TY - CONF A1 - Sanayei, Mohammad Hassan A1 - Farhat, A. A1 - Luu, L.-H. A1 - Werner, L. A1 - Rettinger, C. A1 - Philippe, P. A1 - Cuéllar, Pablo T1 - Micromechanical framework for a 3d solid cohesion model - implementation, validation and perspectives N2 - This article presents a solid cohesion model for the simulation of bonded granular assemblies in the frame of 3D discrete element approaches (DEM). A simple viscoplastic cohesion model for 2D geometries is extended to 3D conditions, while its yield criterion is generalized as a hyper-surface in the space of bond solicitations to include torsional moments. The model is then calibrated using experimental results of uniaxial traction at both the microscopic and macroscopic scales with an artificial granular cohesive soil. The paper finally presents some simulated results on the macromechanical sample traction application and briefly discusses the model's current limitations and promising prospects for subsequent works. T2 - VII International Conference on Particle-Based Methods PARTICLES 2021 CY - Hamburg, Germany DA - 04.10.2021 KW - Granular Cohesive Materials KW - DEM KW - Micromechanical Tensile Failure KW - Macromechanical Sample Strength PY - 2021 SP - 1 EP - 10 AN - OPUS4-53716 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Cuéllar, Pablo A1 - Baeßler, Matthias T1 - Probabilistic approaches to the design and assessment of offshore foundations considering spatially varying site conditions N2 - In this contribution, we consider two applications in which probabilistic approaches can potentially complement or enhance the design and assessment of offshore wind turbine foundations. First, we illustrate in a numerical example that probabilistic modelling can be helpful in dealing with chang-es in turbine locations during the planning phase of an offshore wind farm. In this case, spatial probabilistic modelling of the ground conditions enables (a) an inference of the soil properties at the modified turbine location from field data collected at different locations across an offshore wind farm site and (b) an optimisation of further site investigations. Second, we discuss the uncer-tainties and risks associated with the installation of large diameter monopiles in soils with hetero-geneities such as strong layers and/or embedded boulders. Subsequently, we present a concept for modelling, understanding, and managing these risks based on a probabilistic model of the subgrade conditions, monopile, and subgrade-pile-interaction. T2 - 3rd International Conference on Structural Integrity for Offshore Energy Industry (SI 2021) CY - Online meeting DA - 15.11.2021 KW - Probabilistic modelling KW - Spatially varying ground conditions KW - Foundation reliability analysis KW - Monopile installation risks PY - 2022 SN - 978-1-8383226-3-2 SP - 9 EP - 17 AN - OPUS4-54531 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Straub, D. ED - Matos, J.C. ED - Lourenço, P.B. ED - Oliveira, D.V. ED - Branco, J. ED - Proske, D. ED - Silva, R.A. ED - Sousa, H.S. T1 - Cumulative Failure Probability of Deteriorating Structures: Can It Drop? N2 - The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability. Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model. T2 - 18th International Probabilistic Workshop (IPW 2020) CY - Online meeting DA - 12.05.2021 KW - Deterioration KW - Structural systems KW - Time-variant reliability KW - Bayesian updating KW - Inspection KW - Monitoring PY - 2021 DO - https://doi.org/10.1007/978-3-030-73616-3_18 VL - 153 SP - 253 EP - 264 PB - Springer CY - Cham, Switzerland AN - OPUS4-52769 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schubert, T. A1 - Bracklow, F. A1 - Unger, N. A1 - Hering, Marcus A1 - Beckmann, B. T1 - Impact Tests on Reinforced Concrete Slabs – Variation in Results and Residual Load Bearing Capacity due to Hard Impact N2 - Structures such as concrete slabs and concrete barriers of critical infrastructure facilities must be able to withstand impact events or severe accidents. In particular, the structural safety of the confinements of nuclear power plants against a possible aircraft impact is essential to ensure the safety of inhabitants and environment. This article presents research results of hard impact on reinforced concrete slabs, which have been carried out at the Institute of Concrete Structures (IMB) at TUD Dresden University of Technology Technische Universität (TU Dresden). A specially designed drop tower is available for this purpose on the premises of the Otto Mohr Laboratory of TU Dresden. In the framework of a research project many reinforced concrete slabs of dimension 1.5 x 1.5 x 0.2 m³ were tested by hard impact. The investigation on some already carried out impact tests on reinforced concrete slabs intend to show the range of deviation of impact tests. In particular, the possible standard deviations that should be regarded in impact tests are estimated for the applied test setup. Furthermore, the residual structural capacity of an undamaged and damaged reinforced concrete slab has been investigated. T2 - Conference: 27th International Conference on Structural Mechanics in Reactor Technology (SMiRT) CY - Yokohama, Japan DA - 04.03.2024 KW - Reinforced Concrete KW - Drop Tower KW - Hard Impact PY - 2024 SP - 1 EP - 10 PB - IASMiRT CY - Yokohama, Japan AN - OPUS4-61915 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schubert, Thomas A1 - Máca, Petr A1 - Hering, Marcus A1 - Fiedler, Georg A1 - Beckmann, Birgit T1 - Impact Experiments on Reinforced Concrete Specimens - Investigation of Repeatability and Scaling N2 - Nowadays, the impact resistance of concrete structures has become a prominent concern for critical infrastructure operators, particularly amidst escalating geopolitical tensions. Regulators and design engineers know that reinforced concrete structures can only be developed with high efficiency by considering nonlinear structural and highly nonlinear material behavior. Therefore, specific guidelines on impact design provide instructions for design and analysis of structures required to resist impact loading. These instructions are usually based on published results and evaluated data of impact experiments carried out in laboratories. To widen the knowledge and increase the scientific data the Institute of Concrete Structures (IMB) at TUD Dresden University of Technology (TUD) has carried out many impact experiments on reinforced concrete specimens in recent years. A specially designed drop tower is available for this purpose on the premises of the Otto Mohr Laboratory, TUD. In the framework of the past research at TUD some important issues, such as influence of rebar arrangement, structural thickness, scalability of specimen and repeatability, with regard to experimental impact testing were investigated. This article presents the drop tower facility and research results of impact experiments on reinforced concrete slabs. First, the scalability of impact experiments will be discussed in conjunction with already known theoretical scaling parameters provided by researchers in the past, e.g. Rüdiger et al. [1]. Scalability of experimental data is of huge importance since protective structures made of reinforced concrete differ usually in size in comparison to experimental specimens. The second important research focus is on repeatably of impact experiments. Since impact experiments are usually time consuming and expensive, a certain impact scenario is mostly carried out only once. It is intended to show the range of deviation of impact tests on some already carried out experiments on reinforced concrete slabs. A possible standard deviation is estimated for the applied test setup. T2 - 15th International Conference on Shock & Impact Loads on Structures CY - Gothenburg, Sweden DA - 12.06.2025 KW - Reinforced concrete KW - Drop-weight impact KW - Scaling KW - Repeatability KW - Digital image correlation PY - 2025 SP - 80 EP - 91 AN - OPUS4-63627 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Baeßler, Matthias ED - Cunha, Álvaro ED - Caetano, Elsa T1 - On the reassessment of bridge superstructure vibrations for high-speed traffic N2 - The acceleration thresholds of bridge superstructures remain critical for designing and reassessing railway bridges on high-speed lines, with ballasted track systems historically limited to 3.5 m/s2 vertical accelerations due to destabilization risks. As part of the European InBridge4EU project, this study addresses methodological uncertainties in linking vertical bridge vibrations to lateral track creep—a key focus area for modernizing assessment protocols. A comparative analysis of two acceleration postprocessing methods (peak identification vs. fatigue-derived rainflow counting) as part of a recently proposed framework was conducted using an example bridge and train combination. Results demonstrate that rainflow counting yields more conservative creep estimates with the bulk of cumulative vibration-induced creep attributable to accelerations exceeding 3 m/s2. However, discretizing acceleration ranges into 1 m/s2 bins introduced significant errors compared to continuous cycle data, highlighting sensitivity to analysis parameters. These findings underscore the complexity of reconciling laboratory-derived harmonic vibration models with real-world bridge dynamics, where non-uniform acceleration patterns dominate. The research directly informs ongoing efforts to refine standardized criteria for ballasted track stability, particularly through the InBridge4EU project’s systematic re-evaluation of vibration limits and their engineering implications. By quantifying discrepancies between computational approaches, this work advances the development of robust protocols for predicting track degradation under high-speed operational loads. T2 - 11th International Conference on Experimental Vibration Analysis for Civil Enginering Structures (EVACES 2025) CY - Porto, Portugal DA - 02.07.2025 KW - Infrastructure KW - Railway bridges KW - Ballast destabilization KW - Acceleration limit KW - Ballasted track KW - Rainflow counting PY - 2025 SN - 978-3-031-96105-2 DO - https://doi.org/10.1007/978-3-031-96106-9_53 VL - 675 SP - 506 EP - 515 PB - Springer CY - Cham AN - OPUS4-64267 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias A1 - El-Athman, Rukeia T1 - Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods N2 - Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system. T2 - 11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022) CY - Barcelona, Spain DA - 11.07.2022 KW - Research data management KW - Structural health monitoring KW - Artificial intelligence PY - 2022 SN - 978-1-032-35623-5 SN - 978-1-003-32264-1 DO - https://doi.org/10.1201/9781003322641-127 SP - 1061 EP - 1068 PB - CRC Press CY - Boca Raton AN - OPUS4-55493 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias ED - Yokota, H. ED - Frangopol, D. M. T1 - Bayesian system identification of a reinforced concrete beam subject to temperature variations based on static response data N2 - Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data. T2 - Tenth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020) CY - Online meeting DA - 11.04.2021 KW - Structural systems KW - Bayesian system identification KW - Damage identification KW - Environmental effects KW - Structural health monitoring PY - 2021 DO - https://doi.org/10.1201/9780429279119-125 SP - 934 EP - 941 PB - CRC Press AN - OPUS4-52809 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Recknagel, Christoph ED - Cunha, Á. ED - Caetano, E. T1 - Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations N2 - Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data. T2 - International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) CY - Online meeting DA - 30.06.2021 KW - Digital twin KW - Structural health monitoring KW - Material tests KW - Bayesian updating PY - 2021 SN - 2564-3738 SP - 1537 EP - 1544 AN - OPUS4-54126 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Viefhues, Eva A1 - Said, Samir A1 - Herrmann, Ralf A1 - Baeßler, Matthias ED - Papadrakis, M. ED - Fragiadakis, M. ED - Papadimitriou, C. T1 - Vibration-based Structural Health Monitoring of a Reinforced Concrete Beam Subjected to Varying Ambient Conditions Using Bayesian Methods N2 - Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection. T2 - EURODYN 2020, XI International Conference on Structural Dynamics CY - Online meeting DA - 23.11.2020 KW - Structural health monitoring KW - Bayesian methods KW - Environmental effects PY - 2020 SN - 978-618-85072-2-7 SN - 978-618-85072-0-3 SP - 1254 PB - Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens (NTUA) CY - Athens, Greece AN - OPUS4-51590 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -