaeology and cultural heritage management and monitoring; interactive environments and applications; reproduction techniques and rapid prototyping in CH; e-Libraries and e-Archives in cultural heritage; virtual museum applications (e-Museums and e-Exhibitions); visualisation techniques (desktop, virtual and augmented reality); storytelling and authoring tools; tools for education; 2D and 3D GIS in cultural heritage; and on-site and remotely sensed data collection.