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Damage Identification using Experimental Modal Data through Sparsity Promoting Priors
No full textEngineering structures experience performance degradation due to progressive damage throughout their lifespan. Factors responsible for this include mechanical loading, electrochemical processes such as corrosion, and manufacturing and material defects introduced during construction, among others. Typically, damage manifests as cracks, failed prestressing cables, and similar issues, which can eventually lead to structural failure. The onset of damage is characterised by a localised reduction in stiffness in the affected
area. By using current data from the structure within its computational model, predictions about its ongoing damage state can be made, helping to prevent failures. Additionally, it is necessary to provide probabilistic estimates of these predictions, considering the presence of model discrepancy and noise in the data. Uncertainty propagation within the Bayesian inference framework helps in getting these estimates. The aim of this study is the stochastic localisation and quantification of damage. Parameters of a computer model that characterise structural damage are estimated using modal response data derived from Stochastic
Subspace Identification (SSI) performed on acceleration measurements. An inverse problem is formulated and solved using Bayesian inference. A linear elastic Finite Element (FE) computer model is used, reparameterised to incorporate damage parameters. A damage zone is defined as a sub-domain with a spatially varying damage field that models a reduction in the nominal Young’s modulus within that sub-domain. An arbitrary number of such zones are assumed to exist within the domain. Sparsity-promoting priors are applied to each zone, serving as a switch to signify the presence (on) or absence (off) of the respective zone
[Hirsh et al.]. During inference, these priors prevent over-parameterisation of the model and assist in model selection. The locations of damage zones, their number, and the local reduction in Young’s modulus together constitute the inferred parameter set. A reduced-order model for the sensed locations within the domain is created using the Iterative Improved Reduction System (IIRS) method [Friswell et al.]. The likelihood
function favors minimal errors in the eigenvalue problem when modal data, combined with the damage parameterised reduced-order model matrices, are inserted into the eigenvalue problem. Modal data, along with its uncertainty estimates, is obtained from SSI. The uncertainty is then propagated using the delta method to determine the uncertainty in the eigenvalue problem error. The posterior distribution of the damage parameters is sampled using Markov Chain Monte Carlo (MCMC) sampling. Experimental acceleration data from a T-shaped reinforced concrete structure is used to test the method. The structure is progressively damaged through increasing load cycles. Acceleration measurements are taken
after each load cycle, and simultaneously, the locations of observed cracks on the structure are documented. The damage locations identified in the computer model are then compared with the experimental observations. Although the proposed scheme is applied to cracks in this case, it can be extended to other forms of failure modes, which would be parameterised differently in the model
Towards Standardised Micro and Nanoplastics Analysis via Interlaboratory Comparisons: First Outlook of the VAMAS TWA 45 P3 Project
No full textThis talk is part of the stakeholder workshop of the PlasticTrace project held in September 2025 in Oslo. The presentation shows first results of the VAMAS ILC on nanoplastics. PP nanoparticles were given to the participants. These were asked to measure the mass or particle number or size of the PP nanoplastics. Various techniques such as DLS, FFF, Py-GC/MS, TED-GC/MS, PTA were used
XPS–SEM/EDS Tandem Analysis for the Elemental Composition of Functionalized Graphene Nanoplatelets
No full textOver the past decade, energy-dispersive X-ray spectrometry (EDS) with scanning electron microscopy (SEM) has advanced to enable the accurate analysis of light elements such as C, N, or O. For this reason, EDS is becoming increasingly interesting as an analytical method for the elemental analysis of functionalized graphene and could be an attractive alternative to Xray photoelectron spectroscopy (XPS), which is considered the most important method for elemental analysis. In this study, comparative XPS and EDS investigations under different excitation conditions are carried out on commercially available powders containing graphene particles with different morphologies. The slightly different XPS/HAXPES and EDS results can be explained by the different information depths of the methods and the functionalization of the particle surfaces. For the material with smaller graphene particles and higher O/C ratios, all methods reported a lower O/C ratio in pellets compared with the unpressed powder samples. This clearly shows that sample preparation has a significant influence on the quantification results, especially for such a type of morphology. Overall, the study demonstrates that EDS is a reliable and fast alternative to XPS for the elemental quantification of functionalized graphene particles, provided that differences in the information depth are taken into account. Particle morphology can be examined in parallel with quantitative element analysis, since EDS spectrometers are typically coupled with SEM, which are available in a huge number of analytical laboratories
GNN-DM: A Graph Neural Network Framework for Real-World Gas Distribution Mapping
No full textGas distribution mapping (GDM) is essential for industrial safety and environmental monitoring, as it enables real-time hazard detection and air quality assessment. Traditional GDM methods, such as kernel-based techniques, struggle to reconstruct complex gas plume dynamics accurately. While deep learning has shown promise for GDM, two critical challenges hinder its practical use: the scarcity of available training data and the incompatibility of conventional architectures with irregular sensor layouts. To address these limitations, we propose GNN-DM, a graph neural network-based model for GDM that incorporates the relational structure of sensor networks to infer high-resolution maps from minimal, irregular inputs. The model is pretrained on synthetic gas dispersion data generated from measured wind data and fine-tuned on two industrial datasets collected on a ferry car deck and in a hot rolling mill. Compared with established GDM techniques, GNN-DM achieves higher accuracy on synthetic and real-world data, highlighting the potential of graph-based learning for practical gas mapping applications
Arc-sidewall-attaching-driven control of swing arc motion in narrow gap GMAW
No full textA novel, event-driven approach to controlling the weaving motion in swing arc narrow gap GMAW is presented in this study. The control method is based on independently detecting the arc attachment event at each sidewall of the narrow groove to adjust the weaving motion in real time. Previous arc sensing approaches for swing arc principles are based on evaluating and comparing arc sensor readings collected during the dwell periods at each sidewall. Not only does this require the torch to be positioned at the groove centre and the arc motion to be symmetric, but previous methods have also been shown to rely on complex parametrization of control parameters. The newly presented approach is based on the real-time monitoring of the welding current progression during the approach of the arc towards the sidewall of the groove independently on each side. As soon as the arc attachment at the sidewall is detected based on a characteristic rise in the current signal, the weaving motion is stopped. For reference experiments in a 21-mm wide groove, the weaving angle amplitude is controlled and limited to 50° on both sides individually, resulting in stable process conditions and uniform sidewall fusion. It is further shown that the newly developed control method can successfully be applied to groove widths of 18 mm and 24 mm without reconfiguration of the control parameters, highlighting the flexibility of the approach
Material analysis of afghanistan liturgical quire (ALQ) conducted at the BAM
No full textThe lecture presents the results of the detailed material analysis of the inks and of the parchment of the Afghanistan Liturgical Quire (ALQ), conducted at the Bundesanstalt für Materialforschung und -Prüfung (BAM) in Berlin. Testing was conducted using the following techniques: 3-color photography, 3D-digital microscopy, scanning μ-X-ray fluorescence analysis (XRF), μ- Raman and FTIR spectroscopy. I will give a short explanation of the principles of the techniques and explain the choice of the instruments. The main task of our work at the BAM required characterization of the inks and comparison of the ink as well as the composition of parchment. For the ink characterization we have followed the protocol developed at the BAM. The standard protocol includes three non-invasive stages: a) near infrared photography (NIRR) to determine the ink type or its main component; b) X-ray fluorescence analysis (XRF) to determine a fingerprint for iron-gall inks or contaminants in carbon/plant inks; c) Raman spectroscopy if there is an indication that the inks are of a mixed type.
For parchment characterization, we usually employ µ-XRF-mapping and FTIR to gain information on specific treatments experienced by the parchment during its preparation and history
Rückholbarkeit Klärschlammaschen aus Langzeitlagern
No full textDas Institut für Siedlungswasserwirtschaft (ISA) der RWTH Aachen University sowie die Bundesanstalt für Materialforschung und -prüfung (BAM) untersuchen gemeinsam fünf deponierte KSA, die von drei Deponiebetreibern ausgebaut und zur Verfügung gestellt wurden sind. Im Fokus der Untersuchungen stehen die physikalische und chemische Charakterisierung der deponierten KSA sowie deren P-Rückgewinnungspotenzial bei der Behandlung in thermochemischen sowie nasschemischen Verfahren. Diese Ergebnisse werden im Vergleich mit „frischen“, also nicht deponierten KSA-Proben vergleichbarer Herkunft bewertet, um so den Einfluss der Lagerung auf die KSA zu erfassen
Data-driven nanomechanical study of filled fluoroelastomer aged in air and hydrogen atmosphere
No full textFluoroelastomer (FKM) composites are typically used as sealing materials in challenging non-ambient environments. Depending on the environment, two main aging mechanisms, chemical aging, and physical aging, can be identified. Chemical aging, the degradation of the elastomer, is present for example in thermal-oxidative conditions and can be directly observed as it affects the bulk. Physical aging, relaxation and rearrangement of the elastomers segmental conformation is commonly observed at elevated temperatures and effects predominantly the elastomer interphase. As a highly localized nanoscopic effect it is usually observed indirectly by phenomological approaches and not systematically understood. In this study, as a typical example for chemical aging, filled FKM was aged in air (150°C, 100 days). Physical aging of FKM was realized by exposure to chemically inert H2 (150°C, 50 bar, 100 days), since temperature and gas-induced swelling is known to promote physical aging. The effects of both conditions are directly compared with the initial unaged material. We use atomic force microscopy (AFM) force spectroscopy as a method to resolve nanoscopic heterogeneous FKM. With this method the effect of aging on the spatially distinguishable material phases was directly observed. In thermal oxidative aged FKM the matrix shows a decrease in van der Waals interactions and stiffness, indicating dehydrofluorination and chain scission. In H2 aged FKM, the development of an immobilized amorphous interphase (IAP) was observed, indicating physical aging. By additionally evaluating a larger data set with supervised machine learning, these observations were validated for a larger, statistically representative sample area, allowing conclusions to be drawn about the macroscopic behaviour of the material
Elucidation of the Laser Beam Energy Attenuation by the Vapor Plume Formation during High Power Laser Beam Welding
No full textIn high-power laser beam welding, a common phenomenon is the formation of a keyhole caused by the rapid evaporation of the material. Under atmospheric pressure, this evaporation generates a vapor plume that interacts with the laser beam, leading to energy attenuation and scattering of the laser radiation along its path. These interactions affect the stability of the process and the overall weld quality. This study investigates the influence of the vapor plume on the weld pool and keyhole dynamics during high-power laser beam welding of AlMg3 aluminum alloy through experimental and numerical approaches. The primary goal is to identify key vapor plume characteristics, particularly its length fluctuations, and to improve the accuracy of the numerical models. To achieve this, an algorithm was developed for the automated measurement of the vapor plume length using high-speed imaging and advanced data processing techniques. The measured plume length is then used to estimate the additional vapor heating and laser energy attenuation using the Beer–Lambert law. A refined numerical CFD model, incorporating 3D transient heat transfer, fluid flow, and ray tracing, was developed to evaluate the vapor plume’s impact. Results show that already the time-averaged plume length effectively captures its transient influence and aligns well with experimental weld seam geometries. Additionally, energy scattering and absorption caused by the vapor plume led to a wider weld pool at the top surface. The study also shows an increased percentage of keyhole collapses due to the reduced laser power absorption at the keyhole bottom, further highlighting the importance of accurately modeling vapor plume effects
Understanding bioreceptivity of concrete: material design and characterization
No full textThe climate crisis is driving an increasing demand for ecologically oriented concepts. In the building sector, this demand includes not only the use of environmentally friendly materials but also the greening of urban areas. One promising approach is the development of bioreceptive concrete façades, which support the growth of green biofilms directly on their surfaces. These innovative façades are anticipated to deliver benefits comparable to those of macroscopically greened façades, such as enhanced biodiversity and improved air quality, while offering the advantages of being more self-sustaining and stable systems once fully established.
However, the development of bioreceptive concrete presents substantial challenges. Due to the interdisciplinarity and novelty of this field, standardized methods for material characterization and bioreceptivity assessment are currently lacking. This study proposes an approach for evaluating surface properties crucial for bioreceptivity, developed on differently structured samples of ultra-high-performance concrete (UHPC). Existing methods and standards from concrete technology are critically reviewed and, where necessary, modified to meet the unique requirements of measuring bioreceptive material properties. Special attention is given to the surface pH value and water retention characteristics, as these are essential for promoting microbial growth and ensuring the long-term stability of green biofilms. The observed surface characteristics vary according to the imprinted surface structures, offering a spectrum of material properties and enabling the evaluation of their impact on bioreceptivity. The findings presented form the foundation for subsequent laboratory weathering experiments, which will be discussed in a complementary publication