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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 , 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 , On the vulnerability of citation metrics in the era of generative artificial intelligenceLarge language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, and total citation counts) remain embedded in research evaluation and are sensitive to indexing practices of bibliographic databases, with Google Scholar providing broad coverage combined with comparatively limited curation. In this study, a systematic literature review is conducted to synthesize reported mechanisms of citation-metric manipulation and to examine limitations of citation-metric use, including evidence reported in civil engineering. A Google Scholar proof-of-concept case study examines whether the indexing of LLM-assisted, non-peer-reviewed documents with concentrated references to a target author is associated with changes in author-level citation metrics under platform-specific conditions. After indexing, a stepwise increase in author-level metrics is observed, demonstrating the feasibility of citation-metric manipulation under the platform-specific conditions. Finally, this paper discusses the implications for research integrity and citation manipulation in the era of generative artificial intelligence. It also presents recommendations for researchers, academic institutions and evaluation committees, publishers and editors, bibliographic database providers, and funding institutions and policymakers.Publicationtype: Journal Article36 19 - 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