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    Multi-dimensional sensor fault diagnosis for decentralized wireless structural health monitoring
    Recent trends towards digitalization have been shaping modern structural health monitoring (SHM) concepts, promoting wireless technologies for SHM with wireless sensor nodes capable of decentralized “on-board” data analysis. In this context, and given the lack of constant sensor data transmission, the reliable and fault-tolerant operation of wireless sensor nodes is of paramount importance, motivating the development of sensor fault diagnosis (FD) approaches for SHM. FD approaches that have been proven useful in related disciplines cannot simply be adapted to SHM, due to the characteristics of the structures being monitored. Moreover, existing FD approaches may oversimplify reality, frequently assuming that sensor faults appear in individual sensors and with single fault types, while the structural condition is considered unchanged. In practice, though, sensor faults may occur simultaneously in multiple sensors (or sensor nodes), sensor faults may be combined, and the structural condition may change. The proposed project aims to develop a framework for robust sensor FD in modern wireless SHM systems. The proposed framework will include a sensor FD methodology that will address multiple dimensions, i.e. (i) all FD steps (detection, isolation, identification and accommodation) and all common sensor fault types, (ii) real-world sensor fault dimensions (such as single-sensor faults, simultaneous sensor faults as well as single and combined fault types), and (iii) the diagnosis of sensor faults in the event of structural damage. In addition, the framework will include a design concept towards decentralizing the sensor FD methodology into wireless sensor nodes, thus, enabling the performance of self-diagnostics at the edge of wireless sensor networks. In this direction, artificial intelligence techniques will be coupled, enabling decentralized, fault-tolerant sensor data analysis. To provide robustness of the framework, the steps towards detecting, isolating, identifying and accommodating sensor faults will be based on sound mathematical principles. The research hypothesis is formulated as follows: The reliability of modern wireless structural health monitoring systems stands to benefit from a multi-dimensional sensor fault diagnosis framework, enabling robust fault diagnosis of real-world sensor faults “at the edge” of wireless sensor networks, i.e. in an automated/decentralized fashion without user interaction. The outcome of the project is expected to enhance the alignment of modern SHM concepts, centering around wireless sensor networks that feature on-board data analysis, with emerging trends on edge computing through integrating robust self-diagnostic capabilities into the wireless sensor nodes themselves. As a result, wireless sensor nodes, typically already capable of performing SHM tasks locally and quasi-independently, are expected to function as fully-fledged, fault-tolerant Internet-of-Things devices with enhanced operational autonomy.
      6
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    Prescriptive maintenance of road infrastructure coupling multi-view scene graphs and temporal logic
    Aging road infrastructure increasingly compromises safety and economic performance. Robots can already inspect roads and repair selected defects, but current systems remain largely semi-autonomous and dependent on human supervision. Established maintenance management procedures support decisions at network or section level, yet translating selected measures into robotic operations at work-zone level remains difficult. Robust scene understanding is therefore a key prerequisite for higher autonomy in robot-based road infrastructure maintenance. The project aims to develop a methodology for scene understanding tailored to robot-based road infrastructure maintenance, focusing on work-zone-level execution rather than maintenance management at network or section level. The project does not develop a full road-repair machine or asphalt-processing hardware. Instead, the project provides the methodological basis that existing and future maintenance robots require to move from semi-autonomous task execution toward higher levels of autonomy. The underlying hypothesis is that scene understanding improves robot-based road infrastructure maintenance. To achieve the objective, robot capabilities and maintenance procedures will be formalized in a mathematically sound knowledge representation, and digital road models will be exploited systematically. Scene graphs will capture maintenance-relevant semantics and hierarchies. A framework for scene understanding will generate and fuse ontology-grounded multi-view scene graphs from visual data and digital road models and enrich incomplete scene representations under partial observability. A framework for maintenance planning will couple multi-view scene graphs with planning languages, large language models, and temporal logic to synthesize coordinated maintenance plans and monitor correct execution. The project therefore addresses methodological challenges in formal knowledge representation, multi-view scene graph fusion, knowledge enrichment, planning integration, and runtime monitoring. Validation will be carried out in a staged manner through simulations, laboratory experiments, and field tests of selected core functions in cooperation with public road operators, who will provide infrastructure data, planning documents, and access to real-world operational environments. The expected outcome is a validated methodology for more transparent, reliable, and safety-aware robot-based road infrastructure maintenance. Beyond the application domain, the project is expected to contribute generalizable methodological advances in knowledge-based scene representation, formal planning, semantic reasoning, and execution monitoring for autonomous robotic systems.
      3
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    GRK 3068 - Climate-informed Engineering
    Spokesperson: Professor Dr. Shokri Deputy spokesperson: Professor Dr. Smirnova Breakthroughs in computing have led to the development of new generations of Earth Systems Models, which provide detailed information on how our planet may locally respond to the ongoing global warming, with unprecedented spatial and temporal resolutions of 1 km and a few minutes, respectively. This massive climate data may be of little value, if not utilized by engineers who are involved in developing technical solutions for real-world challenges. Engineers stand to benefit from seizing this opportunity and by incorporating climate data in engineering designs, solutions, and practices. This benefit is precisely the key driving force for founding the Research Training Group (RTG) on Climate-informed Engineering (CIE) as an emerging interdisciplinary field of research integrating state-of-the-art climate information with engineering education. A structured training strategy is designed in the RTG featuring a broad range of educational activities to facilitate training and promote early-career researchers who will contribute to developing the next generation of engineering solutions that are adaptive to climate. In doing so, we will integrate a new generation of climate models in our training through the active involvement of the Max-Planck Institute for Meteorology (MPI-M), an internationally renowned organization at the forefront of global efforts on climate models. Furthermore, the RTG offers a joint PhD program between TUHH and the United Nations University Institute for Water, Environment and Health (UNU-INWEH). Hence, the PhD candidates will benefit from the interactions with renowned experts at UNU and the UN on a variety of topics related to United Nations Sustainable Development Goals, which is at the heart of our RTG. The RTG will utilize engineering science and innovative approaches to develop new materials, processes, and predictive capabilities to help people, businesses, and ecosystem in the face of climate change. The RTG will include three main Research Areas, namely CIE for Built Environment, CIE for Process Engineering and CIE for Sustainable Resource Management and Environment. Ten projects are designed in the first funding phase, covering a wide range of topics, spanning from influence of climate on renewable resources and food engineering to developing novel materials for latent heat storage. The projects will couple indoor and outdoor climates based on Internet-of-Things technologies and will develop predictive capabilities for water and food security. All the principal investigators and PhD candidates share the common goal of employing new-generation climate information to devise strategies for mitigating climate change. This interdisciplinary RTG is the first of its kind, ultimately enabling engineers to build infrastructure and to develop new materials and processes that are informed by the climate data, which will be an increasingly important dimension of engineering education in the 21st century.
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    Decentralized digital twins for structural health monitoring
    Amid the digital transformation, structural health monitoring (SHM) is increasingly relying on wireless sensor networks that transmit structural response data to central servers for offline data analysis, which, however, is associated with several problems, as (i) the requirements for data storage and management may be difficult to fulfil, (ii) analyzing large amounts of data may require powerful database systems and complex algorithms, and (iii) the wireless data transmission may cause issues of data security and data loss. Therefore, research has focused on decentralizing data analysis, via embedding data-driven models into the microcontrollers of wireless sensor nodes, to enable autonomous data analyses on the sensor nodes. However, embedded data-driven models yield limited information on the structural condition, as compared to the information extracted by centralized offline data analyses that combine structural response data with physics-based models, such as finite element models. As a result, a decentralized data analysis approach that (a) overcomes the problems of centralized data analysis but (b) yields rich information on the structural condition is necessary. This project aims to develop a methodology for decentralized data analysis in wireless SHM systems using information-rich numerical models, where digital twins (communicating with each other) are distributedly embedded into the microcontrollers of the wireless sensor nodes. The digital twins will be first segmented into “partial digital twins”, and each partial digital twin will be embedded into one wireless sensor node and used for local data analysis. In this project, digital twins will be represented by finite element models that approximate the structural condition. As a result, the partial digital twins essentially will be formulated as “partial finite element models”, i.e. finite element models segmented into substructures with internal degrees of freedom and interface degrees of freedom. The partial digital twins will be tailored to the characteristics and computational capabilities of wireless sensor nodes, in terms of geometry, mesh refinement, and computational cost. The expected outcome of the project is a methodology for decentralizing digital twins for SHM that advances the level of information on the structural condition currently obtained by state-of-the-art embedded data-driven models, enabling wireless sensor nodes to describe the structural condition in detail. The project is expected to enhance the efficiency and robustness of SHM, thus fulfilling the needs of the increasingly digitalized landscape of structural maintenance. The outcome of the project will align SHM with the latest concepts in digital transformation, building upon the emerging paradigms within Industry 4.0.
      19
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    Robot-based inspection of civil infrastructure using frame semantics and linguistic metamodels
    Future construction sites, frequently referred to as "smart construction sites", will be characterized by digitalization, automation, and interconnection. Subsumed under "Construction 4.0", smart construction sites realize the fourth revolution in construction, taking advantage of the momentum gained from the progress in robotic systems research and development. Robotic systems are increasingly deployed to automate inspections of civil infrastructure both in the construction phase and in the operation phase. To conduct robot-based inspections, inspection routes must be calculated and, if necessary, the routes of robotic fleets, collaboratively conducting inspection tasks, must be minimized and efficiently distributed. Since using digital building models, i.e. building information models, is mandatory in many areas of public construction, it is reasonable to use building information models as a conceptual basis for calculating inspection routes. Currently, however, it is hardly possible to fully integrate robot information into digital building models, which would enable efficient communication and coordination among robotic systems. This research project aims to provide a methodology for automating robot-based inspections using digital building models as a conceptual basis. The research hypothesis, formulated to achieve this aim, can be reduced to the following simple statement: Once an inspection task is specified, the inspection routes for robot fleets can reliably be calculated, based on (i) robot information and (ii) building information derived from digital building models. In this project, the semantics inherent to digital building models, which implicitly contain the information relevant to route calculation, serve as a formal basis for coupling robot and building information. Taking into account existing knowledge sources, the coupling is performed through frame semantics as a formal means for knowledge representation, since frame semantics are linguistically founded in cognitive science and have proven to be a powerful tool in construction grammar and computational linguistics. The frame-based knowledge, as a cognitive representation of the real world, is then mapped into linguistic metamodels in the form of ontologies, easily comprehensible by engineers, which allow generating specific inspection models. Using a reference bridge, standard-compliant building information models will be applied to validate the methodology. As a result, the research project is expected to provide a formal approach for reliably and efficiently deploying robotic systems to inspect civil infrastructure. It is further expected to provide a robust scientific foundation for assessing the potential of robotic systems to enhance the current state of the art of civil infrastructure inspections.
      23
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    FOR 5672: Multi-robot collaboration for monitoring and inspection of civil engineering structures
    Monitoring and inspection, bolstered by the advancements in Internet-of-Things technologies, are vital tools for evaluating the performance of construction projects and, in turn, ensure structural safety, save time, and reduce costs. However, when conducted manually, monitoring and inspection tasks are time-consuming, error-prone, and costly. To overcome these drawbacks, deploying robotic systems to automatically execute monitoring and inspection tasks is a promising approach, which requires robust concepts that ensure reliable and efficient multi-robot collaboration. Although multi-robot collaboration is not new, existing approaches are commonly restricted to robotic systems of one robot type responsible for one task, the potential of the semantics inherent to digital building models has not been fully exploited, and the uncontrolled on-site environments place challenges to the existing approaches that have not yet sufficiently been solved, which, in total, renders multi-robot collaboration in on-site environments a complex engineering task that will be addressed in this subproject. The goal of this subproject is to advance multi-robot collaboration, aiming to support monitoring and inspection of civil engineering structures. Integrating different robot types for solving different tasks, this subproject will take advantage of the semantics provided by existing digital building models. Drawing from a common starting point that aligns all subprojects with the information backbone of the overall research unit, a semantic inspection model will be developed, based on a semantic analysis of civil engineering structures and a formalization of monitoring and inspection activities. In addition to the semantic inspection model, a semantic multi-robot collaboration model for monitoring and inspection of civil engineering structures will be developed by conceptualizing the behavior and capabilities of robots using state machines and class diagrams. The aforementioned semantic models will be formalized in the form of ontologies, and the correctness will be verified through model checking. The semantic models will then converge into a collaborative multi-robot inspection methodology, which represents the core of this sub project. The methodology allows, in a generic way, to generate and optimize tasks and plans for monitoring and inspection, based on digital building models. The methodology couples the aforementioned semantic models with reasoning techniques, description and temporal logic, and with multi-agent technology. For validating the proposed methodology, virtual experiments (i.e. simulations) as well as physical experiments (i.e., laboratory and field tests) will be conducted. As a result, it is expected that monitoring and inspection may be improved by reliable and efficient multi-robot collaboration valid for different types of robotic systems that make use of the semantics inherent to digital building models readily available in civil engineering.
      28
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    Digitalization of earth-based construction processes
    Earth-based structures have a very low environmental impact and provide a healthy indoor climate to inhabitants, thus representing sustainable alternatives to conventional structures. However, labor-intensive, time-consuming processes and the requirement of manual building skills restrict the productivity of earth construction processes, which are based on a centuries-old state of practice. To overcome the current difficulties, principles of additive manufacturing (AM) and smart sensing technologies may be coupled with earth construction processes towards so-called “earth-based AM”, increasing the quality and advancing the productivity. Through digitalizing earth construction processes, earth-based AM has the potential to transform the current practice for cob, adobe, and rammed earth into a smart manufacturing process. Evidently, to efficiently conduct earth-based AM, a formal description is necessary, which allows adequately gathering, storing, analyzing, and describing the information required for earth-based AM. Furthermore, digital data models, providing the basis for construction robots to “print” earth-based structures without information loss, must be extended or developed anew. The proposed research project aims to advance the digitalization of earth-based AM, or "earth printing", by providing a formal conceptual basis for a new methodology for data modeling in earth-based AM. The methodology will generically define earth-based structures and the overall process of earth-based AM. The methodology, although valid for additive manufacturing in general, is expected to bridge information gaps in the digital workflow of earth printing without information loss, enabling (i) modeling of the parameters and the semantic relationships within earth-based AM (process parameters), including sensing technologies that support process planning, process monitoring, and process control, (ii) integration of earth-based materials into the digital workflow of earth-based AM ("digital material"), including material composition, material properties, and material behavior relevant to extrudability and buildability (material parameters), and (iii) instantiation of the digital models (based on geometry parameters) for designing, simulating, and printing earth-based structures. The methodology will be verified using a verification concept for model checking and reasoning to be proposed for earth-based AM, building upon formal logic, to increase the quality and the productivity of earth-based AM. For validation, use cases will be defined and experimental tests will be conducted using a robotic printing system. As a result, the outcome of this project is expected to provide a formal basis that semantically describes all information relevant to earth-based AM, including structures and processes, to further advance earth-based AM towards smart manufacturing.
      21