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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 Article19 7 - 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 167 - 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