Link zur deutschen Version: Auswirkungen von Anforderungen bei Betondecken
Almost a year ago, a blog post was published explaining how digital fabrication could foster the application of structurally efficient concrete floor slabs to reduce the construction industry’s negative ecological impact. Since then, we have carried out a study on floor slab requirements in the same research project and are happy to present some selected results in this blog post. The study was presented at the fib PhD Symposium 2024 in Budapest (see the blog post on that conference here) and awarded a special mention. More details can be found in the conference paper titled “Correlations of requirements and performance metrics for concrete floor slabs”, authored by Rebecca Ammann (the author of this blog post), Dr. Karel Thoma, Prof. Dr. Jaime Mata-Falcón and Prof. Walter Kaufmann.
When studying literature on structurally efficient concrete floor slabs, it is often questionable if the potential sustainability enhancements found in academic works are achievable in real-life applications. This is not only because the regular floor layouts considered in many research works are not fully representative of real-life projects, but also because requirements that may govern the design in real life, such as fire safety or sound insulation, are often not considered in academic works. However, such requirements can be decisive for the development and study of new (digitally fabricated) structural concepts (which is a goal of this research project) and limit the efficiency of conventional concrete floor slabs. Thus it is crucial to study the impact of these requirements for concrete floor slabs systematically.
As there are numerous requirements and because the implications of a requirement are highly dependent on the chosen case study, we developed a data generation pipeline for this purpose. With this pipeline, the impact of different requirements can be assessed quickly, automatically and objectively for a wide range of case studies. A schematic visualisation of the data generation pipeline, implemented in Python with an interface to the software RFEM 6, is given in Figure 1: The input data consists of the geometry of the floor slab, the loads, the material properties and the requirements to be considered. For each set of input data, a finite element (FE) analysis is carried out, and a reinforcement design is found iteratively based on the FE results. Once a valid design fulfilling all ultimate limit state (ULS) and constructability verifications is found, the respective performances are calculated, and the requirements are checked.
We applied this data generation pipeline to a simple case study with a square floor layout and two structural systems (solid slabs and ribbed slabs), as shown in Figure 2.
To limit the scope of this blog post, only the results regarding the serviceability requirement that limits the maximum deflection w are presented below. Further results can be found in the conference paper.
As the geometry of the concrete floor slab is an input parameter in the data generation pipeline and the optimal geometry is not known beforehand, numerous configurations were analysed. In Figure 3, the performances (relative cost and Global Warming Potential GWP) of all valid configurations are shown for a span L of (a) 5 m and (b) 8 m. The minimum cost and GWP for different spans and requirements regarding the deflections are shown in Figure 3 (c) and (d), respectively.
For flat slabs (shown in blue), limiting the maximum deflections w leads to a considerable increase in GWP and – to a lesser extent – costs, especially for larger spans (comparison of light blue line to dark blue line for given span). In contrast, for ribbed slabs (shown in pink), the increase in cost due to limiting the deflections (comparison of light pink line to dark pink line for given span) is more significant than the respective increase in GWP.
While the presented absolute values are subject to the limitations of the simple case study, we could show that with the data generation pipeline, it is possible to quantify the significant effects of requirements such as serviceability on costs and GWP. In a follow-up study, this data generation will be applied to real-life projects provided by an industry partner, taking into account more requirements and performances. As requirements are generally specified at a very early design stage without assessing their implications, we hope to raise awareness and foster the discussion on the huge impact these requirements can have with our work.
The generality of the data generation pipeline allows us to apply it to other problems, such as the comparison of different structural floor systems or assessing the applicability of a specific structural floor system to various projects – the possibilities are countless, and we look forward to updating you about our progress in due course.
Rebecca Ammann