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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 , Decentralized sensor fault diagnosis for wireless structural health monitoring systems using Artificial Intelligence of Things(DEStech Publications, 2025-09); ; ; ; Structural health monitoring (SHM) is a non-destructive evaluation technique that utilizes sensor data for assessing the condition of civil infrastructure. Sensors in SHM systems may experience faults, which may influence the accuracy, reliability, and performance of SHM systems. The timely detection, isolation, and accommodation of sensor faults in SHM systems has been the focus of sensor fault diagnosis (FD) approaches, which have increasingly been employing artificial intelligence (AI) algorithms due to the effectiveness of AI in sensor FD. However, current AI-based FD approaches require transmitting large amounts of raw sensor data to centralized servers for offline analysis, resulting in inefficiencies as well as computational burdens on centralized servers. This paper introduces a decentralized sensor fault diagnosis (DSFD) approach for wireless SHM systems using Artificial Intelligence of Things (AIoT). In particular, AI-based FD models are embedded into wireless sensor nodes of SHM systems to detect, isolate, and accommodate sensor faults. By embedding the FD models into the wireless sensor nodes, only high-level information, specifically the status of the sensors, is transmitted to centralized servers. As a result, data transmission inefficiencies as well as computational burdens on centralized servers are reduced. The proposed DSFD approach is validated in a controlled laboratory experiment, in which custom- built wireless senor nodes are installed on a test structure that is dynamically excited using a shake table. After training and embedding the AI-based FD models into the custom-built wireless sensor nodes, sensor faults are artificially injected into the sensor data, demonstrating the ability of the DSFD approach to diagnose sensor faults in a decentralized manner. The results of the validation test corroborate the capability of the proposed approach to efficiently ensure the accuracy, reliability, and performance of SHM systems.Publicationtype: Conference Paper5 - 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 Paper20 - 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: 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A multivocal literature review of digital twins, architectures, and elements in civil engineering(NDT.net, 2024-07-01); ; ; ; Structural health monitoring strategies in civil engineering increasingly leverage digital twins, supporting predictive maintenance and data-driven decision-making. Despite the widespread adoption of digital twin applications in structural health monitoring (SHM), there is a lack of agreement on a common definition of digital twins. Although a significant number of digital twin definitions have been proposed and a plethora of reviews have been published, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Furthermore, drawing from the review results, a digital twin definition is formulated and a generic digital twin reference architecture is proposed. It is expected that the definition and the generic reference architecture proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.Publicationtype: Conference Paper24 156 - 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 , AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring(NDT.net, 2024-07-01); ; ; Artificial intelligence (AI) algorithms have proven effective in implementing sensor fault diagnosis (FD) for wireless structural health monitoring (SHM). However, FD models based on AI are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. This paper proposes a decentralized framework for sensor fault diagnosis in wireless SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Within the decentralized framework, FD models are embedded into wireless sensor nodes to ensure that the data collected from engineering structures is fault-free. Thus, only the condition of SHM systems, instead of raw data, is transmitted from SHM systems to centralized servers via Internet-of-Things communication. To validate the decentralized framework proposed in this paper, an SHM system is implemented using (i) a portable main station containing the FD models and (ii) four tailor-made wireless sensor nodes equipped with microcontrollers and accelerometers deployed on a test structure. The results of the validation tests show that the SHM system successfully collects acceleration data and diagnoses, in real-time, sensor faults that are inserted into the sensor nodes. In future work, the decentralized framework and the SHM system presented in this paper may be deployed on a bridge for structural condition assessment, while ensuring early detection of sensor faults.Publicationtype: Conference Paper28 95 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital twin architectures in civil engineering: A systematic literature review(2024-03); ; ; Numerous reviews of digital twins have been reported in recent years. However, the majority of digital twin (DT) reviews within the field of civil engineering primarily examine DT implementations tailored to specific projects or investigate particular application domains in civil engineering. DT architectures, including layers and components, have received minimal consideration until now. This study conducts a systematic literature review (SLR) of digital twins to establish a scientific basis on digital twins in civil engineering, aiming to reach a reference DT architecture that includes layers and components. The SLR is conducted for peer-reviewed, indexed literature of existing DT architectures to propose the reference DT architecture. The reference DT architecture serves as a blueprint for implementing DT applications in civil engineering, providing a common understanding and strengthening the reliability of digital twins in civil engineering.Publicationtype: Conference Paper23