Annika Robens-Radermacher

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.

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.

Traditional construction techniques, such as in-situ casting and pre-cast concrete methods, have well-established testing protocols for assessing compressive strength and modulus of elasticity, including specific procedures for sample preparation and curing. In contrast, 3D concrete printing currently lacks standardized testing protocols, potentially contributing to the inconsistent results reported in previous studies. To address this issue, RILEM TC 304-ADC initiated a comprehensive interlaboratory study on the mechanical properties of 3D printed concrete. This study involves 30 laboratories worldwide, contributing 34 sets of data, with some laboratories testing more than one mix design. The compressive strength and modulus of elasticity were determined under three distinct conditions: Default, where each laboratory printed according to their standard procedure followed by water bath curing; Deviation 1, which involved creating a cold joint by increasing the time interval between printing layers; and Deviation 2, where the standard printing process was used, but the specimens were cured under conditions different from water bath. Some tests were conducted at two different scales based on specimen size—“mortar-scale” and “concrete-scale”—to investigate the size effect on compressive strength. Since the mix design remained identical for both scales, the only variable was the specimen size. This paper reports on the findings from the interlaboratory study, followed by a detailed investigation into the influencing parameters such as extraction location, cold joints, number of interlayers, and curing conditions on the mechanical properties of the printed concrete. As this study includes results from laboratories worldwide, its contribution to the development of relevant standardized testing protocols is critical.

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).

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 bead-on-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.

Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future of monitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.