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That human factors (HF) affect the reliability of NDT is not novelty. Still, when it comes to reliability assessments, the role of people is often neglected. Reliability is typically expressed in terms of POD curves, and the effects of human and organisational factors on the inspection are typically tackled by the regulations, procedures and by the qualification and training of the inspection personnel. However, studies have shown that even the most experienced personnel can make mistakes and that the reliability in the field is never as high as the reliability measured in the POD experiments. Generally, HF are considered too unpredictable and too uncontrollable to model. If that is the fact, then what can we do? The engineering perspective to this problem has often been to find ways to automate inspections and, recently, to make use of artificial intelligence tools to decrease the direct effect of people on the inspection results and improve the overall efficiency and reliability. However, despite automation and AI, people remain the key players, though their tasks change. The contemporary approach to HF is not to engineer them out of the system but to design human-machine systems that make the best use of both. In this talk, ways of tackling HF in the design of systems and processes will be presented.
Eine zentrale Aufgabe der zerstörungsfreien Prüfung und der Strukturüberwachung (engl. Structural Health Monitoring - SHM) mit Ultraschallwellen ist die Bewertung von Schäden in Bauteilen. In vielen Bauteilen, wie zum Beispiel platten- und schalenförmigen Strukturen, Rohrleitungen oder Laminaten, breitet sich der Ultraschall in Form geführter Wellen aus. Zwar haben geführte Wellen eine relativ große Reichweite innerhalb des Bauteils und ermöglichen so eine großflächige Prüfung, ihre multimodalen und dispersiven Eigenschaften erschweren jedoch die Analyse der vom Schaden kommenden Reflexionen. Eine Möglichkeit, die Messsignale zu interpretieren und die Schäden zu charakterisieren, ist deren Vergleich mit der Wellenausbreitung in einem digitalen Modell. Hierbei stellt sich die Aufgabe, den Schaden im digitalen Modell anhand der Messdaten zu rekonstruieren. Diese Rekonstruktion beschreibt ein inverses Problem, das mehrere Vorwärtsrechnungen braucht, um das Schadensmodell an die Messdaten anzupassen.Durch die kleine Wellenlänge von Ultraschallwellen sind klassische Vorwärtsmethoden wie die Finte Elemente Methode rechenintensiv, weshalb die Autoren die semi-analytische Scaled Boundary Finite Element Method (SBFEM) benutzen, um den Rechenaufwand zu verringern. Im Beitrag wird ein inverses Verfahren basierend auf dem Automatischen Differenzieren in Kombination mit der SBFEM vorgestellt und an verschiedenen Schadenstypen in 2D-Querschnittmodellen von Wellenleitern getestet. In der präsentierten Vorstudie werden dafür „Messdaten“ aus unabhängigen Simulationen verwendet.
Eine zentrale Aufgabe der zerstörungsfreien Prüfung und der Strukturüberwachung (engl. Structural Health Monitoring - SHM) mit Ultraschallwellen ist die Bewertung von Schäden in Bauteilen. In vielen Bauteilen, wie zum Beispiel platten- und schalenförmigen Strukturen, Rohrleitungen oder Laminaten, breitet sich der Ultraschall in Form geführter Wellen aus. Zwar haben geführte Wellen eine relativ große Reichweite innerhalb des Bauteils und ermöglichen so eine großflächige Prüfung, ihre multimodalen und dispersiven Eigenschaften erschweren jedoch die Analyse der vom Schaden kommenden Reflexionen. Eine Möglichkeit, die Messsignale zu interpretieren und die Schäden zu charakterisieren, ist deren Vergleich mit der Wellenausbreitung in einem digitalen Modell. Hierbei stellt sich die Aufgabe, den Schaden im digitalen Modell anhand der Messdaten zu rekonstruieren. Diese Rekonstruktion beschreibt ein inverses Problem, das mehrere Vorwärtsrechnungen braucht, um das Schadensmodell an die Messdaten anzupassen. Durch die kleine Wellenlänge von Ultraschallwellen sind klassische Vorwärtsmethoden wie die Finte Elemente Methode rechenintensiv, weshalb die Autoren die semi-analytische Scaled Boundary Finite Element Method (SBFEM) benutzen, um den Rechenaufwand zu verringern. Im Beitrag wird ein inverses Verfahren basierend auf dem Automatischen Differenzieren in Kombination mit der SBFEM vorgestellt und an verschiedenen Schadenstypen in 2D-Querschnittmodellen von Wellenleitern getestet. In der präsentierten Vorstudie werden dafür „Messdaten“ aus unabhängigen Simulationen verwendet.
Long term monitoring of concrete structures using innovative NDT and SHM approaches: NDT-CE 4.0
(2021)
In Germany, a large amount of reinforced and/or prestressed concrete structures are approaching the end of their life cycle. Many of them already show a significant reduction of operability or capacity. The issue is most obvious, but not limited to traffic or energy related infrastructure. These structures have to be inspected and assessed on a regular basis. More and more permanent monitoring systems are installed to follow the degradation and to give pre-warning before failure.
While the basic mandatory inspection (e. g. of bridges) is still limited to visual examination and tap tests, more advanced investigation tools as ultrasound or radar are used more and more often. Monitoring e. g. by acoustic emission systems is increasingly used e.g. for detection of wire breaks in prestressed structures. The presentation gives an overview on the state of the art and an outlook on innovative systems, including the integration in modern data processing, evaluation, display and archive systems.
In this contribution, an approach is outlined to process non-destructively gath-ered measurement data in a comparable way in order to include the measured information in probabilistic reliability assessments of existing structures. An es-sential part is the calculation of measurement uncertainties. The effect of incor-porating evaluated NDT-results is demonstrated by means of a prestressed con-crete bridge and GPR measurements conducted on this bridge as a case-study. The bridge is assessed regarding SLS Decompression using the NDT-results.
Civil engineering industry is one of the most important industry sectors in the world-wide economy. It contributes significantly to the gross economic product and general employment. Even more important, it provides many of the basic needs of the society (e. g. housing, infrastructure, protection from natural hazards).
The concept of “Industry 4.0” or “Smart Production” has not yet made significant progress in the civil engineering industry. The design, build and operate processes are still widely dominated by the exchange of printed documents and drawings. Most objects (buildings and other constructions) are unique, and a large part of the production still requires a large amount of manual labor. As-built documentation and quality assurance are often neglected. Civil engineering is among the industries sectors with the lowest level of digitalization and the lowest gain in productivity.
However, this is going to change. In the past decade, several drivers have challenged the ways clients, contractors, and authorities currently operate. These drivers include but are not limited to an increasing demand for serialization and automatization, the mandatory introduction of “Building Information Modeling” (BIM) in public procurement, the availability of construction equipment with sensors and digital interfaces or emerging automated construction technologies such as 3D-printing.
NDE (referred to as NDT-CE in this sector), after a rapid technological development in the last two decades, plays an increasing role in quality assurance, condition assessment and monitoring of structures. However, with very few exceptions, applications are mostly non-standardized and performed only at selected sites. To change this, the NDT-CE community including manufacturers, service providers, clients and the scientific community must work consistently on open data formats, interfaces to BIM, standardization and validated ways for a quantitative use of the results in the assessment of constructions.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Thermografische Super Resolution ermöglicht die Auflösung von Defekten/Inhomogenitäten unterhalb des klassischen Limits, welches durch die Diffusionseigenschaften der thermischen Wellenausbreitung bestimmt wird. Basierend auf einer Kombination aus der Anwendung spezieller Abtaststrategien und einer anschließenden numerischen Optimierungsschritt bei der Datenauswertung hat sich die thermografische Super Resolution bereits bei der Detektion von 1D-Defekten gegenüber den Standard-Thermografieverfahren als überlegen erwiesen. In unserer Arbeit erweitern wir die Möglichkeiten der Methode zur effizienten Detektion und Auflösung von Defektquerschnitten mit einer vollständig 2D-strukturierten Erwärmung.
Der experimentelle Ansatz basiert auf einer wiederholten räumlich strukturierten Erwärmung durch einen Hochleistungslaser. In einem zweiten Nachbearbeitungsschritt werden mehrere kohärente Messungen mittels mathematischer Optimierung und unter Ausnutzung der (Joint-) Sparsity der Defekte innerhalb des Prüfkörpers kombiniert. Als Ergebnis kann eine 2D-sparse Defekt-/ Inhomogenitätskarte erhalten werden. Da die Kombination von räumlich strukturierter Erwärmung und anschließender numerischer Kombination mehrerer kohärenter Messungen nicht nur die Auflösung verbessert, sondern auch die Messkomplexität drastisch erhöht, werden verschiedene Scanstrategien untersucht. Abschließend werden die erhaltenen Ergebnisse mit denen konventioneller thermografischer Prüfverfahren verglichen.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we re-port on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample.