12 results
Filters
Settings
Now showing1 - 10 of 12
- Some of the metrics are blocked by yourconsent settings
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: 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 21 - 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: with files , Quantum and quantum-inspired computing in civil engineeringQuantum computing is expected to offer solutions to computational problems that are otherwise computationally intractable. Although the core technology is still being developed, quantum-inspired computing already has been offering practical advantages for several computationally challenging problems. Despite the promising potential of both quantum computing and quantum-inspired computing, applications in civil engineering remain underexplored. This study aims to lay the foundation for future adoption by introducing the fundamental principles of quantum computing and quantum-inspired computing and by conducting a multivocal literature review. The review provides insights into the current research landscape in civil engineering and offers a detailed analysis of potential use cases and application areas. The findings are expected to serve as a foundation for guiding future research endeavors and practical deployments of quantum computing and quantum-inspired computing in civil engineering, as these technologies continue to mature.Publicationtype: Journal Article25 25 - 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 Paper22 - 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 , BIM-based human-robot collaboration for building inspections using mixed reality(European Council on Computing in Construction (EC3), 2025-07); ; ; 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An embedded computing approach for vibration-based system identification using reduced-order finite elementsSystem identification (SID) constitutes a focal point in experimental testing of civil infrastructure as well as in structural health monitoring (SHM). Conventional SID approaches have relied on creating finite element (FE) models of civil infrastructure assets and on calibrating the models against in-situ derived information from testing equipment or against SHM data. FE models are capable of providing valuable information on the structural condition. However, the equations governing the FE method are coupled, usually calling for centralized data collection, which may be unsuitable for modern wireless SHM systems that employ independently operating wireless sensor nodes. In this paper, a SID approach for making full use of FE models on board wireless sensor nodes is presented, based on embedded computing and on reduced-order finite elements. Specifically, FE models of civil infrastructure assets are reduced, using model-order reduction, and partitioned into partial models, which represent different substructures and are computationally efficient for embedment in wireless sensor nodes. Using the partial models and structural response data from the substructures, insights into the structural condition of the civil infrastructure assets are obtained. The proposed approach is suitable for vibration-based SID, applied as part of long-term SHM strategies, and implementable in wireless SHM systems. Validation tests using SHM data recorded from a real-world road bridge showcase the validity of the proposed SID approach.Publicationtype: Conference Paper14