Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

A new open-database benchmark structure for vibration-based Structural Health Monitoring

Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data

Detecting Structural Defects Using Novel Smart Sensory and Sensor-less Approaches

Monitoring the mechanical integrity of critical structures is extremely important, as mechanical defects can potentially have adverse impacts on their safe operability throughout their service life. Structural defects can be detected by using active structural health monitoring (SHM) approaches, in which a given structure is excited with harmonic mechanical waves generated by actuators. The response of the structure is then collected using sensor(s) and is analyzed for possible defects, with various active SHM approaches available for analyzing the response of a structure to single- or multi-frequency harmonic excitations. In order to identify the appropriate excitation frequency, however, the majority of such methods require a priori knowledge of the characteristics of the defects under consideration. This makes the whole enterprise of detecting structural defects logically circular, as there is usually limited a priori information about the characteristics and the locations of defects that are yet to be detected. Furthermore, the majority of SHM techniques rely on sensors for response collection, with the very same sensors also prone to structural damage. The Surface Response to Excitation (SuRE) method is a broadband frequency method that has high sensitivity to different types of defects, but it requires a baseline. In this study, initially, theoretical justification was provided for the validity of the SuRE method and it was implemented for detection of internal and external defects in pipes. Then, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method was developed based on the SuRE method to eliminate the need for any baseline. Unlike traditional approaches, the CHEBI method requires no a priori knowledge of defect characteristics for the selection of the excitation frequency. In addition, the proposed heterodyne effect-based approach constitutes the very first sensor-less smart monitoring technique, in which the emergence of mechanical defect(s) triggers an audible alarm in the structure with the defect. Finally, a novel compact phased array (CPA) method was developed for locating defects using only three transducers. The CPA approach provides an image of most probable defected areas in the structure in three steps. The techniques developed in this study were used to detect and/or locate different types of mechanical damages in structures with various geometries

Review on smartphone sensing technology for structural health monitoring

Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

Intelligent one-point damage localization of an isotropic surface pipeline using Guassian Process regression

Pipelines are subjected to many damaging agents, such as, earthquake, ground movement, and aging which are responsible for important financial expenses. Structural Health Monitoring (SHM) of civil structures using arrays of sensors is promising such that data form the monitoring systems enable us to trace the structural anomalies and performance for early treatments. The need for introducing faster and intelligent methods has helped researchers propose novel approaches for such monitoring procedures. In this study a new method is introduced for monitoring of surface pipelines used primarily for oil and gas. The framework takes the advantage of Gaussian Process Regression Method (GPRM) to create a probabilistic predictive model for damage detection and the subsequent localization of the defect. To this end, an isotropic pipeline is modeled numerically and validated with an experimental setup. Afterwards, the model is extended to the real-life application to establish a meta model. Damages are introduced as small holes at different locations (one at each time). The GPRM is used to map the system responses to the selected statistical features which are utilized as indicators for the existence of the damages and their locations. GPRM reveals more promising results compared with conventional regression analysis. It considers the uncertainties due to lack of observation. In addition, it is an updatable approach with having local effects on the model. In another words, it affects the model in the vicinity of new observations. Moreover, among selected statistical features, number of peaks greater than or equal to 20% and 60% of the maximum peak values show better results corresponding to damage localization. Also the curve length and correlation coefficient of the system response (induced signal) are found to be efficient for damage detection. The novel method has been validated with filed measurements and experimental data and found to work efficiently

Design and validation of a methodology for wind energy structures health monitoring

L’objectiu de la Monitorització de la salut estructural (SHM) és la verificació de l’estat o la salut de les estructures per tal de garantir el seu correcte funcionament i estalviar en el cost de manteniment. El sistema SHM combina una xarxa de sensors connectada a l’estructura amb monitoratge continu i algoritmes específics. Es deriven diferents beneficis de l’aplicació de SHM, on trobem: coneixement sobre el comportament de l’estructura sota diferents operacions i diferents càrregues ambientals , el coneixement de l’estat actual per tal de verificar la integritat de l’estructura i determinar si una estructura pot funcionar correctament o si necessita manteniment o substitució i, per tant, reduint els costos de manteniment. El paradigma de la detecció de danys es pot abordar com un problema de reconeixement de patrons (comparació entre les dades recollides de l’estructura sense danys i l’estructura actual, per tal de determinar si hi ha algun canvi) . Hi ha moltes tècniques que poden gestionar el problema. En aquest treball s’utilitzen les dades dels acceleròmetres per desenvolupar aproximacions estadístiques utilitzant dades en temps per a la detecció dels danys en les estructures. La metodologia s’ha dissenyat per a una turbina eòlica off - shore i només s’utilitzen les dades de sortida per detectar els danys. L’excitació de la turbina de vent és induïda pel vent o per les ones del mar. La detecció de danys no és només la comparació de les dades. S’ha dissenyat una metodologia completa per a la detecció de danys en aquest treball. Gestiona dades estructurals, selecciona les dades adequades per detectar danys, i després de tenir en compte les condicions ambientals i operacionals (EOC) en el qual l’estructura està treballant, es detecta el dany mitjançant el reconeixement de patrons. Quan es parla del paradigma de la detecció de danys sempre s’ha de tenir en compte si els sensors estan funcionant correctament. Per això és molt important comptar amb una metodologia que comprova si els sensors estan sans. En aquest treball s’ha aplicat un mètode per detectar els sensors danyats i s’ha insertat en la metodologia de detecció de danys.The objective of Structural Health Monitoring (SHM) is the verification of the state or the health of the structures in order to ensure their proper performance and save on maintenance costs. The SHM system combines a sensor network attached to the structure with continuous monitoring and specific, proprietary algorithms. Different benefits are derived from the implementation of SHM, some of them are: knowledge about the behavior of the structure under different loads and different environmental changes, knowledge of the current state in order to verify the integrity of the structure and determine whether a structure can work properly or whether it needs to be maintained or replaced and, therefore, reduce maintenance costs. The paradigm of damage detection can be tackled as a pattern recognition problem (comparison between the data collected from the structure without damages and the current structure in order to determine if there are any changes). There are lots of techniques that can handle the problem. In this work, accelerometer data is used to develop statistical data driven approaches for the detection of damages in structures. As the methodology is designed for wind turbines, only the output data is used to detect damage; the excitation of the wind turbine is provided by the wind itself or by the sea waves, being those unknown and unpredictable. The damage detection strategy is not only based on the comparison of many data. A complete methodology for damage detection based on pattern recognition has been designed for this work. It handles structural data, selects the proper data for detecting damage and besides, considers the Environmental and Operational Conditions (EOC) in which the structure is operating. The damage detection methodology should always be accessed only if there is a way to probe that the sensors are correctly working. For this reason, it is very important to have a methodology that checks whether the sensors are healthy. In this work a method to detect the damaged sensors has been also implemented and embedded into the damage detection methodology.El objetivo de la Monitorización de la salud estructural (SHM) es la verificación del estado o la salud de las estructuras con el fin de garantizar su correcto funcionamiento y ahorrar en el costo de mantenimiento. El sistema SHM combina una red de sensores conectada a la estructura con monitorización continua y algoritmos específicos. Se derivan diferentes beneficios de la aplicación de SHM, donde encontramos: conocimiento sobre el comportamiento de la estructura bajo diferentes operaciones y diferentes cargas ambientales, el conocimiento del estado actual con el fin de verificar la integridad de la estructura y determinar si una estructura puede funcionar correctamente o si necesita mantenimiento o sustitución y, por lo tanto, reduciendo los costes de mantenimiento. El paradigma de la detección de daños se puede abordar como un problema de reconocimiento de patrones (comparación entre los datos recogidos de la estructura sin daños y la estructura actual, con el fin de determinar si hay algún cambio). Hay muchas técnicas que pueden manejar el problema. En este trabajo se utilizan los datos de los acelerómetros para desarrollar aproximaciones estadísticas utilizando datos en tiempo para la detección de los daños en las estructuras. La metodología se ha diseñado para una turbina eólica off-shore y sólo se utilizan los datos de salida para detectar los daños. La excitación de la turbina de viento es inducida por el viento o por las olas del mar. La detección de daños no es sólo la comparación de los datos. Se ha diseñado una metodología completa para la detección de daños en este trabajo. Gestiona datos estructurales, selecciona los datos adecuados para detectar daños, y después de tener en cuenta las condiciones ambientales y operacionales (EOC) en el que la estructura está trabajando, se detecta el daño mediante el reconocimiento de patrones. Cuando se habla del paradigma de la detección de daños siempre se debe tener en cuenta si los sensores están funcionando correctamente. Por eso es muy importante contar con una metodología que comprueba si los sensores están sanos. En este trabajo se ha aplicado un método para detectar los sensores dañados y se ha metido en la metodología de detección de dañosPostprint (published version