Walter Kaufmann


Last Name

Kaufmann

First Name

Walter

ORCID

0000-0002-8415-4896

Organisational unit

09469 - Kaufmann, Walter / Kaufmann, Walter

Search Results

Publications 1 - 10 of 192
  • Determination of the effective concrete area in tension relevant for modelling tension stiffening in SLS and ULS design
    Item type: Conference Paper
    Galkovski, Tena; Mata Falcón, Jaime; Kaufmann, Walter (2021)
    fib Symposium Proceedings ~ Concrete Structures: New Trends for Eco-Efficiency and Performance, Proceedings of the fib Symposium 2021
  • Puente Basculante Porta d' Europa en el Puerto de Barcelona
    Item type: Other Conference Item
    Arenas, Juan J.; Kaufmann, Walter (2001)
  • Flächentragwerke: 20-148
    Item type: Educational Material
    Kaufmann, Walter (1998)
  • Guided generation of strut-and-tie models for reinforced concrete structures with parametric graph grammatical evolution
    Item type: Journal Article
    Yu, Karin; Chatzi, Eleni; Kaufmann, Walter; et al. (2026)
    Advanced Engineering Informatics
    Strut-and-tie models are typically a manual design approach that follows a truss analogy for designing reinforced concrete structures with discontinuities. Their advantage of simplicity, flexibility and their sound mechanical basis are negated by the requirement of engineering judgement and time-consuming iterative development. Automation attempts with topology or discrete layout optimisation often struggle to incorporate soft requirements such as practicality or manufacturability. This limits their applicability in real-world use cases. To address this shortcoming, the authors have previously proposed a graph grammar-based approach using rewrite rules to iteratively transform an initial graph into a truss by incorporating domain biases. Yet, the method relies on user guidance and faces scalability issues. This work proposes parametric graph grammatical evolution, an extension of grammatical evolution combining formal grammars with evolutionary algorithms, to guide the generation of strut-and-tie models. A novel selection method suggests diverse solutions based on the optimisation objectives and the graph topology. Validation demonstrates that the method can generate simple, valid solutions with an explainable generation process. For more complex geometries, it benefits from more intricate initial trusses, which can be developed by the user, emphasising its suitability for human-computer interaction.
  • Artificial Intelligence - Finite Element Method - Hybrids for Efficient Nonlinear Analysis of Concrete Structures
    Item type: Conference Paper
    Kraus, Michael Anton; Bischof, Rafael; Kaufmann, Walter; et al. (2022)
    Acta Polytechnica CTU Proceedings ~ International Probabilistic Workshop 2022
    Realistic structural analyses and optimisations using the non-linear finite element method are possible today yet suffer from being very time-consuming, particularly in case of reinforced concrete plates and shells. Hence such investigations are currently dismissed in the vast majority of cases in practice. The "Artificial Intelligence - Finite Element - Hybrids"project addresses the current unsatisfactory situation with an approach that combines non-linear finite element models for reinforced concrete shells with scientific machine learning algorithms to create hybrid AI-FEM models. The AI-based surrogate material model provides the material stiffness as well as the stress tensor for given concrete design parameters and the strain tensor. This paper reports on the current status of the project and findings of the calibration of the AI-based reinforced concrete material model. We successfully calibrated and evaluated k-nearest-neighbour, LGBM and ResNet algorithms and report their predictive capabilities. Finally, some light is shed on the future work of integrating the AI surrogate material models back into the finite element method in the course of the numerical analysis of reinforced concrete structures.
  • NTAB0: Design Priors for AI-Augmented Generative Design of Network Tied-Arch-Bridges
    Item type: Conference Paper
    Kuhn, Sophia V.; Bischof, Rafael; Klonaris, Georgios; et al. (2022)
    Proceedings of 33. Forum Bauinformatik
    Projects in the Architecture, Engineering and Construction (AEC) industry inherit a great complexity due to a tremendous amount of design parameters, multiple objectives, and many involved stakeholders. Especially in the conceptual design stage of bridges, an in-detail analysis of many performance attributes for each design alternative is time-consuming and infeasible under the current approaches. In the industry today, therefore the initial design solution predominantly depends on the expertise of the involved team. In contrast to the status quo, this paper introduces the novel concept of bridge design prior models to predict the layout and structural properties of bridges as the (near-optimal) starting point for Generative Design. The concept of design prior models for bridges is demonstrated on network tied-arch bridges (NTAB). NTAB0 is calibrated upon a curated database consisting of existing real-world NTABs and captures numeric, semantic, and topological relations between bridge properties such as materials, cross-sections or bracing systems. First, a clustering analysis is performed by applying the k-Prototype and DBSCAN algorithms. In the second step, a predictive model is trained using a gradient-boosted decision tree algorithm. A subsequent study evaluates the suitability of the algorithms to serve as sensible design priors. We found that the AI prior model NTAB0 is able to suggest meaningful design parameters, assisting the designing team with an informed initial bridge design for further design space exploration and optimisation. It enables designers to make more informed decisions towards optimised bridge structures at an early design stage. The application of the AI prior model shows great potential to improve future construction projects by providing easy and fast access to the information saved in the existing structures of today.
  • Assessment and Integration of Sustainability and Circularity Metricswithin Generative Bridge Design
    Item type: Conference Paper
    Kuhn, Sophia V.; Hodel, Anna; Bischof, Rafael; et al. (2023)
    The 30th EG-ICE: International Conference on Intelligent Computing in Engineering
    Given the construction sector’s large environmental impact, analysing and optimising sustainability of a structure becomes increasingly important. The growing number of Life-Cycle Assessment (LCA) tools for buildings is however neither directly applicable nor transferable to bridges. Furthermore, circularity is hardly ever measured, let alone enforced in bridge design. We derived and implemented a software tool that enables automated computational evaluation of the environmental impact and circularity of bridges. The tool is applied within an innovative performance-based design space exploration and multi-objective optimisation framework. A Conditional Variational Autoencoder is trained on synthetically generated bridge alternatives to enable designers to make informed decisions towards more sustainable and circular yet reliable bridge structures. The study proves the framework with integrated LCA and circularity measure valuable for the conceptual design phase and simultaneously identifies challenges for its broader adoption within bridge design.
  • Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
    Item type: Working Paper
    Balmer, Vera; Kuhn, Sophia; Bischof, Rafael; et al. (2022)
    arXiv
  • Potentials of Steel Fibres for Mesh Mould Elements
    Item type: Conference Paper
    Pfändler, Patrick; Wangler, Timothy; Mata Falcón, Jaime; et al. (2018)
    RILEM Bookseries
  • Fundamental Studies on the Use of Distributed Fibre Optical Sensing on Concrete and Reinforcing Bars
    Item type: Journal Article
    Galkovski, Tena; Lemcherreq, Yasmin; Mata Falcón, Jaime; et al. (2021)
    Sensors ~ Distributed Optical Fiber Sensors for Concrete Structure Monitoring
    Distributed fibre optical sensing (DFOS) allows for quasi-continuous strain measurement in a broad range of gauge lengths and measurement frequencies. In particular, Rayleigh backscatter-based coherent optical frequency domain reflectometry has recently registered a significant application increase in structural concrete research and monitoring thanks to its numerous merits, such as high resolution and low invasiveness. However, it is not a plug-and-play technique. The quality of the acquired data depends highly on the choice of the fibre optical sensor and the methods of instrumentation and post-processing. Furthermore, its unprecedented resolution and sensitivity allow capturing local effects not well documented so far. This paper analyses the suitability of DFOS based on Rayleigh backscatter for reliably measuring strains and discusses the origin and structural relevance of local variations in the results. A series of experimental investigations are presented, comprising tensile tests on bare reinforcing bars and concrete compression tests. A critical analysis of the results leads to a best practice for applying DFOS to reinforcing bars and concrete, which establishes a basis for reliable, accurate measurements in structural concrete applications with bonded reinforcement.
Publications 1 - 10 of 192