Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Quasi-static and dynamic response of GFRP and BFRP bars under compression

Abstract: The application of fiber-reinforced polymers (FRP) bars in the building industry has grown intensively over the years, due to their numerous advantages such as corrosion resistance. However, current guidelines in design codes disregard the contribution of FRP bars in compression. The need of investigation of compressive mechanical properties of FRP bars is important, while there is a lack of experimental tests under varying dynamic and quasi-static loading rates. The present paper aims to evaluate the compressive strength and failure modes of glass and basalt fiber-reinforced polymer (GFRP and BFRP) bars. A series of quasi-static and dynamic tests were performed on different FRP bar sizes. Dynamic tests were conducted using the drop hammer procedure and different loading rates were achieved through varying the weight of mass and height of fall. Results showed that GFRP bars attained higher compressive strengths than BFRP bars for the same loading conditions. GFRP bars exhibited compressive strength in the range of 300 MPa to 600 MPa while the maximum compressive strength of BFRP bars was 470 MPa. The increase in the bar diameter resulted in an increase in the compressive strength of most FRP bars. However, Some FRP bars showed different variation in their compressive strengths at high loading rates. Finite element (FE) models were also developed to simulate the FRP bars under dynamic tests by considering the orthotopic material nature of the FRP composites. The FE models were verified with the experiments and successfully described qualitatively the trends observed during the full-scale drop-hammer tests.

Abstractive Snippet Generation

Abstract: An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page. Compared to the conventional extractive snippets, which are generated by extracting phrases and sentences verbatim from a web page, abstractive snippets circumvent copyright issues; even more interesting is the fact that they open the door for personalization. Abstractive snippets have been evaluated as equally powerful in terms of user acceptance and expressiveness—but the key question remains: Can abstractive snippets be automatically generated with sufficient quality? This paper introduces a new approach to abstractive snippet generation: We identify the first two large-scale sources for distant supervision, namely anchor contexts and web directories. By mining the entire ClueWeb09 and ClueWeb12 for anchor contexts and by utilizing the DMOZ Open Directory Project, we compile the Webis Abstractive Snippet Corpus 2020, comprising more than 3.5 million triples of the form ⟨query, snippet, document⟩ as training examples, where the snippet is either an anchor context or a web directory description in lieu of a genuine query-biased abstractive snippet of the web document. We propose a bidirectional abstractive snippet generation model and assess the quality of both our corpus and the generated abstractive snippets with standard measures, crowdsourcing, and in comparison to the state of the art. The evaluation shows that our novel data sources along with the proposed model allow for producing usable query-biased abstractive snippets while minimizing text reuse. Code, data, and slides: https://webis.de/publications.html#?q=WWW+2020

Simplified methods for design of base‐isolated structures in the long‐period high‐damping range

Abstract: A recent trend in the design of base-isolated structures is the extension of the natural period and the incorporation of high damping. This paper shows that the existing simplified methods perform less accurately in this field of application, mainly due to inappropriate use of spectral data and insufficiently adjusted equivalent models. The paper proposes new period-dependent concepts to reduce pseudo-acceleration spectra and to transform these values into total accelerations with respect to the viscous damping ratio. The model of equivalent damping is adjusted to reflect several period-dependent effects. The estimation of the accelerations in MDOF systems is based on additional period shifts. All modifications are derived for a simplified linear approach based on eigenforms, and a non-linear approach based on pushover and capacity spectrum analysis. To illustrate observed problems and to demonstrate the capabilities of the proposed concepts, example structures are studied in detail. Furthermore, intensive statistical tests prove the effectiveness of the modifications in a wide parameter range and show considerable improvements over traditional approaches. Copyright © 2005 John Wiley & Sons, Ltd.

Using Bad Learners to find Good Configurations

Abstract: Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, building an accurate performance model can be very expensive (and is often infeasible in practice). The central insight of this paper is that exact performance values (e.g. the response time of a software system) are not required to rank configurations and to identify the optimal one. As shown by our experiments, models that are cheap to learn but inaccurate (with respect to the difference between actual and predicted performance) can still be used rank configurations and hence find the optimal configuration. This novel \emph{rank-based approach} allows us to significantly reduce the cost (in terms of number of measurements of sample configuration) as well as the time required to build models. We evaluate our approach with 21 scenarios based on 9 software systems and demonstrate that our approach is beneficial in 16 scenarios; for the remaining 5 scenarios, an accurate model can be built by using very few samples anyway, without the need for a rank-based approach.