5 results
Now showing1 - 5 of 5
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Workshop numerical methods in geotechnics: AI in geotechnics, deformation based design, numerical optimization(Technische Universität Hamburg, 2026-09-17); ; ; ; The Workshop “Numerical Methods in Geotechnics”, held on 17–18 September 2026 in Hamburg, Germany, brings together researchers and practitioners to discuss recent developments in computational geotechnics. The workshop focuses on three main topics: artificial intelligence in geotechnics, deformation-based design, and numerical optimization. The contributions presented in these proceedings cover data-driven and AI-assisted modelling, surrogate models, advanced finite element methods, uncertainty quantification, soil–structure interaction, and optimization approaches in geotechnical engineering. Together, they highlight current advances in numerical methods and their growing importance for more predictive, efficient, and reliable geotechnical analysis and design.Publicationtype: Conference Proceedings127 69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Adaptive time-delay dynamic mode decomposition for identifying ground vibrations during pile installation(Institute for Risk and Reliability, 2026-09); ; ; ; Vibrodriving is a widely used technique for installing piles. However, the process generates vibrations that propagate through the surrounding soil and may affect nearby structures. A thorough understanding of induced ground-vibration transmission in both the near and far fields is essential for safe installation. Accurate modelling of induced ground vibrations is complicated by temporally varying soil conditions during installation, including stratification, heterogeneity, and nonlinear soil–pile interactions, as well as disturbances introduced by the installation process itself. Therefore, adaptive system-identification methods are required. Dynamic mode decomposition (DMD) offers a powerful data-driven framework for extracting modal information from measured signals. However, the evolving nature of the vibrodriving process requires a sliding-window implementation. The study presents an adaptive implementation of DMD with time-delay embedding (TD-DMD) for identifying and analysing ground vibrations measured by geophones during pile installation. The method uses the spectral properties of the measured data to automatically determine an appropriate truncation rank. Furthermore, the approach exploits the increase in the spectral gap between retained and discarded singular values as the number of delay coordinates grows, enabling effective denoising and adaptive selection of both the truncation rank and the number of delays. The adaptive selection of rank and delay coordinates results in a robust and self-tuning framework for online vibration analysis. Preliminary field measurement results demonstrate the ability of the proposed approach to model soil vibrations. The findings highlight the potential of adaptive TD-DMD as a reliable tool for monitoring ground vibrations during vibrodriving.Publicationtype: Conference Paper6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Workshop smartGeotech Künstliche Intelligenz, Automatisierung, Robotik(Institut für Geotechnik und Baubetrieb, 2025-09-25); ; ; ; Der Workshop smartGeotech 2025 an der Technischen Universität Hamburg zeigte, wie Sprachmodelle wie ChatGPT, Automatisierung und Robotik die geotechnische Praxis transformieren. Diskutiert wurden Chancen und Grenzen für Berechnungen, Gutachten und Bauabläufe, die Einordnung im Gefüge von Normen und „Stand der Technik“, mögliche Haftungs- und Urheberrechtsfragen sowie die Konsequenzen für Ausbildung und Berufsethos. Forschende und Industrie gaben Einblicke in aktuelle Arbeiten, Auswirkungen auf den Ingenieuralltag und bereits umgesetzte Lösungen samt Ausblick auf kommende Entwicklungen.Publicationtype: Conference Proceedings171 133 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modeling and predicting soil vibrations during vibrodriving installation using noisy data and dynamic mode decomposition(Springer, 2025-07); ; ; ; Accurately predicting ground vibrations during vibrodriving remains a significant challenge due to the risks posed to nearby structures. In the present study a data-driven approach to modeling soil vibrations using dynamic mode decomposition (DMD), an equation-free framework, is introduced. However, the performance of DMD is susceptible to noise, which may compromise its accuracy. To address the effects of noise, two DMD modifications are evaluated and compared: (i) augmented DMD (Aug-DMD), which uses time-delayed coordinates, and (ii) forward-backward DMD (FB-DMD), a denoising strategy that combines forward and backward time evolution to reduce systematic eigenvalue bias caused by noise. Synthetic geophone data augmented with white noise is used to simulate noise during the measurement process and to evaluate the effectiveness of the algorithms. In summary, the present study identifies the conditions under which each method predominates and provides recommendations for their application in real-time field scenarios.Publicationtype: Conference Paper11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven modeling of structural dynamics: improving measurement noise robustness with Augmented Dynamic Mode Decomposition(Institute of Structural Analysis and Antiseismic Research National Technical University of Athens, 2025-06); ; ; Accurate modeling of structural dynamics is essential for designing effective vibration control strategies in earthquake engineering. Traditional system identification techniques often struggle to distinguish intrinsic structural behavior from external forces, particularly in the presence of measurement noise. Dynamic Mode Decomposition with Control (DMDc) offers a data-driven approach to extracting system dynamics while accounting for external inputs. However, its performance degrades significantly in noisy environments, leading to inaccurate mode identification and misrepresented damping characteristics. To address this challenge, we introduce Augmented Dynamic Mode Decomposition with Control (AugDMDc), a novel extension that incorporates time-delay coordinates into DMDc to enhance noise robustness. By embedding past measurements into the state-space representation, AugDMDc effectively reduces the impact of sensor noise while preserving the ability to separate a structure’s natural response from external excitations. We validate this approach using a structural dynamic system equipped with an Active Mass Damper (AMD), comparing the performance of DMDc and AugDMDc under various conditions.Publicationtype: Conference Paper30