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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 , Comparative analysis of explainable artificial intelligence methods in sensor fault diagnosis for vibration-based structural health monitoringReliable structural health monitoring (SHM) depends on accurate analysis of sensor data as well as on fault-free operation of sensors collecting the data, which is usually ensured via sensor fault diagnosis (FD). Artificial intelligence (AI) models have shown promising po-tential for sensor FD, mainly exploiting the pattern-learning capabilities in the sensor data. However, the logic behind AI predictions is often opaque, limiting practitioner trust and adop-tion in autonomous SHM systems. To counteract the aforementioned limitation, explainable artificial intelligence (XAI) has emerged, aiming to improve transparency of AI models by in-dicating which aspects of input sensor data influence AI-derived predictions. Nevertheless, XAI applications in SHM remain rare, and most studies focus on the feasibility of applying XAI for SHM rather than practical aspects, such as the reliability and robustness of XAI explanations. This study investigates explanation robustness in XAI-based sensor FD for vibration-based SHM through a comparative analysis of different XAI methods. In particular, AI models are trained on sensor data, and synthetic faults are injected in the sensor data, simulating realistic sensor faults. Explanation robustness is analyzed using attribution consistency (Spearman rank correlation coefficient), and attribution stability (coefficient of variation) across multiple fault scenarios, comparing gradient-based and perturbation-based XAI methods. Validation tests are presented using acceleration response data, collected from a pedestrian bridge. The vali-dation results show that gradient-based methods demonstrate higher explanation robustness than perturbation-based methods when explaining vibration-based sensor fault patterns. Fu-ture work will extend the comparative analysis to distinguish between anomalies caused by sensor faults and anomalies arising from changes in structural dynamic behavior.Publicationtype: Conference Paper11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Quantum technologies in structural health monitoringStructural health monitoring (SHM) is vital for ensuring structural safety and reliability of civil infrastructure, and it requires analysis of large datasets and complex structural dynamics that strain classical computing. Quantum computing and quantum-inspired computing promise new ways to address computational problems that are intractable on classical computers. As datasets and model complexity grow, SHM faces computational bottlenecks in high-dimensional sensor data processing and complex optimization tasks that classical computing struggles to handle, and the potential of quantum and quantum-inspired approaches for SHM remains largely unexplored. To address this gap, the paper outlines core principles of quantum and quantum-inspired computing and conducts a multivocal literature review to evaluate the research landscape centered around quantum and quantum-inspired computing. Furthermore, potential application areas and use cases in SHM are identified. As a result, the review shows that adoption of quantum and quantum-inspired computing to conducting SHM tasks remains in an early stage, but promising opportunities are identified, including optimization, simulation, machine learning, and sensing. As quantum and quantum-inspired computing continue to mature, the findings are expected to provide a foundation for guiding future research endeavors and practical deployments of quantum and quantum-inspired computing to advance SHM tasks.Publicationtype: Conference Paper6 - 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 , Prescriptive maintenance of sewer systems based on large language models(2026-07); ; ; Sewer systems constitute urban infrastructure critical for public health, environmental protection, and flood prevention. Decision-making in sewer systems maintenance often fails to effectively and efficiently interpret heterogeneous and multimodal inspection data into actionable maintenance strategies. To improve decision-making in sewer systems maintenance, this paper introduces a prescriptive maintenance (RxM) framework, in which a pre-trained large language model (LLM) is adapted to the domain of sewer systems using adapter-based fine-tuning. As will be shown in this paper, the LLM is capable of interpreting heterogeneous, multimodal inspection data and recommending stepwise repair procedures of sewer systems, following the paradigm of RxM.Publicationtype: Conference Paper8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A decentralized digital twinning approach for assessing civil engineering structures(2026-07); ; In wireless structural health monitoring (SHM), the focus is usually placed on reducing the power-consuming data transmission for preserving the limited resources of wireless sensor nodes. In this direction, embedded computing has been leveraged for distributing SHM data analysis tasks to the sensor nodes instead of transmitting raw data to centralized servers. This paper reports on decentralizing SHM tasks using reduced-order numerical models of structures, embedded into wireless sensor nodes as decentralized digital twins that use sensor data to assess the structural condition. The applicability of the proposed approach is demonstrated via laboratory validation tests.Publicationtype: Conference Paper7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The information backbone of robotized construction: Principles, procedures, and software architecture(International Association for Automation and Robotics in Construction (IAARC), 2026-06-22); ; ; ; Advances in robotic systems, sensing technologies, and artificial intelligence offer substantial potential to automate construction processes. However, current construction robotics applications are limited to specific tasks in isolated systems, lacking the integration required for coordinated, large-scale robotized construction that is applicable in a wide spectrum of trades. A key challenge is the absence of digital frameworks integrating product information, construction processes, robotic capabilities, and execution feedback across planning, simulation, and on-site operation. This paper presents the concept, principles, and software architecture of the information backbone of robotized construction (IBRC), a unifying framework for planning, simulating, and executing construction projects using coordinated, heterogeneous robotic systems. The IBRC comprises two tightly coupled environments: an offline planning environment and an online execution environment. The planning environment supports successive refinement of product and process information, starting from a building information model (BIM), via a fabrication information model (FIM), to a robot execution model (REM). The refinement is realized through a graph-based product-process representation and rule-based construction recipes, enabling the derivation of atomic construction actions at a granularity suitable for robotic execution. The execution environment builds on the results and supports both simulated and real-world operation. It relies on middleware, such as ROS 2 for real-time robot communication or Apache Kafka for scalable integration with higher-level services. Central orchestration is achieved through behavior-tree-based multi-robot coordination.Publicationtype: Conference Paper33 - 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: without files , A reference architecture for digital immune systemsModern structural health monitoring (SHM) systems and the physical structures being monitored constitute coupled cyber-physical systems that must be protected as a whole. As a consequence, threats no longer solely stem from structural anomalies, but also from internal SHM system faults and, in particular, from cyberattacks that may falsify sensor data, disrupt communication, or even sabotage critical infrastructure. The capabilities required to withstand the threats are often subsumed under the notion of a “digital immune system”. However, prior work on digital immune systems either targets other application domains that cannot be directly transferred to civil infrastructure or uses the label “digital immune system” as a buzzword without systematically mapping biological immune functions to digital counterparts. This paper presents a biologically inspired reference architecture for digital immune systems for civil infrastructure, defines and formally specifies the threat categories relevant to coupled cyber-physical SHM systems, introduces a compact illustrative sensor-fault scenario, and derives implementation-oriented recommendations intended to support computational modeling and simulation. Explicitly drawing from the biological immune functions, the reference architecture includes innate and adaptive layers, distinguishing between cellular-oriented, local modules and humoral-oriented, global elements. The architecture is intended to serve as a generally applicable blueprint for designing digital immune systems for civil infrastructure, aiming to advance cyber-physical SHM systems at the interface between civil engineering and computer science.Publicationtype: Conference Paper14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Climate-informed engineering for subsurface management: Concept, framework, and applicationsSubsurface resource management underpins a wide range of energy, storage, water, and extraction systems that operate over long timescales and interact with complex geological environments. The performance and integrity of these systems are sensitive to variability in surface climate conditions that control thermal loading, fluid recharge, pressure evolution, and mechanical response in the subsurface. As precipitation patterns, temperature regimes, and extreme events evolve, traditional engineering approaches based on stationary or spatially coarse climate assumptions introduce increasing uncertainty into long-term design, operation, and risk assessment. This paper advances the emerging concept of Climate-Informed Engineering by developing a forward-looking framework tailored to resilient subsurface management and earth energy systems. The framework emphasizes the use of high-resolution, time-dependent climate information to define site-specific boundary conditions that are directly relevant to subsurface processes. By discussing advances in climate modeling, subsurface process understanding, and engineering decision-making, we identify key climate drivers and coupled hydro–thermal–mechanical–chemical processes that govern system performance, integrity, and risk. The applicability of Climate-informed Engineering for Subsurface Management (CIE–SM) is examined across major subsurface domains, including geothermal energy systems, underground energy storage, carbon storage, managed aquifer recharge, and subsurface mining and excavation. By treating climate as a time-evolving, site-specific boundary condition, this work positions CIE–SM as a practical foundation for robust, adaptive, and physically grounded subsurface engineering under environmental uncertainty.Publicationtype: Journal Article25 50