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The European Commission adopted an EU
Energy Label for smartphones and tablets in June 2023. For the
first time, the EU Energy Label will depict, among energy
efficiency, also a reparability score, battery endurance, battery
lifetime, drop resistance and dust and water ingress protection.
However, does the multitude of parameters bear the risk to
confuse consumers instead of triggering environmentally
sustainable purchase decisions? In a survey in Germany,
consumers were asked to make a choice when the label does not
clearly identify the environmentally better product. Instead,
energy efficiency has to be valued against reparability,
reliability against energy efficiency, and reparability against
reliability. The sustainability lever of the different aspects is not
the same, as e.g. lifetime extension has a much more positive
environmental impact than energy efficiency in case of mobile
devices. Therefore, the question remains, whether consumers
intuitively make the right choice. Results of the survey indicate
that consumers interpret the complex label in the desired
manner and value those aspects higher, which indeed have a
more positive effect on the environment.
An analytical expression for the frequency response function of a coupled pedestrian-bridge system is presented and evaluated using an experimental measurement campaign performed on the Folke Bernadotte Bridge in Stockholm, Sweden. A finite element model and the modal models that consider the human–structure interaction effect are calibrated with respect to the measurements. The properties of the spring–mass–damper model representing the pedestrians were identified, considering the different structural modes of the system. Good agreement was obtained between the experimental and theoretical frequency response functions. A sensitivity analysis of the obtained solution was performed, validating the determined analytical expression for the frequency response function of the coupled pedestrian-bridge system that takes into account the human–structure interaction effect.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material.
The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification.
Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
The Infrastar training school provides lectures and hands-on training to Master and PhD students, early-stage researchers and (young) professionals on all aspects of asset management of civil infrastructures with respect to fatigue of materials. It is organized yearly since 2019 and up to 20 profiles are selected to attend the 3.5-day training.
A team of 7 teachers provides insight in multi-disciplinary and intersectoral basic concepts in three core fields, ranging from the design to the dismantling of the structures (bridges and wind turbines):
1. Monitoring and auscultation,
2. Structural and action models,
3. Reliability, risk and decision analyses.
Each year, a recognized expert is invited to deliver a keynote lecture to raise interest in a specific aspect of one of the fields covered. The presentation is recorded and published on the training school website.
The Infrastar training school provides lectures and hands-on training to Master and PhD students, early-stage researchers and (young) professionals on all aspects of asset management of civil infrastructures with respect to fatigue of materials. It is organized yearly since 2019 and up to 20 profiles are selected to attend the 3.5-day training.
A team of 7 teachers provides insight in multi-disciplinary and intersectoral basic concepts in three core fields, ranging from the design to the dismantling of the structures (bridges and wind turbines):
1. Monitoring and auscultation,
2. Structural and action models,
3. Reliability, risk and decision analyses.
Each year, a recognized expert is invited to deliver a keynote lecture to raise interest in a specific aspect of one of the fields covered. The presentation is recorded and published on the training school website.
Seit beinahe 50 Jahren werden Zuverlässigkeitsbewertungen zerstörungsfreier Prüfverfahren erfolgreich in unterschiedlichen Industriebranchen eingesetzt. Dennoch gibt es weiterhin viele Vorbehalte, da die Bewertungsgrundlage nicht standardisiert ist und konkrete Handlungsanweisungen fehlen. Dem gegenüber steht die Anerkennung in der ZfP-Community. Es ist bisher nur Wenigen bekannt, dass dem Wissen über die Zuverlässigkeit eines zerstörungsfreien Prüfverfahren eineSchlüsselfunktion für die ZfP 4.0 zukommt.
Im Vortrag wird die Notwendigkeit für eine Norm oder eine Richtlinie zum Thema Zuverlässigkeitsbewertungen aufgezeigt. Es wird darauf eingegangen, warum es bisher (in Deutschland) noch keine Richtlinie gab und gibt und was der:die Anwender:innen aus einer Richtlinie zur Zuverlässigkeitsbewertung von ZfP-Prüfverfahren erwarten kann.
Es werden konkrete Schritte und Vorgehensweisen zur Bewertung der Zuverlässigkeit eines Verfahrens aufgezeigt; von der Definition eines Anwendungsfalls, über die Herstellung geeigneter Testkörper bis hin zur Durchführung und Bewertung von Prüfungen unter Einbeziehung von menschlichen Einflüssen. Die Beispiele umfassen sowohl Anwendungen aus dem Bauwesen als auch aus dem Maschinen- und Anlagenbau.
Der Vortrag fasst die bisherigen und geplanten Arbeiten in dem WIPANO Projekt „Normung für die probabilistische Bewertung der Zuverlässigkeit für zerstörungsfreie Prüfverfahren“ zusammen.
Visually detecting irregularities on components is a complex process, influenced by physical, technical, and human factors. Current standards mainly focus on illuminance, demanding highest accuracy for it, without providing scientific justification. In this research, 63 inspectors visually inspected forgings, with and without crack-like indications. The influence of illuminance on probability of detection was investigated. Therefore, illuminance was varied in three steps (200 lx, 350 lx, 500 lx). Human factors (e.g., experience, motivation) were recorded by using questionnaires. The results show no significant difference in probability of detection between the three illuminance levels. Equal probability of detection rates could be achieved at illuminances below and at the required threshold (500 lx). A difference in illuminance was found between the measurement point and the test area of the respective participant. This leads to higher assumed illuminance than present on the component. Compared with the measurement accuracy of the used illuminance meter, none of these deviations could be used to explain the similar results at different illuminance. The fact that illuminance has a significant influence in detecting indications has been disproven for this case. Whether human factors can provide explanation for the results remains unclear. This will be investigated in further research.
Menschliche Faktoren sind ein häufig genanntes Thema, wenn wir über
die Zuverlässigkeit der zerstörungsfreien Prüfung (ZfP) sprechen. Die Probability of Detection (POD), das üblicherweise verwendete Maß für die Zuverlässigkeit der ZfP, betrachtet jedoch nur die technische Fähigkeit eines ZfP-Systems, einen Fehler zu entdecken. Nach mehreren Jahrzehnten der Erforschung des Einflusses der menschlichen Faktoren auf die Zuverlässigkeit der ZfP gibt es immer noch keinen allgemein akzeptierten Ansatz, um menschliche Faktoren in der Zuverlässigkeitsbewertung sichtbar zu machen. Dieser Beitrag gibt einen Überblick über verschiedene verfügbare Methoden zur Einbeziehung menschlicher Faktoren in die Zuverlässigkeitsbewertung. Das Thema ist ein essenzieller Bestandteil des laufenden WIPANO-Projekts "normPOD", das sich als Ziel gesetzt hat, die Standardisierung der Zuverlässigkeitsbewertung in Deutschland voranzutreiben und im Vergleich zu den schon bekannten internationalen Normen einen Fokus auf den Umgang mit den menschlichen Faktoren zu legen.
Each engineering decision is based on a number of more or less accurate information. In assessment of existing structures, additional relevant information collected with on-site inspections facilitate better decisions. However, observed data basically represents the physical characteristic of interest with an uncertainty. This uncertainty is a measure of the inspection quality and can be quantified by expressing the measurement uncertainty. The internationally accepted rules for calculating measurement uncertainty are well established and can be applied straightforwardly in many practical cases. Nevertheless, the calculations require the occasionally time-consuming development of an individually suitable measurement model. This contribution attempts to emphasize proposals for modelling the non-destructive depth measurement of tendons in concrete using the ultrasonic echo technique. The proposed model can serve as guideline for the determination of the quality of the measured information in future comparable inspection scenarios.
Approaches to the development of a (GUM-) measurement model for the calculation of measurement uncertainties in the localization of tendons in concrete structures using the ultrasonic echo method including demonstration were presented.
The round robin test investigated the reliability users can expect for AlSi10Mg additive manufactured specimens by laser powder bed fusion through examining powder quality, process parameter, microstructure defects, strength and fatigue. Besides for one outlier, expected static material properties could be found. Optical microstructure inspection was beneficial to determine true porosity and porosity types to explain the occurring scatter in properties. Fractographic analyses reveal that the fatigue crack propagation starts at the rough as-built surface for all specimens. Statistical analysis of the scatter in fatigue using statistical derived safety factors concludes that at a stress of 36.87 MPa the fatigue limit of 107 cycles could be reached for all specimen with a survival probability of 99.999 %.
How much do we, the small-angle scatterers, influence the results of an investigation? What uncertainty do we add by our human diversity in thoughts and approaches, and is this significant compared to the uncertainty from the instrumental measurement factors?
After our previous Round Robin on data collection, we know that many laboratories can collect reasonably consistent small-angle scattering data on easy samples1. To investigate the next, human component, we compiled four existing datasets from globular (roughly spherical) scatterers, each exhibiting a common complication, and asked the participants to apply their usual methods and toolset to the quantification of the results https://lookingatnothing.com/index.php/archives/3274).
Accompanying the datasets was a modicum of accompanying information to help with the interpretation of the data, similar to what we normally receive from our collaborators. More than 30 participants reported back with volume fractions, mean sizes and size distribution widths of the particle populations in the samples, as well as information on their self-assessed level of experience and years in the field.
While the Round Robin is still underway (until the 25th of April, 2022), the initial results already show significant spread in the results. Some of these are due to the variety in interpretation of the meaning of the requested parameters, as well as simple human errors, both of which are easy to correct for. Nevertheless, even after correcting for these differences in understanding, a significant spread remains. This highlights an urgent challenge to our community: how can we better help ourselves and our colleagues obtain more reliable results, how could we take the human factor out of the equation, so to speak?
In this talk, we will introduce the four datasets, their origins and challenges. Hot off the press, we will summarize the anonymized, quantified results of the Data Analysis Round Robin. (Incidentally, we will also see if a correlation exists between experience and proximity of the result to the median). Lastly, potential avenues for improving our field will be offered based on the findings, ranging from low-effort yet somehow controversial improvements, to high-effort foundational considerations.
The acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based random variables to be included in reliability assessment. It is shown, how measured data representing time-invariant characteristics, in this case non-destructively measured inner geometrical dimensions, can be transferred into measurement results that are both comparable and quality-evaluated. The calculations are based on the rules provided in the guide to the expression of uncertainty in measurement (GUM). The GUM-framework is internationally accepted in metrology and can serve as starting point for the appropriate processing of measured data to be used in assessment. In conclusion, the effects of incorporating the non-destructively measured data into reliability analysis are presented using a prestressed concrete bridge as case-study.
Imaging in the shortwave-infrared region (SWIR, λ = 1000–2500 nm) has the potential to enable deep tissue imaging with high resolution. Critical to the development of these Methods is the identification of low molecular weight, biologically compatible fluorescent probes that emit beyond 1000 nm.
Exchanging the bridging oxygen atom on the xanthene scaffold (C10’ position) with electron withdrawing groups has been shown to lead to significant redshifts in absorbance and emission. Guided by quantum chemistry computational modeling studies, we investigated the installation of a Ketone bridge at the C10’ position. This simple modification extends the absorbance maxima to 860 nm and the emission beyond 1000 nm, albeit with reduced photon output. Overall, These studies demonstrate that broadly applied xanthene dyes can be extended into the SWIR range.
"The reassessment of bridges continues to take great importance both nationally and internationally. A major challenge is to find computation models reflecting the actual properties of the considered structures sufficiently accurate. Besides regular inspections, the conduction of advanced measurements is suitable to generate reliable information about a structure to be assessed. Prior to incorporating measurement results in reassessment, the relevance, the trueness, and the precision of the measured information needs to be stated. On the one hand, the use of information whose quality has not been assessed can lead to errors with serious consequences. On the other, the measurement of irrelevant information is inefficient. Although the use of measured data in assessment is currently mostly unregulated, their appreciation in reliability analyses is beneficial since the built environment can be assessed more realistically. Utilizing NDT in reassessment has the potential to extend remaining lifetimes of a structure, save resources, and improve infrastructural availabilities. The power of judgment regarding the decision on the reliability of an existing structure can be increased.
In this contribution, an approach is outlined to process non-destructively gathered measurement data in a comparableway in order to include themeasured information in probabilistic reliability assessments of existing structures. An essential part is the calculation of measurement uncertainties. The effect of incorporating evaluated NDT-results is demonstrated by means of a prestressed concrete bridge and GPR measurements conducted on this bridge as a case-study. The bridge is assessed regarding SLS Decompression using the NDT-results."
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.