22 results
Now showing1 - 10 of 22
- 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 Paper7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven identification of cavitational behavior during vibrodriving(Institute for Risk and Reliability, 2026-09); ; ; ; During vibrodriving, pile penetration dynamics can be broadly divided into noncavitational and cavitational regimes. In the cavitational regime, excessive acceleration may cause intermittent pile–soil detachment, thereby affecting energy transfer, soil disturbance, and final bearing capacity. Identifying the prevailing driving regime is essential for adaptive control and for assessing installation efficiency. However, conventional measurements of reaction force and displacement are often impractical in field applications because of sensor cost and integration constraints. This study proposes a data-driven classification approach to distinguish between cavitational and non-cavitational vibrodriving regimes. Laboratory model tests are performed using a scaled vibrodriver prototype with independently controlled eccentric masses. Reaction forces are measured using a piezoelectric load cell mounted at the pile head, tip pressure is measured beneath the pile toe, and vertical displacement is obtained from an encoder. The reference measurements enable cycle-wise classification using two independent methods. Both methods show consistent results and capture the transition zone between the two regimes. Since the reference measurements are often too costly or impractical for real-time field applications, labeled acceleration signals derived from force, pressure, and displacement measurements are used as input for a machine learning model. A supervised learning workflow based on a convolutional neural network is trained for classification. The results show that the network achieves a test accuracy of 99.94% and correctly predicts nearly all cycles. The accelerometerbased classification provides a promising low-cost basis for monitoring and real-time control of vibrodriving systems.Publicationtype: Conference Paper9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , CT-based assessment of 3D-printed clay elements(Springer, 2026-06); ; ; ; Clay-based 3D printing has emerged as a low-carbon alternative to cementitious additive manufacturing in construction. However, pore space, inhomogeneities, shrinkage, and crack formation in clay-sand-water mixtures are still poorly understood, particularly with respect to their influence on the structural integrity of clay-printed elements. This paper investigates the use of X-ray computed tomography (CT) for specimen-scale analysis of clay-sand-water mixtures used in additive manufacturing. One printed specimen and two manually prepared reference specimens in the wet and dried state were scanned with the ENCI CT system. The reconstructed and segmented volumes were used to quantify pore volume, equivalent spherical diameter, sphericity, a derived specific pore surface, and spatial porosity distribution. The printed sample exhibited markedly smaller pores than the manually prepared dried sample, indicating compaction during extrusion and layer deposition, while the dried sample showed larger and more crack-like pores consistent with shrinkage during drying. Local porosity maps and their coefficients of variation were further used to identify heterogeneous zones that may act as mechanically weaker regions. The results demonstrate the feasibility of CT-based assessment for characterizing internal porosity and heterogeneity in 3D-printed clay elements.Publicationtype: Conference Paper13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optimising geometries of drag embedment anchors for floating offshore structures using a multi-objective evolutionary algorithmFloating offshore renewable energy systems rely on secure anchoring, e.g. drag embedment anchors (DEAs). DEA design involves balancing multiple objectives and considering various geometric parameters. Although multi-objective optimisation packages are widely available, DEA optimisation is still frequently handled heuristically. This study introduces a workflow that couples established semi-analytical DEA kinematics in MATLAB with multi-objective evolutionary optimisation (gamultiobj, based on NSGA-II) to identify anchor geometries that minimise anchor volume while maximising bearing capacity and penetration depth. Radar charts and the visualisation of the Pareto front across the design space provide interpretable structures that are not directly apparent from the governing equations alone. Three case studies are presented. The first identifies Pareto-optimal fluke and shank lengths and examines the influence of the boundary on the solutions. The second expands the design space to include fluke thickness, junction length, and fluke–shank angle. Clustering of Pareto solutions reveals three consistent geometric groups: (i) generally large anchors with small junctions, (ii) anchors with larger junctions and variable proportions, and (iii) anchors with broad flukes. A high fluke-shank angle is consistently present across all groups and appears beneficial for all objectives. The third case applies a weighted-sum formulation to produce a single design. Overall, this study provides a workflow for optimisation-driven exploration of DEA design, including a classification of Pareto-optimal geometries that offers a compact way to assess trade-offs from concept to sizing.Publicationtype: Journal Article23 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Digital twins in civil engineeringA plenitude of digital twin reviews have been published in recent years, most of which, however, focus on project-specific digital twin implementations or specific application areas within civil engineering. The architecture, including the composition of internal elements, and schemas of digital twins have not been studied yet in a universally applicable and systematic manner. In this study, a multivocal review of digital twins in civil engineering is conducted, combining a systematic literature review and a gray literature review to capture both peer-reviewed literature and industry practices. To achieve a shared understanding of digital twins in civil engineering, a systematic survey of definitions, schemas, system architectures, and internal elements of digital twins is conducted. Drawing from the results of the review, (i) a definition, (ii) a pattern for a schema, and (iii) a reference architecture for digital twins are proposed, serving as a blueprint for facilitating digital twin implementations in civil engineering.Publicationtype: Journal Article20 10 - 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 Proceedings175 133 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Decision making in structural health monitoring using large language models(DEStech Publications, 2025-09); ; ; ; Structural health monitoring (SHM) is fundamental in decision making for damage identification and prescriptive maintenance of civil infrastructure. Traditional decisionmaking methods often fall short in integrating heterogeneous sensor data, inherent to SHM, and the natural language processing required to interpret, e.g., inspection reports, expert assessments, and historical documentation relevant to prescriptive maintenance. To address these limitations, this study introduces a framework for integrating large language models (LLMs) into SHM workflows, facilitating decision making in damage identification and prescriptive maintenance. The proposed framework links convolutional neural networks (CNNs) with generative LLMs. Unlike approaches based solely on prompt engineering, this study applies task-specific fine-tuning via low-rank adaptation (LoRA) to the Mistral-7B-Instruct-v0.1 model, using CNN-generated output and damage metadata as input. The results demonstrate – apart from the proof of concept – a successful generalization to previously unseen CNN-generated output, enabling context-sensitive damage identification. In conclusion, integrating LLMs into SHM workflows allows synthesizing heterogeneous sensor data and natural language, thus enhancing decision making for damage identification and prescriptive maintenance of civil infrastructure.Publicationtype: Conference Paper22 - 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 , Improved neighbor search algorithm for efficiently locating material points in numerical simulations using the material point method (MPM)(2025-01); This study introduces an improved neighbor search algorithm for efficiently locating material points in numerical simulations using the Material Point Method (MPM). In MPM, determining which elements or nodes transmit and receive information from material points is critical for accuracy and performance, particularly in large-scale dynamic geotechnical problems. Traditional methods rely on mesh structures, where structured meshes simplify this process, but unstructured meshes complicate it, leading to longer computational times[1]. To address these challenges, this research proposes a space-partitioning technique using an auxiliary grid that reduces the number of elements requiring checks during simulations. By employing vectorization and organizing semi-structured meshes into sub-meshes with different spacings, the algorithm significantly improves efficiency [2]. Validation results show that the method enhances computational speed, particularly for large models, without compromising accuracy. This approach offers a promising solution for handling complex, unstructured meshes in MPM simulations and is planned for integration in upcoming releases of the Anura3D software.Publicationtype: Conference Paper28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Ontology-based knowledge representation for wire arc additive manufacturing and composite extrusion modelingKnowledge representation in additive manufacturing (AM) is essential for data management, enabling semantic interoperability and decision-making. Wire arc additive manufacturing (WAAM) and composite extrusion modeling (CEM) are advanced manufacturing methods employed within metal-based additive manufacturing. Although knowledge representation in AM has been widely explored, there is a notable gap in research addressing knowledge representation tailored to WAAM and CEM. Aiming to advance knowledge representation for WAAM and CEM, this paper proposes an ontology-based knowledge representation approach. Two ontologies, the Wire Arc Additive Manufacturing Application Ontology (WAAMAO) and the Composite Extrusion Modeling Application Ontology (CEMAO), are proposed, following a well-known ontology engineering methodology to ensure a rigorous and systematic ontology design process. To validate the proposed approach, a manufacturing information system utilizing both ontologies is presented. The findings highlight the capability of WAAMAO and CEMAO in knowledge representation, enabling efficient data management and supporting semantic interoperability in metal-based AM processes.Publicationtype: Conference Paper41 27