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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 , 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: 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 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable sensor fault diagnosis for structural health monitoring(2025); ; Publicationtype: Conference Paper13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Construction site AI: Opportunities and limits of artificial intelligence in civil engineering(2025); ; Sensor faults in structural health monitoring (SHM) systems create significant challenges for maintaining infrastructure safety and operational efficiency. Faults, e.g. caused by sensor aging and environmental conditions, may result in inaccurate monitoring data and unreliable diagnostics. Artificial neural network (ANN) models utilizing time-series data have been proven powerful in diagnosing sensor faults in SHM systems, leveraging the ability of ANN models to analyze complex patterns within large datasets. However, the lack of transparency in – generally – black-box ANN models limits trust and usability, creating barriers to the widespread adoption of ANN models for sensor fault diagnosis. In particular, typical ANN decision-making processes lack explainability and interpretability, thus preventing end- users from confidently relying on ANN-based solutions for SHM applications. To overcome the aforementioned limitation, this study introduces an explainable sensor fault diagnosis (ESFD) approach, drawing from the field of explainable artificial intelligence (XAI), whose application in the context of SHM has been scarce. The ESFD approach essentially consists of a time-series-based ANN concept, designed to enhance the explainability and interpretability of sensor fault diagnosis in SHM by incorporating XAI methods that provide insights into decision-making processes. The ESFD approach is validated using SHM data from a pedestrian bridge for identifying combined sensor faults. The performance of the ESFD approach is assessed using XAI evaluation metrics, including “sufficiency” as a faithfulness metric. The results from the validation tests demonstrate that the ESFD approach improves the explainability and interpretability of time-series-based ANN models for sensor fault diagnosis in SHM. This advancement is expected to support the development of explainable and autonomous SHM systems. Future research will focus on decentralizing the ESFD approach for explainable, real-time decision-making to enhance scalability and reduce computational overhead.Publicationtype: Conference Paper49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital-twin-based monitoring and simulation of robot kinematics for clay printing(2024-09); ; ; 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital twins, architectures, and elements in civil engineering – A multivocal literature review(Springer, 2024-08); ; ; ; A plenitude of digital twin reviews have been published in recent years, most of which, however, solely review project-specific digital twin implementations or focus on specific application areas within civil engineering. Digital twin architectures, including the composition of the internal elements of digital twins, have received little attention so far. A common understanding of digital twin architectures and the composition of their elements would advance digital twin implementations, enhancing the reliability and performance of digital twins. In this study, a multivocal review of digital twins in civil engineering is conducted, aiming to provide a scientific basis for reaching a consensus on the abstract architecture and the composition of the internal elements of digital twins. Methodologically, the review is conducted in a twofold manner, accounting for both peer-reviewed, indexed literature, as well as for material that may report on research and development projects and industrial applications not indexed in well-accepted citation databases. The main contribution of this study is a systematic overview of existing architectures of digital twins implemented in civil engineering, including the internal elements. Furthermore, a generic reference architecture of digital twins is proposed, and digital twins are defined in both an informal and formal (i.e., mathematical) manner. It is expected that the reference architecture and the definitions proposed in this study will serve as a blueprint for digital twin implementations in civil engineering, providing a common understanding and enhancing the reliability and performance of digital twins.Publicationtype: Conference Paper45 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Identification of combined sensor faults in structural health monitoring systems(IOP Publishing, 2024-07-19); ; ; ; Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.Publicationtype: Journal Article10 172 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Identification of composite sensor faults in structural health monitoring systems using long short-term memory networks(European Council on Computing in Construction (EC3), 2024-07); ; ; ; Fault identification (FI) is an integral part of sensor fault diagnosis in structural health monitoring (SHM) systems. However, current FI approaches often overlook composite sensor faults, i.e. different sensor fault types occurring simultaneously within an individual sensor. As a result, actual fault occurrences in real-world SHM systems may be underestimated. This paper introduces an FI approach utilizing long short-term memory networks, addressing composite faults. The FI approach is validated using sensor data recorded by a real-world SHM system. The results demonstrate the capability of the FI approach to identify composite sensor faults, thus enhancing the reliability and accuracy of fault diagnosis.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A deep-learning-based approach towards identifying combined faults in structural health monitoring(NDT.net, 2024-07-01); ; ; ; Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.Publicationtype: Conference Paper15 91