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Information about an existing structure can be collected at certain costs to evaluate the reliability and condition as realistically as necessary. This information can be relevant or irrelevant, true or biased, precise or imprecise. The incorporation of relevant and quality-assessed measured information into reliability reassessment offers the chance to extend remaining lifetimes and support decision making about optimal actions or maintenance strategies. This paper shows recent developments in a national research project that aims to produce a guideline on the NDT-based, structure-specific modification of partial safety factors. The general methodology, results from recalculations according to the Eurocodes and metrologically solvable testing tasks relevant in the recalculation of the concrete bridges are shown and compared with the non-destructive testing methods applicable to concrete bridges. A case study is used to demonstrate that as-built drawings, in this case of the positions of tendons and shear reinforcement, can be verified using the radar method.
The bond between polymer fibers and the surrounding cementitious matrix is essential for the development of concrete reinforcement. The single fiber pull-out test (SFPT) is the standard characterization technique for testing the bond strength. However, the different phases of debonding cannot be distinguished by the SFPT. This study investigates the debonding of different polymer fibers from the surrounding cementitious matrix with a modified SFPT and proposes methods to change the SFPT setup to generate more valuable information on the debonding mechanism. The SFPT was equipped with linear variable differential transformers (LVDT), digital image correlation (DIC) and acoustic emission (AE) analysis. The results demonstrate that the modified SFPT allows a better understanding of the different phases of debonding during fiber pull-out. Furthermore, bond strength values calculated by different methods reveal that the chemical bond of the investigated polymers is not different as reported by previous studies. Deformation measurements performed using LVDTs and DIC are suitable measuring techniques to characterize the debonding mechanism in SFPT. A correlation between recorded AE and debonding phases was not found.
Bacterial biofilms pose serious problems in medical and industrial settings. One of the major societal challenges lies in the increasing resistance of bacteria against biocides used in antimicrobial treatments, e.g., via overabundant use in medicine, industry, and agriculture or cleaning and disinfection in private households. Hence, new efficient bacteria-repellent strategies avoiding the use of biocides are strongly desired. One promising route to achieve bacteria-repellent surfaces lies in the contactless and aseptic large-area laser-processing of technical surfaces. Tailored surface textures, enabled by different laser-processing strategies that result in topographic scales ranging from nanometers to micrometers may provide a solution to this challenge. This article presents a current state-of-the-art review of laser-surface subtractive texturing approaches for controlling the biofilm formation for different bacterial strains and in different environments. Based on specific properties of bacteria and laser-processed surfaces, the challenges of anti-microbial surface designs are discussed, and future directions will be outlined.
Materials that support natural biodiversity on their surfaces can compensate for human activities that have a negative impact on nature and thus contribute to a carbon-neutral and nature-positive world. Specifically designing bioreceptive materials which favor the growth of biofilms on their surface is an approach complementing conventional, macroscopic green façades. But what exactly characterizes a bioreceptive substrate and how do biofilm and substrate interact? How and why does a spontaneous colonization and the formation of biofilms take place? What are biofilms and how can they be established in a laboratory setting? How can this existing knowledge be transferred to the artificial stone concrete so that this material can be tuned to increase (or decrease) its bioreceptivity?
This review paper aims at summarizing the existing state of knowledge on bioreceptive concrete and pointing out inconsistencies and contradictions which can only be removed by more interdisciplinary research in the field.
A systematic study on a face‐centered cubic‐based compositionally complex alloy system Al–Co–Cr–Cu–Fe–Ni in its single‐phase state is carried out, where a mother senary compound Al₈Co₁₇Cr₁₇Cu₈Fe₁₇Ni₃₃ and five of its suballoys, obtained by removing one element at a time, are investigated and exhaustively analyzed determining the contribution of each alloying element in the solid solution. The senary and the quinaries are compared using experimental techniques including X‐ray absorption spectroscopy, X‐ray diffraction, transmission electron microscopy, and first principles hybrid Monte Carlo/molecular dynamics simulations. Chemical short‐range order and bond length distances have been determined both at the experimental and computational level. Electronic structure and local atomic distortions up to 5.2 Å have been correlated to the microhardness values. A linear regression model connecting hardness with local lattice distortions is presented.
Ratiometric green–red fluorescent nanosensors for fluorometrically monitoring pH in the acidic range were designed from 80 nm-sized polystyrene (PS) and silica (SiO2) nanoparticles (NPs), red emissive reference dyes, and a green emissive naphthalimide pH probe, analytically and spectroscopically characterized, and compared regarding their sensing performance in aqueous dispersion and in cellular uptake studies. Preparation of these optical probes, which are excitable by 405 nm laser or LED light sources, involved the encapsulation of the pH-inert red-fuorescent dye Nile Red (NR) in the core of self-made carboxylated PSNPs by a simple swelling procedure and the fabrication of rhodamine B (RhB)-stained SiO2-NPs from a silane derivative of pH-insensitive RhB. Subsequently, the custom-made naphthalimide pH probe, that utilizes a protonation-controlled photoinduced electron transfer process, was covalently attached to the carboxylic acid groups at the surface of both types of NPs. Fluorescence microscopy studies with the molecular and nanoscale optical probes and A549 lung cancer cells confirmed the cellular uptake of all probes and their penetration into acidic cell compartments, i.e., the lysosomes, indicated by the switching ON of the green naphthalimide fluorescence. This underlines their suitability for intracellular pH sensing, with the SiO2-based nanosensor revealing the best performance regarding uptake speed and stability.
Recovery is an important measure for seal applications representing to which extent the elastomer regains its initial shape after deformation and release of an applied force. Compression set (CS) indicates the degree of recovery. Ethylene propylene diene rubber (EPDM) was aged at 75 ◦C, 100 ◦C, 125 ◦C and 150 ◦C for different ageing times up to five years and compression set measurements were performed at different times after disassembly and after additional tempering. Short- and long-term recovery up to one year after release for samples aged at 125 ◦C and 150 ◦C was also studied. To assess the curvature in the Arrhenius diagram that may occur due to non-sufficiently aged specimens, a degradation-rate based model was fitted to the CS data after tempering. For each ageing temperature, two decay fit functions were proposed, each with an activation energy and a corresponding degradative process. The influence of ageing on the leak-tightness after fast small partial release is investigated and estimated through the analysis of the shift factors from time temperature superposition (TTS) of CS measurements at different times after disassembly. Shift factors of CS measurement after 1 s and after additional tempering are in good agreement.
Corrosion monitoring of reinforced concrete structures:The DGZfP specification B12 Collaboration
(2023)
Corrosion monitoring of reinforced or prestressed concrete structures has becomeincreasingly important in recent years. Areas of application include componentsthat are no longer accessible after completion or where potential fieldmeasurements cannot be carried out due to existing coatings. Corrosion monitoringcan also be used to monitor the progress of corrosion in corroding systems, e.g. to prove the success of repair measures according to repair principle 8 in accordancewith EN 1504‐9 or repair method 8.3 in accordance with the DIBt repair guideline.It also could be used to prove the functionality of cathodic corrosion protectionsystems in accordance with ISO 12696. Despite the increasing importance ofcorrosion monitoring, no guidelines orrecommendations existed until 2018. Thisgap was closed by the English version of specification B12,“Corrosion Monitoringof Reinforced and Prestressed Concrete Structures,”of the German Society for Non‐Destructive Testing, which was published in 2021. This article introducesspecification B12 by explaining the basicmeasurement principles and illustratingthe potential of corrosion monitoring in new and existing buildings.
The embrittlement of metallic alloys by liquid metals leads to catastrophic material failure and severely impacts their structural integrity. The weakening of grain boundaries by the ingress of liquid metal and preceding segregation in the solid are thought to promote early fracture. However, the potential of balancing between the segregation of cohesion-enhancing interstitial solutes and embrittling elements inducing grain boundary decohesion is not understood. Here, we unveil the mechanisms of how boron segregation mitigates the detrimental effects of the prime embrittler, zinc, in a Σ5 [0 0 1] tilt grain boundary in α −Fe (4 at.% Al). Zinc forms nanoscale segregation patterns inducing structurally and compositionally complex grain boundary states. Ab-initio simulations reveal that boron hinders zinc segregation and compensates for the zinc induced loss in grain boundary cohesion. Our work sheds new light on how interstitial solutes intimately modify grain boundaries, thereby opening pathways to use them as dopants for preventing disastrous material failure.
This contribution contains the raw data used to compare experimental results with thermodynamic calculations using the CALPHAD method, which is related to the research article “The AlMo0.5NbTa0.5TiZr refractory high entropy superalloy: experimental findings and comparison with calculations using the CALPHAD method” [1] , and therefore this article can be used as a basis for interpreting the data contained therein. The AlMo0.5NbTa0.5TiZr refractory superalloy was characterized in the cast and annealed condition (1400 °C for 24 h) in order to measure grain size and to identify and measure the size and area fraction of the phases present. The raw data of this article include X-ray diffraction (XRD) measurements, microstructural characterization by scanning and transmission electron microscopy (SEM and TEM), and elemental analysis by energy dispersive X-ray spectroscopy (EDX). XRD includes the determination of phases and the lattice parameters (A2, B2, and hexagonal structure). Microstructural analysis by scanning and transmission electron microscopy includes (1) identification of composition, size, and volume fraction of the present phases and (2) determination of grain size. Based on these experimental data, it is possible to identify similarities and discrepancies with the data calculated using the CALPHAD method for the alloy under study in Ref. [1] , which provides the basis for better and more efficient development of reliable databases.
Standards play a crucial role in the transition to more sustainable production and consumption models by promoting the development of new markets for innovative products. In the case of the bioeconomy, standards are particularly relevant to ensure the sustainability of products and processes. Hence, standards represent an essential strategic tool to foster sustainable development. Furthermore, the standard-setting process brings together different stakeholders, which encourages research and the transfer of knowledge to industrial practice. In particular, scientific publications assist the standardisation process by providing scientific evidence. This study investigates the role of science and regulation in the bioeconomy standardisation process. It first provides a general analysis of bioeconomy-related standards, followed by an analysis of the regulations and publications referenced in the standards. Our analysis discloses a strong use of scientific knowledge in the bioeconomy standardisation process and demonstrates the readiness to integrate novel academic findings into revised versions of the standards. In addition, it reveals that the interaction between science and regulation is an important driver for standardisation activities around the bioeconomy, needed to support sustainable transitions.
Liquid-metal embrittlement (LME) of galvanized (Zn-coated) advanced high-strength steels is a long-known problem in materials science. Here we reveal the initial microstructural processes underneath the Zn-coating that lead to LME-microcrack initiation in the steel substrate. We track the microstructural evolution during the first tens of milliseconds and find pronounced signatures of Fe-Zn intermetallic precipitation in both ferrite grain boundaries and at internal ferrite-oxide phase boundaries. In concert with novel CALPHAD-integrated density-based thermodynamic modelling, we demonstrate that Zn-rich intermetallic phase-nucleation can occur at markedly low processing temperatures due to a segregation transition. We show that a small Znenrichment caused by Zn bulk-diffusion during the initial temperature rise in a joining process is sufficient to induce the segregation transition and subsequent nucleation of Fe-Zn intermetallic grain-boundary phases, which the experiments link to crack initiation sites. These findings direct focus onto LME-controlling microstructural and thermodynamic phenomena at temperatures below the ductility trough and the austenite formation temperature.
Density-based phase-field (DPF) methods have emerged as a technique for simulating grain boundary thermodynamics and kinetics. Compared to the classical phase-field, DPF gives a more physical description of the grain boundary structure and chemistry, bridging CALPHAD databases and atomistic simulations, with broad applications to grain boundary and segregation engineering. Notwithstanding their notable progress, further advancements are still warranted in DPF methods. Chief among these are the requirements to resolve its performance constraints associated with solving fourth-order partial differential equations (PDEs) and to enable the DPF methods for simulating moving grain boundaries. Presented in this work is a means by which the aforementioned problems are addressed by expressing the density field of a DPF simulation in terms of a traditional order parameter field. A generic DPF free energy functional is derived and used to carry out a series of equilibrium and dynamic simulations of grain boundaries in order to generate trends such as grain boundary width vs. gradient energy coefficient, grain boundary velocity vs. applied driving force, and spherical grain radius vs. time. These trends are compared with analytical solutions and the behavior of physical grain boundaries in order to ascertain the validity of the coupled DPF model. All tested quantities were found to agree with established theories of grain boundary behavior. In addition, the resulting simulations allow for DPF simulations to be carried out by existing phase-field solvers.
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing.
Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust.
In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSs were deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https://github.com/BAMresearch/NFDI4IngScientificWorkflowRequirements), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.
Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction
(2023)
In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed by solving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling.
Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their tructural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management.
The performance of second generation ICP-TOFMS, equipped with a micro-channel plate (MCP) enabling multi-isotope detection, in terms of isotope ratio precision and instrumental isotopic fractionation (IIF) for (multi-)isotope ratio analysis was thoroughly assessed for B, Sr and Pb. Experimental isotope ratio precision of 0.14 % for 11B/10B intensity ratio, 0.15 % for 87Sr/86Sr intensity ratio and 0.07% for 208Pb/206Pb intensity ratio were obtained at high signal levels ($500 mg L−1) which is comparable to first generation ICP-TOFMS. The long-term stability of isotope ratios, measured over several hours and expressed as repeatability, is between 0.05 % and 1.8 % for B, Sr and Pb. The observed IIF per mass unit is negative for B (i.e., −11 % for 11B/10B) which is in accordance with measurements using sector field (MC) ICP-MS. But the observed IIF per mass unit is positive for Sr (i.e., 2 % for 87Sr/86Sr) and Pb (i.e., 4.5 % for 208Pb/206Pb) which is not in accordance with measurements using sector field (MC) ICP-MS. Furthermore, different IIFs per mass unit were observed for different isotope pairs of the same isotopic system (i.e., Sr, Pb) and adjacent isotopic systems (i.e., Pb vs. Tl). This and the observations from three-isotope plots for Sr and Pb show that ion formation, ion extraction, ion transmission, ion separation and ion detection in second generation ICP-TOFMS is subject to IIF that does not follow the known mass dependent fractionation laws and is possibly caused by mass independent fractionation and/or multiple (contradictory) fractionation processes with varying contributions. The non-mass dependent IIF behavior observed for second generation ICP TOFMS has profound consequences for the IIF correction of isotope raw data, including application of multi-isotope dilution mass spectrometry (IDMS) using ICP-TOFMS. Hence, only IIF correction models that correct also for mass independent fractionation are applicable to calculate reliable isotope ratios using second generation ICP-TOFMS. In the present study, reliable d11B values, and absolute B, Sr and Pb isotope ratios could be determined using the SSB approach in single-element solutions as well as in a mixture of B, Sr and Pb, where the isotopes were measured simultaneously.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
The present work is part of the AIFRI project (Artificial Intelligence For Rail Inspection), where we and our project partners train a neural network for defect detection and classification. Our goal at BAM is to generate artificial ultrasound and eddy current training data for the A.I. This paper has an exploratory nature, where we focus on the simulation of eddy current signals for head check cracks, one of the most important rail surface defects. The goal of this paper is twofold. On the one hand, we present our general simulation setup. This includes geometric models for head check cracks with features like branching and direction change, a model for the HC10 rail testing probe, and the configuration of the Faraday simulation software.
On the other hand, we use the Faraday software to simulate eddy current testing signals with a strong focus on the influence of the damage depth on the signal, while differentiating between different crack geometries. Here, we observe an early saturation effect of the test signal at a damage depth of 2 mm (at a crack angle of 25◦ to the surface). That is about 2 mm earlier than we would expect from measurements at a crack angle of 90◦. This behavior will be investigated further in a future paper. Finally, we interpolate the simulated signals in a two-step curve fitting process. With these interpolations we may generate eddy current test signals for any damage depth within the simulated range.