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A stochastic model for fatigue short crack growth is presented. It takes into account the interaction between the crack-tip plastic zone and grain boundaries. The process is Markovian. It is completely described by the crack length and the size of the plastic zone. The integro-differential equation giving the evolution of the transition probability distribution is derived.
In this article a comparative study of the behaviour of
different commercial anti-graffiti on natural stone and
brick is presented. 8 different European substrates were
selected and 4 commercial anti-graffiti of different
chemical nature were applied on these substrates. The
variations of their hydric properties and aspect (colour
and gloss) with regard to the untreated substrates were
later studied in the laboratory.
The results obtained permitted to assess the suitability of
4 of the main types of chemical formulations employed
to be used as anti-graffiti. This study concludes that the
sacrificial anti-graffiti with polymeric paraffins in its
composition presents the lowest reductions of the hydric
properties of the studied substrates, being also the
variations in colour the least perceptible.
An der BAM wurde Anfang 1996 die GUM-konforme Ermittlung und Angabe der Unsicherheit quantitativer Prüfergebnisse eingeführt. Nach einem kurzen Überblick über die wesentlichen Schritte und die bisherigen Erfahrungen wird die Ermittlung der Unsicherheit an Beispielen aus zwei Schwerpunktsbereichen der Prüftätigkeit der BAM vorgestellt: atomspektrometrische Analyse anorganischer Materialien und mechanisch-technologische Werkstoffprüfung. Abschließend wird ein Ausblick auf die Ermittlung der Unsicherheit qualitativer Prüfergebnisse gegeben. Diese Arbeit ist ein ergänzender Beitrag zur Thematik des tm-Sonderheftes über GUM-konforme Auswertung von Messungen, das im Januar 2001 erschienen ist.
At BAM, GUM-compliant evaluation and expression of uncertainty for quantitative test results was introduced in early 1996. After a brief overview of essential steps and experience so far, uncertainty evaluation is presented for examples from two main fields of BAM´s testing activities: analysis of inorganic materials by atomic spectrometry and mechanical materials testing using tensile/compression testing machines. Finally, an outlook is given on the evaluation of uncertainty for qualitative test results. This paper is a supplementary contribution to the topic of the tm special edition on GUM-compliant evaluation of measurements published January 2001.
Volume fraction determination of discretely oriented disc-shaped precipitates in transmission mode
(2017)
In der vorliegenden Arbeit wurde eine Formel zur Berechnung des Volumenantei ls dis kret orientierter scheibenförmiger Phasen für die Untersuchung im Transmissionsmodus hergeleitet, validiert und mit bereits veröffent lichen Formeln verglichen. Bei diesem Ver gleich konnte gezeigt werden,dass die in zahl reichen Veröffentlichungen benutzte Formel von E. E. Underwood für die Volumenanteils bestimmungbeidiskret orientierten Ausschei dungsphasen nicht benutzt werden sollte, da sie den Volumenanteil deutlich unterschätzt.
Die Ursache liegt in der von E.E. Underwood getroffenen Annahme, dass die kreisförmigen Scheiben regellos angeordnet und nicht aus gerichtet sind, was in Al-Legierungen in der Regel nicht der Fall ist.
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.