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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 , 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 , Implementation and Validation of a Low-Cost loT-Enabled Shake Table SystemShake tables are essential tools for validating structural health monitoring (SHM) systems. However, commercially available shake tables are relatively expensive and often hardly affordable for research and teaching institutions. Moreover, the widespread adoption of remote working and online teaching, fostered by the COVID-19 pandemic, often prevents researchers and students from physically accessing laboratories to conduct experiments. Research and teaching institutions would benefit from using low-cost shake tables with Internet-of-Things (IoT) capabilities for remotely conducting shake table experiments on test structures equipped with SHM systems. This paper presents the implementation and validation of a low-cost IoT-enabled shake table system (IoT-STS). The IoT-STS is composed of off-the-shelf components and builds upon a four-layer architecture. A smartphone application is devised to send periodic or random excitations, i.e. displacement time histories, through an IoT-based cloud service to a microprocessor, which, via actuators, imposes excitations on the shake table. Vice versa, the microprocessor sends the output displacement data, representing the motion reproduced by the shake table, to the cloud for remote access and visualization. To validate the IoT-STS, displacement time histories, remotely entered through the smartphone application, are compared with output displacement data, recorded by a potentiometer. The results demonstrate that the low-cost IoT-STS is capable of accurately imposing periodic and random excitations on test structures, thus showcasing the compatibility of the IoT-STS with remote working and online teaching in light of the COVID-19 pandemic.Publicationtype: Conference Paper35