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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: 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