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The RILEM TC 304-ADC has set up a large interlaboratory study on the mechanical properties of 3D printed concrete (ILS-mech). The study was prepared in 2022 by a preparation group leading to a Study Plan which the TC approved on 29 November 2022 (https://doi.org/10.14459/2023mp1705940). The ILS-mech was performed in 2023. The data was collected using a pre-prepared spreadsheet template. For data management, a database was derived and set-up in openBIS. The underlying Postgres database of openBIS was exported to the here-published SQLite database for sharing without maintaining a server. The structure of the database is described in (doi). The results are discussed in three associated papers focusing on the overall outcomes and evaluation of the procedures (doi), the compressive test results (doi), and the tensile test results (doi).
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.
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).
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.
Extrusion-based 3D Concrete Printing (3DCP) offers transformative potential for the construction industry, yet its widespread adoption is hindered by mechanical anisotropy and the lack of tools to accurately predict material performance in design. This research addresses this critical gap by developing and validating a novel Physics-Guided Neural Network (PGNN) framework capable of producing reliable and interpretable anisotropic strength predictions. The PGNN combines a physics-based branch, which models compressive strength as a function of age and water-to-binder ratio (W/B) via a modified Abram’s Law, with a shallow neural network branch that captures residual nonlinear effects from loading orientation and mix design. Training and validation were performed on the largest and most comprehensive international dataset available, generated through the RILEM TC-304 ADC interlaboratory study. The PGNN demonstrated high predictive accuracy on unseen RILEM test datasets (R
2
= 0.86–0.89). In addition, the model was applied to six external datasets in a blind test which confirmed reasonable generalisation for typical printing mortars within the compositional range of the training dataset, but was less conclusive for atypical mix designs such as ultra-high-performance and steel-fibre-reinforced composites. The framework yields a transparent formulation, making the relationship between variables and predicted strength directly understandable and allows the application of response surface analyses to gain insight into the influence of mix design variables such as aggregate size, aggregate-to-binder ratio, and W/B on anisotropy. The presented work provides a positive outlook for the transformative potential of physics-guided machine learning in 3D concrete printing to underpin reliable deployment of 3DCP technologies.
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.
