TY - JOUR A1 - Schneider, Ronald A1 - Fischer, J. A1 - Bügler, M. A1 - Nowak, M. A1 - Thöns, S. A1 - Borrmann, A. A1 - Straub, D. T1 - Assessing and updating the reliability of concrete bridges subjected to spatial deterioration - principles and software implementation N2 - Inspection and maintenance of concrete bridges is a major cost factor in transportation infrastructure, and there is significant potential for using information gained during inspection to update predictive models of the performance and reliability of such structures. In this context, this paper presents an approach for assessing and updating the reliability of prestressed concrete bridges subjected to chloride-induced reinforcement corrosion. The system deterioration state is determined based on a Dynamic Bayesian Network (DBN) model that considers the spatial variability of the corrosion process. The overall system reliability is computed by means of a probabilistic structural model coupled with the deterioration model. Inspection data are included in the system reliability calculation through Bayesian updating on the basis of the DBN model. As proof of concept, a software prototype is developed to implement the method presented here. The software prototype is applied to a typical highway bridge and the influence of inspection information on the system deterioration state and the structural reliability is quantified taking into account the spatial correlation of the corrosion process. This work is a step towards developing a software tool that can be used by engineering practitioners to perform reliability assessments of ageing concrete bridges and update their reliability with inspection and monitoring data. KW - Structural reliability KW - Dynamic Bayesian Networks KW - Spatial deterioration KW - Inspection KW - Monitoring general KW - Analysis and design methods KW - Reinforcement KW - Corrosion KW - Prestressed concrete PY - 2015 DO - https://doi.org/10.1002/suco.201500014 SN - 1464-4177 VL - 16 IS - 3 SP - 356 EP - 365 PB - Ernst & Sohn CY - Berlin AN - OPUS4-34336 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pereira, P.M.R. A1 - Carvalho, José Joao A1 - Silva, S. A1 - Cavaleiro, J.A.S. A1 - Schneider, Rudolf A1 - Fernandes, R. A1 - Tomé, J.P.C. T1 - Porphyrin conjugated with serum albumins and monoclonal antibodies boosts efficiency in targeted destruction of human bladder cancer cells N2 - The synthesis of a novel PS conjugated with bovine and human serum albumin (BSA and HSA) and a monoclonal antibody anti-CD104 is reported, as well as their biological potential against the human bladder cancer cell line UM-UC-3. No photodynamic effect was detected when the non-conjugated porphyrin was used. Yet, when it was coupled covalently with the mAb anti-CD104, BSA and HSA, the resulting photosensitizer conjugates demonstrated high efficacy in destroying the cancer cells, the mAb anti-CD104 efficacy overruling the albumins. KW - Rinderserumalbumin KW - BSA KW - Monoklonale Antikörper KW - Blasenkrebs KW - Konjugate KW - Conjugates KW - Therapeutische Antikörper KW - CD104 PY - 2014 DO - https://doi.org/10.1039/c3ob42082e SN - 1477-0520 SN - 1477-0539 VL - 12 IS - 11 SP - 1804 EP - 1811 PB - RSC CY - Cambridge AN - OPUS4-30451 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - RPRT A1 - Schneider, Ronald A1 - Fischer, J. A1 - Straub, D. A1 - Thöns, S. A1 - Bügler, M. A1 - Borrmann, A. T1 - Intelligente Bauwerke - Prototyp zur Ermittlung der Schadens- und Zustandsentwicklung für Elemente des Brückenmodells N2 - Dieser Bericht beschreibt ein Systemmodell für eine integrale Ermittlung und Prognose der Schadens- und Zustandsentwicklung der Elemente eines Brückensystems unter Berücksichtigung von Ergebnissen aus Inspektionen und Überwachung. Das Systemmodell wurde anhand eines ausgesuchten Spannbetonüberbaus in einzelliger Kastenbauweise entwickelt. Es besteht aus zwei integralen Teilmodellen: ein Modell zur Beschreibung des Systemschädigungszustandes und ein Modell zur Beschreibung der Standsicherheit. Für die Modellierung des stochastischen Systemschädigungszustandes eines Brückensystems werden dynamische Bayes'sche Netze (DBN) vorgeschlagen. Dieser Ansatz ermöglicht es, alle relevanten Schädigungsprozesse und deren stochastische Abhängigkeiten zu berücksichtigen. Ein wesentlicher Vorteil dieses Ansatzes ist es, dass DBN ideal dafür geeignet sind, Bayes'sche Aktualisierungen auf Grundlage von Informationen aus Inspektionen und Überwachungsmaßnahme auf eine effiziente und robuste Art und Weise durchzuführen. Der DBN-Ansatz ist deshalb für die Entwicklung von Software für das Erhaltungsmanagement von alternden Brückenbauwerken, die vom Benutzer keine vertieften Kenntnisse der Zuverlässigkeitstheorie verlangt, ideal geeignet. Für die Modellierung der Standsicherheit eines alternden Kastenträgers wird vereinfachend Biegeversagen des globalen Längssystems betrachtet. Zur Berechnung der maximalen Traglast eines Kastenträgers infolge des Systemschädigungszustandes wird ein plastisch-plastisches Verfahren eingesetzt, wobei die Beanspruchungen mittels der Fließgelenktheorie unter Ausnutzung der plastischen Beanspruchbarkeit der Querschnitte des Kastenträgers ermittelt werden. Ein Kastenträger versagt, wenn sich durch die Ausbildung einer ausreichend großen Anzahl von Fließgelenken eine kinematische Kette ausbildet. Dieser Modellierungsansatz berücksichtigt Redundanzen, die sich aus der plastischen Beanspruchbarkeit der Querschnitte und der statischen Unbestimmtheit eines Kastenträgers ergeben. Zum Nachweis der praktischen Einsetzbarkeit des entwickelten Systemmodells wurde ein Software-Prototyp entwickelt, der eine intuitiv benutzbare graphische Benutzeroberfläche (Front-End) mit einem Berechnungskern (Back-End) koppelt. Die aktuelle Version des Software-Prototyps implementiert ein Modell der chloridinduzierten Bewehrungskorrosion und ein Tragwerksmodell, welches das Verfahrens der stetigen Laststeigerung zur Bestimmung der maximalen Traglast des Kastenträgers auf der Grundlage eines Finite-Elemente-Modells umsetzt. Zur Durchführung von Bayes'schen Aktualisierungen des Systemschädigungszustandes auf der Grundlage des DBN-Modells implementiert der Prototyp den Likelihood-Weighting-Algorithmus. Die entwickelte Architektur des Prototyps ermöglicht eine Erweiterung der Software um weitere Schädigungsprozesse. Der entwickelte Software-Prototyp ermöglicht Benutzern ohne vertiefte Kenntnisse der Zuverlässigkeitstheorie eine Berechnung des Einflusses von Bauwerksinformationen auf den Systemschädigungszustand und die Tragsicherheit eines Kastenträgers. Auf dieser Grundlage können effiziente Inspektions- und Überwachungsmaßnahmen identifiziert und das Erhaltungsmanagement optimiert werden. KW - Bridge KW - Condition survey KW - Damage KW - Deterioration KW - Development KW - Digital model KW - Durability KW - Engineering structure KW - Expert system KW - Forecast KW - Germany KW - Propability KW - Prototype KW - Reinforced concrete KW - Research report KW - Software KW - Stability KW - Stochastic process PY - 2015 UR - http://bast.opus.hbz-nrw.de/volltexte/2015/1615/ SN - 978-3-95606-190-5 SN - 0943-9293 VL - 117 SP - 1 EP - 74 PB - Carl Schünemann Verlag GmbH CY - Bergisch Gladbach AN - OPUS4-35193 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Freitas, R. A1 - Almeida, Ângela A1 - Calisto, V. A1 - Velez, C. A1 - Moreira, A. A1 - Schneider, Rudolf A1 - Esteves, V.I. A1 - Wrona, F. J. A1 - Soares, A.M.V.M. A1 - Figueira, E. T1 - How life history influences the responses of the clam Scrobicularia plana to the combined impacts of carbamazepine and pH decrease N2 - In the present study, the bivalve Scrobicularia plana, collected from two contrasting areas (pristine location and mercury contaminated area), was selected to assess the biochemical alterations imposed by pH decrease, carbamazepine (an antiepileptic) and the combined effect of both stressors. The effects on oxidative stress related biomarkers after 96 h exposure revealed that pH decrease and carbamazepine induced alterations on clams, with greater impacts on individuals from the contaminated area which presented higher mortality, higher lipid peroxidation and higher glutathione S-transferase activity. These results emphasize the risk of extrapolating results from one area to another, since the same species inhabiting different areas may be affected differently when exposed to the same stressors. Furthermore, the results obtained showed that, when combined, the impact of pH decrease and carbamazepine was lower than each stressor acting alone, which could be related to the defence mechanism of valves closure when bivalves are under higher stressful conditions. KW - Ocean acidification KW - Biomarkers KW - Oxidative stress KW - Bivalves KW - Pharmaceutical drugs PY - 2015 DO - https://doi.org/10.1016/j.envpol.2015.03.023 SN - 0269-7491 SN - 0013-9327 SN - 1873-6424 VL - 202 SP - 205 EP - 214 PB - Elsevier CY - New York, NY [u.a.] AN - OPUS4-33818 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ecke, Alexander A1 - Westphalen, Tanja A1 - Hornung, J. A1 - Voetz, M. A1 - Schneider, Rudolf T1 - A rapid magnetic bead-based immunoassay for sensitive determination of diclofenac N2 - Increasing contamination of environmental waters with pharmaceuticals represents an emerging threat for the drinking water quality and safety. In this regard, fast and reliable analytical methods are required to allow quick countermeasures in case of contamination. Here, we report the development of a magnetic bead-based immunoassay (MBBA) for the fast and cost-effective determination of the analgesic diclofenac (DCF) in water samples, based on diclofenac-coupled magnetic beads and a robust monoclonal anti-DCF antibody. A novel synthetic strategy for preparation of the beads resulted in an assay that enabled for the determination of diclofenac with a significantly lower limit of detection (400 ng/L) than the respective enzyme-linked immunosorbent assay (ELISA). With shorter incubation times and only one manual washing step required, the assay demands for remarkably shorter time to result (< 45 min) and less equipment than ELISA. Evaluation of assay precision and accuracy with a series of spiked water samples yielded results with low to moderate intra- and inter-assay variations and in good agreement with LC–MS/MS reference analysis. The assay principle can be transferred to other, e.g., microfluidic, formats, as well as applied to other analytes and may replace ELISA as the standard immunochemical method. KW - Immunoassay KW - Magnetic beads KW - Diclofenac KW - Water analysis KW - LC-MS/MS PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542346 DO - https://doi.org/10.1007/s00216-021-03778-7 SN - 1618-2650 VL - 414 SP - 1563 EP - 1573 PB - Springer CY - Heidelberg AN - OPUS4-54234 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schnur, C. A1 - Moll, J. A1 - Lugovtsova, Yevgeniya A1 - Schütze, A. A1 - Schneider, T. T1 - Explainable machine learning for damage detection - In carbon fiber composite plates under varying temperature conditions N2 - Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics. T2 - 48th Annual Review of Progress in Quantitative Nondestructive Evaluation CY - Online meeting DA - 28.07.2021 KW - Explainable machine learning KW - Guided waves KW - Damage detection KW - Structural health monitoring KW - Composite structures PY - 2021 SN - 978-0-7918-8552-9 DO - https://doi.org/10.1115/QNDE2021-75215 SP - 1 EP - 6 PB - American Society of Mechanical Engineers (ASME) CY - New York, NY AN - OPUS4-54219 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, J. A1 - Farris, L. A1 - Nolze, Gert A1 - Reinsch, Stefan A1 - Cios, G. A1 - Tokarski, T. A1 - Thompson, S. T1 - Microstructure evolution in Inconel 718 produced by powder bed fusion additive manufacturing N2 - Inconel 718 is a precipitation strengthened, nickel-based super alloy of interest for the Additive Manufacturing (AM) of low volume, complex parts to reduce production time and cost compared to conventional subtractive processes. The AM process involves repeated rapid melting, solidification and reheating, which exposes the material to non-equilibrium conditions that affect elemental segregation and the subsequent formation of solidification phases, either beneficial or detrimental. These variations are difficult to characterize due to the small length scale within the micron sized melt pool. To understand how the non-equilibrium conditions affect the initial solidification phases and their critical temperatures, a multi-length scale, multi modal approach has been taken to evaluate various methods for identifying the initial phases formed in the as-built Inconel 718 produced by laser-powder bed fusion (L-PBF) additive manufacturing (AM). Using a range of characterization tools from the bulk differential thermal analysis (DTA) and x-ray diffraction (XRD) to spatially resolved images using a variety of electron microscopy tools, a better understanding is obtained of how these minor phases can be properly identified regarding the amount and size, morphology and distribution. Using the most promising characterization techniques for investigation of the as-built specimens, those techniques were used to evaluate the specimens after various heat treatments. During the sequence of heat treatments, the initial as-built dendritic structures recrystallized into well-defined grains whose size was dependent on the temperature. Although the resulting strength was similar in all heat treated specimens, the elongation increased as the grain size was refined due to differences in the precipitated phase distribution and morphology. KW - Metal additive manufacturing KW - Inconel 718 KW - Heat treatment KW - Grain boundary precipitates KW - Laves phase PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542758 DO - https://doi.org/10.3390/jmmp6010020 SN - 2504-4494 VL - 6 IS - 1 SP - 1 EP - 20 PB - MDPI CY - Basel AN - OPUS4-54275 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schnur, C. A1 - Goodarzi, P. A1 - Lugovtsova, Yevgeniya A1 - Bulling, Jannis A1 - Prager, Jens A1 - Tschöke, K. A1 - Moll, J. A1 - Schütze, A. A1 - Schneider, T. T1 - Towards interpretable machine learning for automated damage detection based on ultrasonic guided waves N2 - Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. KW - Composite structures KW - Structural health monitoring KW - Carbon fibre-reinforced plastic KW - Interpretable machine learning KW - Automotive industry PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-542060 DO - https://doi.org/10.3390/s22010406 SN - 1424-8220 VL - 22 IS - 1 SP - 1 EP - 19 PB - MDPI CY - Basel AN - OPUS4-54206 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lesny, K. A1 - Arnold, P. A1 - Sorgatz, J. A1 - Schneider, Ronald T1 - Wie sicher sind unsere Bauwerke? - Strukturpapier des Arbeitskreises 2.15 der DGGT „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ N2 - Der zukünftige Eurocode 7 wird ausdrücklich die Nutzung zuverlässigkeitsbasierter Methoden in der geotechnischen Planung und Bemessung erlauben. In Deutschland gibt es bisher kaum Erfahrung in der praktischen Anwendung derartiger Verfahren und entsprechend sind die Vorbehalte gegenüber diesen Methoden oft groß. Der neue DGGT-Arbeitskreis (AK) 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ hat sich zum Ziel gesetzt, durch praxisorientierte Anleitungen und Empfehlungen sowie begleitende Aus- und Weiterbildungsangebote den praktischen Zugang zu diesen Verfahren zu unterstützen. Ziel ist es, Möglichkeiten und Grenzen zu verdeutlichen sowie vor allem ihre Potenziale zu erschließen. In dem vorliegenden Beitrag werden allgemeine Grundlagen und die zukünftigen Arbeitsfelder des AK 2.15 vorgestellt. Ausgehend von der Einführung relevanter Fachbegriffe wird zunächst die Einbettung zuverlässigkeitsbasierter Verfahren in den aktuellen Normungs- und Regelungskontext aufgezeigt. Anschließend werden anhand des Lebenszyklus eines geotechnischen Bauwerks die Unsicherheiten in den geotechnischen Prognosen und Bewertungen beschrieben. Daran anknüpfend wird aufgezeigt, an welchen Stellen zuverlässigkeitsbasierte Methoden als mögliches Werkzeug sinnvoll genutzt werden können, um Ingenieur:innen, Bauherr:innen und Prüfer:innen in Nachweis- und Entscheidungsprozessen zu unterstützen. Zu den sich daraus ableitenden Arbeitsthemen werden durch den AK 2.15 zukünftig Empfehlungen erarbeitet und sukzessive veröffentlicht KW - Brückensicherheit KW - Sicherheit KW - Wahrscheinlichkeit KW - Zuverlässigkeit KW - Bemessung KW - Bewertung KW - Offshore Wind PY - 2023 DO - https://doi.org/10.1002/gete.202300014 VL - 46 IS - 3 SP - 153 EP - 164 PB - Ernst & Sohn GmbH CY - Berlin AN - OPUS4-58208 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schnur, C. A1 - Moll, J. A1 - Lugovtsova, Yevgeniya A1 - Schütze, A. A1 - Schneider, T. ED - Kundu, T. ED - Reis, H. ED - Ihn, J.-B. ED - Dzenis, Y. T1 - Explainable Machine Learning for Damage Detection: in Carbon Fiber Composite Plates Under Varying Temperature Conditions N2 - Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics. T2 - 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation CY - Online meeting DA - 28.07.2021 KW - Explainable machine learning KW - Guided waves KW - Damage detection KW - Structural health monitoring KW - Composite structures PY - 2021 SN - 978-0-7918-8552-9 DO - https://doi.org/10.1115/QNDE2021-75215 VL - QNDE2021-75215 SP - 1 EP - 6 PB - The American Society of Mechanical Engineers (ASME) CY - New York, USA AN - OPUS4-56723 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -