Journal: ce/papers

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Wiley

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2509-7075

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2022 - 20236
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Publications 1 - 6 of 6
  • Contribution to Lateral Torsional Buckling of Girders with Rotational Restraints
    Item type: Journal Article
    Müller, Andreas; Taras, Andreas (2022)
    ce/papers
    The instability case of lateral torsional buckling (LTB) under bending load My is known to be of decisive importance in the design of beams with open cross-sections. This instability case is expressed by a lateral displacement and simultaneous torsion (or twisting) of the member axis. The restraint of one or both of these deformation components represents an effective and practice oriented method to stabilize beams against LTB. In practice, therefore, use is made of regulations from the current version of EN 1993-1-1:2010, Annex BB, which allow for the consideration of the stabilizing effect of continuous and planar components directly connected to the compression chords of the beams. This paper presents the results of a study that investigated the effects of the innovations introduced in the draft standard for the 2nd generation of EN 1993-1-1 for the LTB verification of beams with I and H cross-sections on the regulations for the calculation of the minimum stiffness of a rotational restraint to avoid LTB. In the first part of the paper, the background of the existing and new regulations is discussed together with previous proposals for adapting the definition of the minimum rotation restraint. In addition, a comparison is made with current specifications for the minimum rotational stiffness contained in the National Annex of DIN EN 1993-1-1. Subsequently, the regulations of the minimum rotational stiffness in prEN1993-1-1, Annex D are assessed.
  • Scientific Machine and Deep Learning Investigations of the Local Buckling Behaviour of Hollow Sections
    Item type: Journal Article
    Müller, Andreas; Taras, Andreas; Kraus, Michael A. (2022)
    ce/papers
    The recent advent of Machine and Deep Learning (ML/DL) increased interest among applicability of data-driven methods to design and verification in structural civil engineering concerned, specifically with members made of steel within this paper. Scientific ML (SciML) follows the idea of combining domain knowledge with the abilities of ML and DL algorithms to make the best out of both worlds. This paper investigates the SciML algorithms Random Forrests, XGBoost and a deep neural network model for the prediction of the cross-section dependent local buckling reduction factor χ for different hot rolled and cold-formed SHS and RHS profiles made of mild and high strength steel. The process of data collection, cleaning, compilation and model training and evaluation is described. It is found, that especially tree-based methods can serve as feature importance and selection methods and have similarly high predictive capabilities such as deep neural networks for the case of of the cross-section dependent local buck-ling reduction factor χ for different hot rolled and cold-formed SHS and RHS profiles made of mild and high strength steel. The data of this paper furthermore serve as the initial data-set for establishing the Github repository “SciML4StructEng_Repository”. This repository which will foster the dissemination of data sets for structural civil engineering to allow the prototyping, development and benchmark testing of scientific machine and deep learning (SciML) algorithms.
  • Deformation Capacity and Ductility of Shear Connectors
    Item type: Conference Paper
    Baertschi, Roland; Garcia, Samuel; Kroyer, Robert; et al. (2023)
    ce/papers
    Deformation capacity and slip range (eventually called “ductility”) of shear connectors are key parameters for the design of shear connectors in composite beams, especially in partial shear connection. Existing design rules require a deformation capacity of at least 6 mm for the use of shear connectors with plastic design methods. This rule is combined with the assumption of rigid-ideal plastic behaviour of the shear connectors. Still, the real characteristics of most of today's usual shear connectors differ considerably from these assumptions, even more for shear connectors used in combination with metal decking. The paper describes the results of a literature research conducted in order to obtain reliable values for the deformation capacity and the slip range of headed stud shear connectors. Based on these results, advanced design methods can be used to realise composite beams with more realistic lower limits for the degree of partial shear connection and thus better economic performance.
  • Predictive modelling and latent space exploration of steel profile overstrength factors using multi-head autoencoder-regressors
    Item type: Journal Article
    Kraus, Michael Anton; Müller, Andreas; Bischof, Rafael; et al. (2023)
    ce/papers
    This paper investigates the suitability and interpretability of a data-driven deep learning algorithm for multi cross sectional overstrength factor prediction. For this purpose, we first compile datasets consisting of experiments from literature on the overstrength factor of circular, rectangular and square hollow sections as well as I- and H-sections. We then propose a novel multi-head encoder architecture consisting of three input heads (one head per section type represented by respective features), a shared embedding layer as well as a subsequent regression tail for predicting the overstrength factor. By construction, this multi-head architecture simultaneously allows for (i) the exploration of the nonlinear embedding of different cross-sectional profiles towards the overstrength factor within the shared layer, and (ii) a forward prediction of the overstrength factor given profile features. Our framework enables for the first time an exploration of cross-section similarity w.r.t. the overstrength factor across multiple sections and hence provides new domain insights in bearing capacities of steel cross-sections, a much wider data exploration, since the encoder-regressor can serve as meta model predictor. We demonstrate the quality of the predictive capabilities of the model and gain new insights of the latent space of different steel sections w.r.t. the overstrength factor. Our proposed method can easily be transferred to other multi-input problems of Scientific Machine Learning.
  • Deep Neural Network Direct Stiffness Method: Fundamental Steps into Beam Design
    Item type: Journal Article
    Mueller, Andreas; Taras, Andreas (2023)
    ce/papers
    This paper presents a novel approach for performing beam element analysis that considers the nonlinear deformation behavior of various RHS and SHS sections made of mild to high-strength steel: the Deep Neural Network Direct Stiffness method (DNN-DSM), which uses Deep Neural Networks (DNN), a subset of machine learning algorithms and more general artificial intelligence approaches, to predict the nonlinear stiffness matrix terms in a beam element formulation for implementation in the direct stiffness matrix (DSM). These predictions are made using trained DNN models drawn from an extensive pool of geometric-material nonlinear simulations with additional imperfections (GMNIA) using shell-based models. Initial implementations of this method are able to predict the nonlinear load-displacement and moment-rotation behavior of various cross sections with high accuracy. This combines the precision of shell analysis with the computational efficiency of beam element analysis. Previous publications have already demonstrated the suitability and advantages of this method, albeit on a small scale for local buckling prediction. This work goes beyond previous studies and focuses on finite beam element design. This includes a review of the modeling approaches of DNN-DSM using experimental and numerical results from literature.
  • A full-scale case study of vibration-based structural health monitoring of bridges: prospects and open challenges
    Item type: Journal Article
    Reuland, Yves; Garcia-Ramonda Estevez, Larisa; Martakis, Panagiotis; et al. (2023)
    ce/papers
    The implementation of Structural Health Monitoring (SHM) offers the prospect for sustainable and safe service-life extension of existing bridges, a large portion of which is approaching the end of their nominal life. Many SHM frameworks for civil infrastructure address timely damage detection and identification. However, the scarcity of case studies on real damaged bridges hinders the generalized application of SHM in practice. In this contribution, monitoring data from a four-day campaign on the Ponte-Moesa bridge, a three-span concrete box-girder bridge, is presented as a benchmark for data-driven damage diagnosis schemes. The monitoring data, covering accelerations from ambient and forced vibrations, contains the reference state after concluding the service life along with several gradually increasing damage states, including drilling holes and cutting reinforcement rebars and prestressed cables. The potential of damage-sensitive features to identify damage is presented and the uncertainties, resulting from the environmental and operational conditions and sensor malfunctioning, pertaining to robust damage detection are discussed. Drawing from real bridge monitoring data, a range of prospects and open challenges of vibration-based SHM for bridges are reviewed.
Publications 1 - 6 of 6