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Item type:Publication, Access status: without files , Prescriptive maintenance of sewer systems based on large language models(2026-07); ; ; Sewer systems constitute urban infrastructure critical for public health, environmental protection, and flood prevention. Decision-making in sewer systems maintenance often fails to effectively and efficiently interpret heterogeneous and multimodal inspection data into actionable maintenance strategies. To improve decision-making in sewer systems maintenance, this paper introduces a prescriptive maintenance (RxM) framework, in which a pre-trained large language model (LLM) is adapted to the domain of sewer systems using adapter-based fine-tuning. As will be shown in this paper, the LLM is capable of interpreting heterogeneous, multimodal inspection data and recommending stepwise repair procedures of sewer systems, following the paradigm of RxM.Publicationtype: Conference Paper8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A decentralized digital twinning approach for assessing civil engineering structures(2026-07); ; In wireless structural health monitoring (SHM), the focus is usually placed on reducing the power-consuming data transmission for preserving the limited resources of wireless sensor nodes. In this direction, embedded computing has been leveraged for distributing SHM data analysis tasks to the sensor nodes instead of transmitting raw data to centralized servers. This paper reports on decentralizing SHM tasks using reduced-order numerical models of structures, embedded into wireless sensor nodes as decentralized digital twins that use sensor data to assess the structural condition. The applicability of the proposed approach is demonstrated via laboratory validation tests.Publicationtype: Conference Paper7 - 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: 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 , Automated defect detection in clay printingAdditive manufacturing (AM) of eco-friendly materials has the potential to decarbonize the construction industry by enabling the creation of complex structures with minimal waste. Clay has been integrated into AM processes as a building material, giving rise to an emerging research field referred to as “clay printing”. Defects, such as tearing and sagging, are common in clay printing and could affect the structural integrity, load-bearing capacity, and overall durability of the structures. However, limited research on defect detection in clay printing and lack of datasets restrict the development of defect detection models. This paper presents a tool - the automated defect detection (ADD) preprocessor - developed to generate a dataset for defect detection models in clay printing. The tool uses images and videos as input for preprocessing and labeling images required to build the dataset, meeting the requirements of defect detection models based on convolutional neural networks. The ADD preprocessor is implemented and validated as a proof of concept for clay printing processes. The results demonstrate the capability of the ADD preprocessor to successfully build a dataset for the deployment of defect detection models in clay printing.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Digital-twin-based management of sewer systems: research strategy for the KaSyTwin project(Multidisciplinary Digital Publishing Institute (MDPI), 2025-01-22); ; ; ; Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing.Publicationtype: Journal Article19 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital twins for sewer systems maintenance: a systematic literature review(Springer, 2025); ; Sewage systems are critical urban infrastructure elements, essential for flood prevention, environmental protection, and public health. As part of sewage system infrastructure, and similar to other infrastructure, sewer systems are deteriorating due to aging. Current maintenance measures for sewer systems predominantly rely on manual processes based on heterogeneous and inconsistent data. However, digital sewer models are expected to facilitate proactive maintenance strategies to mitigate damage and extend the lifespan of sewer systems. This paper investigates current state-of-the-art research concerning the implementation of digital twins in sewer system maintenance during the operating phase through a systematic literature review (SLR). The findings indicate that digital twins offer numerous advantages for proactive maintenance in sewer systems, specifically for sewer system assessment and damage prognosis. Nonetheless, in the current body of research, confusion between digital twins and simpler digital shadows and digital models is apparent. Furthermore, the implementation of digital twins for sewer system maintenance is associated with several challenges, including data-related, technological, and methodological challenges. Finally, the outcome of the SLR sheds light on potential research directions towards realizing digital twins based on building information modeling (BIM), e.g., BIM-based digital twins, for sewer system maintenance.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automated defect detection in fused filament fabrication coupling deep learning and computer visionFused filament fabrication (FFF) is an additive manufacturing technique, popular due to its versatility and cost-effectiveness. However, FFF machines, such as 3D printers, are prone to runtime errors, wasting time and material, while requiring constant human supervision. This paper presents a defect detection approach for FFF processes based on artificial intelligence, combining convolutional neural networks and computer vision. The defect detection approach is validated using 3D prints designed to mimic common FFF defects. The results demonstrate the capability of the proposed approach to automatically detect defects in FFF processes, thereby reducing time and material waste as well as the need for human supervision.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Blockchain-based secure archiving of sensor malfunctions in structural health monitoring systemsStructural health monitoring (SHM) systems are widely used to assess the condition of civil infrastructure. Sensor malfunctions in SHM systems may result in erroneous assessments, illuminating the need for secure archiving of malfunctions. However, methods with online functionality for secure archiving of sensor malfunctions in SHM systems have not been reported yet. This paper presents a system for secure archiving of sensor malfunctions based on blockchain technology, designed as a modular add-on for SHM systems. Using a public blockchain framework, the blockchain-based archiving system is implemented and validated for two use cases, demonstrating the potential of blockchain for SHM systems.Publicationtype: Conference Paper23