TY - CONF A1 - Robens-Radermacher, Annika T1 - Efficient model identification using a PGD forward model - Influence of surrogate accuracy and converergence approach N2 - There is a rising attention of using numerical models for effcient structural monitoring and ensuring the structure's safety. Setting up virtual models as twin for real structures requires a model identification process calculating the unknown model parameters, which mostly are only indirectly measurable. This is a computationally very costly inverse optimization process, which often makes it unfeasible for real applications. Effcient surrogate models such as reduced order models can be used, to overcome this limitation. But the influence of the model accuracy on the identification process has then to be considered. The aim is to automatically control the influence of the model's accuracy on the identification. Here, a variational Bayesian inference approach[3] is coupled with a reduced forward model using the Proper Generalized Decomposition (PGD) method. The influence of the model accuracy on the inference result is studied and measured. Therefore, besides the commonly used Bayes factor the Kullback-Leibler divergences between the predicted posterior pdfs are proposed. In an adaptive inference procedure, the surrogate's accuracy is iteratively increased, and the convergence of the posterior pdf is analysed. The proposed adaptive identification process is applied to the identification of spatially distributed damage modeled by a random eld for a simple example with synthetic data as well as a small, reinforced bridge with real measurement data. It is shown that the proposed criteria can mirror the influence of the model accuracy and can be used to automatically select a suffciently accurate surrogate model. T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Model order reduction KW - Model identification KW - Bayes factor KW - PGD KW - Kullback-Leibner divergence PY - 2022 AN - OPUS4-55112 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika T1 - Stiffness identification by efficient model calibration of random field variables for the Young's modulus N2 - The main challenge in using numerical models as digital twins in real applications for prognosis purposes, such as reliability analysis, is the calibration and validation of the models based on uncertain measurement data. Uncertainties are not limited to the measurement data, but the numerical model itself will not be perfect due to the modelling assumptions. In this contribution, a probabilistic inference method for model calibration, based on the Bayes’ Theorem, is used to face that issue. Such inference approaches include uncertainties on the data as well as on the model parameters, allowing to compute an a posteriori distribution for the model parameters as well as a noise term reflecting the measured data. However, such probabilistic inference methods require a lot of evaluations of the numerical forward model for different model parameters. An improvement of the efficiency is obtained by replacing the forward model with a reduced model. Model reduction, e.g. the proper generalized decomposition (PGD) method, is a popular concept to decrease the computational effort, where each evaluation of the reduced forward model is a pure less costly function evaluation. The heterogeneous spatial distribution of material parameters in the forward model is described by a lognormal random field. This allows identifying a variable stiffness over the spatial directions by identifying the random field variables with given measurement data. These changes can e.g. be caused by damage. The lognormal field is approximated as series expansion for the PGD problem. The derived efficient model identification procedure is shown using a real reinforced prestress demonstrator bridge and stereophotogrammetry measurement data. A digital twin for that demonstrator bridge is build up using a set of measurement data and verified by testing additional measurement data. PGD model error against the FEM model is discussed based on an importance sampling analysis computing the Bayes Factor. T2 - COMPLAS 2021 CY - Berlin, Germany DA - 08.09.2021 KW - Digital twin KW - Structure monitoring KW - Model updating KW - Proper generalized decomposition KW - Bayesian inferences PY - 2021 AN - OPUS4-53285 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika T1 - Advantages and challenges of data stores for interlaboratory studies – an example from mechanical test data of printed concrete structures N2 - Interlaboratory studies are common tools for collecting comparable data to implement standards for new materials or testing technologies. In the case of construction materials, these studies form the basis for recommendations and design codes. Depending on the study, the amount of data collected can be enormous, making manual handling and evaluation difficult. On the other hand, the importance of the FAIR (findable, accessible, interoperable, and reusable) principles for scientific data management, published by Wilkinson et al. in 2016, is constantly growing and changing the view on data usage. The benefits of using data management tools such as data stores/repositories or electronic laboratory notebooks are many. Data is stored in a structured and accessible way (at least within a group) and data loss due to staff turnover is reduced. Tools usually support data publishing and analysis interfaces. In this way, data can be reused years later to generate new knowledge with future insights. On the other hand, there are many challenges in setting up a data repository, such as selecting suitable software tools, defining the data structure, enabling data access, and understanding by others and ensuring maintenance, among others. This talk discusses the advantages and challenges of setting up and applying a data repository using the interlaboratory study on the mechanical properties of printed concrete structures carried out in RILEM TC 304-ADC as example. First, the definition of a suitable data structure including all information is discussed. The tool-dependent upload process is then described. Here, the data management system openBIS (open source software developed by ETH Zurich) is used. Since in most cases an open compute platform allowing access from different organisations is not possible or available due to data protection and maintenance issues, tool-independent export options are discussed and compared. Finally, the different query and analysis possibilities are demonstrated. T2 - RILEM spring convention & conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milan, Italy DA - 07.04.2024 KW - Data stores KW - Data structuring KW - Data evaluation KW - Automatization KW - Interlaboratory PY - 2024 UR - https://www.rilem.net/agenda/rilem-spring-convention-conference-on-advanced-construction-materials-and-processes-for-a-carbon-neutral-society-1530 AN - OPUS4-59906 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika T1 - Model identification coupling Bayesian Inference with PGD reduced models with random field material parameters N2 - The main challenge using numerical models as digital twins in real applications is the calibration and validation of the model based on uncertain measurement data. Therefore, model updating approaches which are inverse optimization processes are applied. This requires a huge number of computations of the same numerical model with slightly different model parameters. For that reason, model updating becomes computationally very expensive for real applications. Model reduction, e.g. the proper generalized decomposition method, is a popular concept to decrease the computational effort of complex numerical simulations. Therefore, a reduced model of the structure of interest is derived and will be used as surrogate model in a Variational Bayesian procedure to create a very efficient digital twin of the structure. An efficient model updating approach by means of a PGD reduced model with random field material stiffness parameters is shown. The random field allows, to calibrate the model considering parameter changes over the spatial direction. These changes can be caused by local damages as well as by production. As an exemplary application a demonstrator bridge is used. Digital twins can reduce the costs for maintenance and inspections especially for the costly civil infrastructure with high requirements at their performance over the whole lifetime. Currently, the current state of the structure is determined by regular manual and visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. In this case, model-based approaches where a digital twin is set up can improve the process. Based on this digital twin, a prognosis of the future performance of the structure, e.g. the failure probability, can be computed. The influences of the reduction degree, the mesh discretization as well as the correlation length in the PGD Bayesian approach are studied by means of the digital twin of a simple pre-stressed concrete two field bridge. T2 - Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) 2020@21 CY - Online meeting DA - 15.03.2021 KW - Digital twin supporting KW - Proper generalized decomposition PY - 2021 AN - OPUS4-52317 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Unger, Jörg F. A1 - Robens-Radermacher, Annika A1 - Tamsen, Erik T1 - Data provenance - from experimental data to trustworthy simulation models and standards N2 - FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general. When implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods and tools is outlined. We begin with a motivation on potential use cases for the application of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models. In a second part, different options for structuring the data (including metadata schema) are discussed. The first one is the openBIS system, which is an open-source lab notebook and PostgreSQL based data management system. A second option is a semantic representation using RDF based on ontologies for the domain of interest. In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database. Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. A metadata schema for a rheological measurement setup is derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behavior, the automatic workflow to use the stored data for model parameter estimation is demonstrated. KW - FAIR data KW - Semantic web KW - Scientific workflows KW - Metadata KW - Reproducible data processing PY - 2023 DO - https://doi.org/10.1016/j.matpr.2023.08.081 SP - 1 EP - 10 PB - Elsevier AN - OPUS4-58215 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Andrés Arcones, Daniel A1 - Diercks, Philipp A1 - Robens-Radermacher, Annika A1 - Rosenbusch, Sjard Mathis A1 - Tamsen, Erik A1 - Tyagi, Divyansh A1 - Unger, Jörg F. T1 - FenicsXConcrete N2 - FenicsXConcrete is a Python package for the simulation of mechanical problems. The general PDE solving software FEniCSx is extended with classes describing experimental setups, mechanical problems, thermo-mechanical problems, additive manufacturing and sensors. KW - FEM KW - Fenics KW - Concrete modelling PY - 2023 UR - https://github.com/BAMresearch/FenicsXConcrete DO - https://doi.org/10.5281/zenodo.7780757 PB - Zenodo CY - Geneva AN - OPUS4-59121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Robens-Radermacher, Annika A1 - Tamsen, Erik T1 - Data provenance - from experimental data to trustworthy simulation models and standards N2 - FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general, and when implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods, tools and procedures is outlined. We begin with a motivation on potential use cases for the applications of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models. In a second part, different options for structuring the data are discussed. On the one hand, this includes a discussion on how to define actual data structures and in particular metadata schema, and on the other hand, two different systems for storing the data are discussed. The first one is the openBIS system, which is an open-source Lab notebook and PostgreSQL based data management system. A second option are a semantic representations using RDF based ontologies for the domain of interest. In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database. Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. Metadata schemata for a rheological measurement setup are derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behaviour, the automatic workflow to use the stored data for model parameter estimation is demonstrated. T2 - 4th International Congress on Materials & Structural Stability CY - Rabat, Morocco DA - 08.03.2023 KW - Data provenance KW - Experimental data to trustworthy KW - Simulation models and Standards PY - 2023 UR - https://www.rsc-cmss23.asmatec.org/RILEM%20Committees%20Program.php SP - 1 EP - 9 CY - Rabat, Morocco AN - OPUS4-57349 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Veroy, K. A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction N2 - 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 bysolving 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 KW - Multiscale methods KW - Variational multiscale method KW - Localized model order reduction KW - Proper orthogonal PY - 2024 UR - https://arxiv.org/abs/2201.10374 DO - https://doi.org/10.48550/arXiv.2201.10374 PB - Arxiv; Cornell Tech CY - New York, NY AN - OPUS4-59803 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bos, Freek A1 - Menna, Constantino A1 - Robens-Radermacher, Annika A1 - Wolfs, Rob A1 - Roussel, Nicolas A1 - Lombois-Burger, Hélène A1 - Baz, Bilal A1 - Weger, Daniel A1 - Nematollahi, Behzad A1 - Santhanam, Manu A1 - Zhang, Yamei A1 - Bhattacherjee, Shantanu A1 - Jia, Zijian A1 - Chen, Yu A1 - Mechtcherine, Viktor T1 - Mechanical properties of 3D printed concrete: a RILEM TC 304-ADC interlaboratory study — approach and main results N2 - To show compliance to structural engineering codes and implement quality control measures, it is critical to obtain reliable mechanical properties of the materials in question. For conventional cast and precast concrete, the experimental procedures and relationships between mechanical properties, the material composition, and the production methods are globally known, but for 3D concrete printing (3DCP), these relations have not yet been established. Previous studies have shown little consistency in results, and the underlying experimental methods have not been established broadly. There is an urgent need to address these issues as the application of 3DCP in practice projects is growing rapidly. Therefore, RILEM TC 304-ADC: Assessment of Additively Manufactured Concrete Materials and Structures has set up a large interlaboratory study into the mechanical properties of 3D printed concrete. This paper presents key elements of the experimental approach detailed in the Study Plan and the supporting considerations. Furthermore, it reports on the response, consisting of 34 contributions from 30 laboratories, detailing global coverage, properties of the applied mixture designs and characteristics of the printing facilities that have been used. Subsequently, some fundamental results from compression, flexural, and E-modulus testing are presented and—considering cast specimens as a reference—discussed. On average, a reduction in strength was found in compression and E-modulus (all tested orientations). For flexure, on the other hand, an increase was found in two testing orientations, while a decrease was observed in the third orientation. Importantly, even though the applied experimental methods were found to be reasonably appropriate to obtain the required data, the differences found between individual contributions are significant and sometimes non-consistent, suggesting that testing on specific material-facility combinations is necessary to reliably determine the mechanical properties of objects produced from them. Furthermore, a theoretical framework needs to be developed to further explain the variations that were observed. Extensive analyses of all acquired data are out of the scope of this contribution, but presented in two associated papers, whereas a third presents the data management approach used to process the approximately 5,000 test results. KW - 3D printed concrete KW - 3DCP KW - Mechanical properties KW - Interlaboratory study PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634631 DO - https://doi.org/10.1617/s11527-025-02686-x VL - 58 IS - 5 SP - 1 EP - 29 PB - Springer Nature AN - OPUS4-63463 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Strobl, Dominic A1 - Unger, Jörg F. A1 - Ghnatios, C. A1 - Klawoon, Alexander A1 - Pittner, Andreas A1 - Rethmeier, Michael A1 - Robens-Radermacher, Annika T1 - Efficient bead-on-plate weld model for parameter estimation towards effective wire arc additive manufacturing simulation N2 - Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a beadon-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data. KW - Proper generalized decomposition KW - Model order reduction KW - Hardly separable problem KW - Additive manufacturing KW - Model calibration KW - Wire arc additive manufacturing PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596502 DO - https://doi.org/10.1007/s40194-024-01700-0 SN - 0043-2288 SP - 1 EP - 18 PB - Springer AN - OPUS4-59650 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika T1 - Efficient cooling time optimization in Wire Arc Additive Manufacturing using a multi-layer reduced order model N2 - Additive manufacturing (AM) has transformed the industry by enabling the production of complex geometries and parts with customized properties. Among various AM techniques, wire arc additive manufacturing (WAAM) stands out due to its high deposition rate and low equipment cost. However, WAAM’s complex thermal history poses challenges for real-time simulation, essential for online process control and optimization. Consequently, experimental optimization remains the state-of-the-art approach. A critical parameter to optimize is the cooling phase duration, which prevents structural overheating, controls the molten pool size, and influences the mechanical properties of the final product. For efficient cooling time optimization, a fast-to-evaluate model of the temperature field during multi-layer deposition is necessary. This study proposes a reduced order model (ROM) using the proper generalized decomposition (PGD) method as a powerful tool to minimize computational effort. Given the moving heat source in WAAM processes, a mapping approach is employed to achieve a fully separated representation of the temperature field. Building on the authors’ previous one-layer approach, this contribution extends the model to multiple layers through enhanced mapping and compression techniques. The compression reduces the total number of PGD modes as the number of layers increases. The extended mapping allows computations with a fixed mesh over the simulation time, in contrast to standard methods such as the element birth technique. For cooling time optimization, the cooling duration of each layer is incorporated as PGD variables, enabling time-efficient computation of the temperature field for varying cooling times. The developed ROM is applied to optimize the cooling time of a multiple layer example. Therefore a 5-10 layer wall structure is investigated using the austenitic stainless steel 1.4404 (AISI 316 L). The resulting cooling times and the efficiency of the approach are discussed. T2 - 12th European solid mechanics conference (ESMC) CY - Lyon, France DA - 07.07.2025 KW - Model order reduction KW - Proper generalized decomposition KW - Welding KW - Additive manufacturing KW - Optimzation PY - 2025 UR - https://esmc2025.sciencesconf.org/ AN - OPUS4-63855 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Robens-Radermacher, Annika A1 - Kujath, Cezary A1 - Bos, Freek A1 - Mechtcherine, Viktor A1 - Unger, Jörg F. T1 - Mechanical properties of 3D printed concrete: a RILEM TC 304-ADC interlaboratory study-Design and implementation of a database system for querying, sharing, and analyzing experimental data N2 - Interlaboratory studies are essential for implementing standardized test methods for new innovative materials or technologies such as 3D concrete printing, certifying reference materials, and validating methods. They provide the basis for recommendations and design standards. Typically, the collected data are used only for one study and are published in paper form, without open access to the raw data files. However, preserving the collected data and analysis procedures in a shareable and reusable way leads to advantages for further usage. New data with the same structure can be added or the data can be analyzed by a new analysis procedure generating new knowledge. Additionally, resources are saved by avoiding the repetition of the same measurements. In line with the recent digitalization trend in material science and engineering, a database for the interlaboratory study on mechanical properties of 3D printed concrete conducted in RILEM Technical Committee 304-ADC is established. The challenges and experiences from defining the data structure, uploading the data, and using the database for evaluations are discussed. The openBIS software is used to create and fill the database, while an export method to an SQLite format is developed and discussed to generate a shareable and reusable database. With the exported database, querying the data is possible independently of a particular data management system. Additionally, examples of data processing with reusable workflows automating the analysis procedure are demonstrated. KW - Data management KW - Interlaboratory study KW - 3D concrete printing KW - Database KW - Reusability PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634627 DO - https://doi.org/10.1617/s11527-025-02650-9 SN - 1871-6873 VL - 58 IS - 184 SP - 1 EP - 37 PB - Springer Nature AN - OPUS4-63462 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wolfs, Rob A1 - Versteegen, Jelle A1 - Santhanam, Manu A1 - Bhattacherjee, Shantanu A1 - Bos, Freek A1 - Robens-Radermacher, Annika A1 - Muthukrishnan, Shravan A1 - Menna, Costantino A1 - Ozturk, Onur A1 - Ozyurt, Nilufer A1 - Roupec, Josef A1 - Richter, Christiane A1 - Jungwirth, Jörg A1 - Miranda, Luiza A1 - Ammann, Rebecca A1 - Caron, Jean-François A1 - de Bono, Victor A1 - Monte, Renate A1 - Navarrete, Iván A1 - Eugenin, Claudia A1 - Lombois‑Burger, Hélène A1 - Baz, Bilal A1 - Sinka, Maris A1 - Sapata, Alise A1 - Harbouz, Ilhame A1 - Zhang, Yamei A1 - Jia, Zijian A1 - Kruger, Jacques A1 - Mostert, Jean‑Pierre A1 - Šter, Katarina A1 - Šajna, Aljoša A1 - Kaci, Abdelhak A1 - Rahal, Said A1 - Snguanyat, Chalermwut A1 - Arunothayan, Arun A1 - Zhao, Zengfeng A1 - Mai, Inka A1 - Rasehorn, Inken Jette A1 - Böhler, David A1 - Freund, Niklas A1 - Lowke, Dirk A1 - Neef, Tobias A1 - Taubert, Markus A1 - Auer, Daniel A1 - Hechtl, C. Maximilian A1 - Dahlenburg, Maximilian A1 - Esposito, Laura A1 - Buswell, Richard A1 - Kolawole, John A1 - Isa, Muhammad Nura A1 - Liu, Xingzi A1 - Wang, Zhendi A1 - Subramaniam, Kolluru A1 - Mechtcherine, Viktor T1 - Mechanical properties of 3D printed concrete: a RILEM TC 304-ADC interlaboratory study — flexural and tensile strength N2 - This paper discusses the flexural and tensile strength properties of 3D printed concrete, based on the results of a RILEM TC 304-ADC interlaboratory study on mechanical properties. These properties are determined using different testing techniques, including 3- and 4-point flexural tests, splitting tests, and uniaxial tension tests, on specimens extracted from large 3D printed elements in accordance with a prescribed study plan. The relationship between compressive and flexural or tensile strengths, cast or printed samples, different types of tests, and different loading orientations, are analysed to understand the influence of 3D printing. As expected, the strength can reduce significantly when the main tensile stress is acting perpendicular to the interface between layers. The role of deviations from the standard study procedure, in terms of the time interval between the placing of subsequent layers, or the adoption of a different curing strategy, are also assessed. While the increased time interval significantly impacts the strength in the critical direction, the use of variable curing conditions does not seem to have a clear-cut effect on the strength ratios of the printed to cast specimens. Additionally, the paper looks at the variability in the results for the printed specimens, in order to emphasize the need for multiple replicates for obtaining a proper result. An extensive insight into the aspects affecting the variability is presented in the paper. Finally, with the limited dataset available for specimens tested at a larger scale, it is difficult to arrive at a clear understanding of the role of specimen size (i.e., greater number of layers). KW - 3D concrete printing KW - Digital fabrication KW - Flexural strength KW - Tensile strength KW - Interlayer bond strength PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634655 DO - https://doi.org/10.1617/s11527-025-02687-w SN - 1871-6873 VL - 58 IS - 5 SP - 1 EP - 33 PB - Springer Nature AN - OPUS4-63465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -