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Die Weltbevölkerung wächst und gleichzeitig werden die Ressourcen immer knapper. Einen großen Anteil daran hat das Bauwesen. Die Lösung für ressourceneffizientes Bauen, sei es auf dem Wasser oder an Land, sehen Expertinnen und Experten in anpassungsfähigen, so genannten adaptiven Strukturen. Sie beschreiben die Idee, wie künftig für mehr Menschen ressourcenschonend und emissionsfrei gebaut werden kann. Dabei soll nicht nur mehr Wohnraum mit weniger Material geschaffen werden, sondern auch der Energieverbrauch gesenkt und der Nutzerkomfort gesteigert werden. Über den neuesten Stand der Technik diskutieren Expertinnen und Experten aus Wissenschaft und Wirtschaft am 23. und 24. Mai im Rahmen des Workshops „Adaptive Structures at Shore“ an der Technischen Universität Hamburg. In unterschiedlichen Fachvorträgen geht es einerseits darum, welche Auswirkungen beispielsweise Wind und Wasser auf Kaimauern oder schwimmende Bauwerke haben, oder inwiefern eine hohe Verkehrslast Brücken beansprucht. Dabei stellt sich die Frage, wie Bauwerke nachhaltig auf entsprechende Herausforderungen optimiert und angepasst werden können und inwiefern dabei Modellierungen und Simulationen helfen können. Der interdisziplinäre, englischsprachige Workshop wird durch das TU-Institut für Geotechnik und Baubetrieb organisiert und richtet sich an Interessierte unterschiedlicher Fachbereiche aus Wissenschaft, Wirtschaft und Verwaltung., Adaptive structures at shore provide an opportunity to minimize the resource consumption and the impact on the marine ecosystems through an efficient adaptation to changing boundary conditions. A combination of impact prediction, monitoring and material resistance estimation, numerical modelling, and innovative design is the key to success. The workshop “Adaptive Structures at Shore 2022” hosted by Hamburg University of Technology (TUHH) brings together international scientists and professionals to discuss the latest developments in the field and to explore synergies.

Intelligent transportation systems (ITS) provide safer, greener, and more convenient mobility, while reducing the impact on the environment. In recent years, simulation platforms have been employed to study ITS applications, mostly focusing on traffic-related simulations. Despite several research studies on ITS applications and simulation platforms, formal semantic descriptions of intelligent transportation systems have not been addressed yet. In this paper, a semantic model describing intelligent transportation systems for roads is proposed. The semantic model is devised to provide a basis for designing ITS simulation platforms. Building upon the semantic model, an extension to an open building information modeling (BIM) standard, i.e., the Industry Foundation Classes (IFC) schema, is presented. The IFC schema extension is verified and validated using a BIM-based simulation scenario of ITS for roads. It is shown that the proposed IFC-compliant description of ITS for roads provides a formal basis for generating BIM-based simulations and hence facilitates ITS infrastructure modeling in BIM projects. It is concluded that the present work represents a cornerstone for designing BIM-based ITS simulation platforms. In future endeavors, potential standardization and formalization efforts may be discussed.

Rapid advancements of modern technologies put high demands on mathematical modelling of engineering systems. Typically, systems are no longer “simple” objects, but rather coupled systems involving multiphysics phenomena, the modelling of which involves coupling of models that describe different phenomena. After constructing a mathematical model, it is essential to analyse the correctness of the coupled models and to detect modelling errors compromising the final modelling result. Broadly, there are two classes of modelling errors: (a) errors related to abstract modelling, eg, conceptual errors concerning the coherence of a model as a whole and (b) errors related to concrete modelling or instance modelling, eg, questions of approximation quality and implementation. Instance modelling errors, on the one hand, are relatively well understood. Abstract modelling errors, on the other, are not appropriately addressed by modern modelling methodologies. The aim of this paper is to initiate a discussion on abstract approaches and their usability for mathematical modelling of engineering systems with the goal of making it possible to catch conceptual modelling errors early and automatically by computer assistant tools. To that end, we argue that it is necessary to identify and employ suitable mathematical abstractions to capture an accurate conceptual description of the process of modelling engineering systems

One of the most promising and recent advances in computer-based planning is the transition from classical geometric modeling to building information modeling (BIM). Building information models support the representation, storage, and exchange of various information relevant to construction planning. This information can be used for describing, e.g., geometric/physical properties or costs of a building, for creating construction schedules, or for representing other characteristics of construction projects. Based on this information, plans and specifications as well as reports and presentations of a planned building can be created automatically. A fundamental principle of BIM is object parameterization, which allows specifying geometrical, numerical, algebraic and associative dependencies between objects contained in a building information model. In this paper, existing challenges of parametric modeling using the Industry Foundation Classes (IFC) as a federated model for integrated planning are shown, and open research questions are discussed.

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems.

This conceptual paper discusses opportunities and challenges towards the digital representation of structural health monitoring systems using the Industry Foundation Classes (IFC) standard. State-of-the-art sensor nodes, collecting structural and environmental data from civil infrastructure systems, are capable of processing and analyzing the data sets directly on-board the nodes. Structural health monitoring (SHM) based on sensor nodes that possess so called “on-chip intelligence” is, in this study, referred to as “intelligent SHM”, and the infrastructure system being equipped with an intelligent SHM system is referred to as “intelligent infrastructure”. Although intelligent SHM will continue to grow, it is not possible, on a well-defined formalism, to digitally represent information about sensors, about the overall SHM system, and about the monitoring strategies being implemented (“monitoring-related information”). Based on a review of available SHM regulations and guidelines as well as existing sensor models and sensor modeling languages, this conceptual paper investigates how to digitally represent monitoring-related information in a semantic model. With the Industry Foundation Classes, there exists an open standard for the digital representation of building information; however, it is not possible to represent monitoring-related information using the IFC object model. This paper proposes a conceptual approach for extending the current IFC object model in order to include monitoring-related information. Taking civil infrastructure systems as an illustrative example, it becomes possible to adequately represent, process, and exchange monitoring-related information throughout the whole life cycle of civil infrastructure systems, which is referred to as monitoring information modeling (MIM). However, since this paper is conceptual, additional research efforts are required to further investigate, implement, and validate the proposed concepts and methods.

In construction engineering, a schedule’s input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project.

Modern distributed engineering applications are based on complex systems consisting of various subsystems that are connected through the Internet. Communication and collaboration within an entire system requires reliable and efficient data exchange between the subsystems. Middleware developed within the web evolution during the past years provides reliable and efficient data exchange for web applications, which can be adopted for solving the data exchange problems in distributed engineering applications. This paper presents a generic approach for reliable and efficient data exchange between engineering devices using existing middleware known from web applications. Different existing middleware is examined with respect to the suitability in engineering applications. In this paper, a suitable middleware is shown and a prototype implementation simulating distributed wind farm control is presented and validated using several performance measurements.

International audience; Sensor faults in wireless structural health monitoring (SHM) systems may reduce the monitoring quality and, if remaining undetected, might cause substantial economic loss due to inaccurate or missing sensor data required for structural assessment and life-cycle management of the monitored structure. Usually, fault detection in sensor networks is achieved through a redundant deployment of sensors and further hardware components ("physical redundancy"), which involves considerable penalties in cost and maintainability. Overcoming these drawbacks, in this study the information inherent in the SHM system and the known relationships between the sensors are used for fault detection without the need for additional sensors ("analytical redundancy"). Furthermore, the analytical redundancy approach is implemented in a fully decentralized manner: Partial models of the SHM system, being embedded directly into the wireless sensor nodes, enable each sensor node to autonomously detect sensor faults in real time while efficiently using the limited computing resources.

Vibration-based methods, using response measurements of civil engineering structures, are among the most popular methods for damage detection and system identification in structural health monitoring (SHM). To accurately obtain response measurements, wireless sensors networks are increasingly applied for collecting monitoring data from the observed structures. However, several types of noise usually contaminate the monitoring data leading to inaccurate or even incorrect diagnoses and prognoses of the dynamic properties of the structures. The main objective of this study is the elimination of such noise directly on the wireless sensor nodes in order to obtain high-quality monitoring data. Specifically, a wireless SHM system is designed and, for validation purposes, mounted on a tower-like laboratory test structure. The wireless sensors are provided with different embedded filters designed to improve the estimation of the system properties, which are calculated from the dynamic response measurements, i.e. from the monitoring data. In a final step, the quality of the dynamic response measurement is assessed.

Due to increasing numbers of wind energy converters, the accurate assessment of the lifespan of their structural parts and the entire converter system is becoming more and more paramount. Lifespan-oriented design, inspections and remedial maintenance are challenging because of their complex dynamic behavior. Wind energy converters are subjected to stochastic turbulent wind loading causing corresponding stochastic structural response and vibrations associated with an extreme number of stress cycles (up to 109 according to the rotation of the blades). Currently, wind energy converters are constructed for a service life of about 20 years. However, this estimation is more or less made by rule of thumb and not backed by profound scientific analyses or accurate simulations. By contrast, modern structural health monitoring systems allow an improved identification of deteriorations and, thereupon, to drastically advance the lifespan assessment of wind energy converters. In particular, monitoring systems based on artificial intelligence techniques represent a promising approach towards cost-efficient and reliable real-time monitoring. Therefore, an innovative real-time structural health monitoring concept based on software agents is introduced in this contribution. For a short time, this concept is also turned into a real-world monitoring system developed in a DFG joint research project in the authors’ institute at the Ruhr-University Bochum. In this paper, primarily the agent-based development, implementation and application of the monitoring system is addressed, focusing on the real-time monitoring tasks in the deserved detail.

Die Bauwerksüberwachung gewinnt aus sicherheitstechnischen sowie aus wirtschaftlichen Gründen zunehmend an Bedeutung. Nicht nur die Bauwerkssicherheit kann durch leistungsfähige Monitoring-Systeme angemessen beurteilt, auch die Nutzungsdauer bestehender Bauwerke kann durch die gewonnenen Informationen deutlich verlängert werden. Das vorliegende Papier beschreibt die Entwicklung eines webbasierten Talsperren-Monitoring-Systems, das die automatisierte Erfassung von Daten vor Ort sowie die computergestützte Aufbereitung und Analyse der gesammelten Messdaten ermöglicht. Das Monitoring-System ist durch seinen modularen Aufbau nicht auf die Talsperren-Überwachung beschränkt, sondern kann ohne großen Aufwand an andere Überwachungsaufgaben angepasst werden. Das System besteht aus drei wesentlichen Modulen: (i) einer erweiterbaren Klassenbibliothek, die die Steuerung der im Bauwerk installierten Messelektronik ermöglicht, (ii) einem webbasierten Datenerfassungsmodul, das neben der automatischen Datenerfassung eine Fernsteuerung der Messelektronik erlaubt und Funktionen zur Verwaltung der Überwachungsaufgaben bereitstellt, sowie (iii) einem webbasierten Visualisierungs- und Auswertungsmodul zur Aufbereitung und Analyse der gesammelten Daten. Alle an der Überwachung beteiligten Mitarbeiter können mit einem üblichen Web-Browser über das Internet auf das entwickelte System zugreifen; ein Zugriff mittels Mobiltelefon ist alternativ möglich. Das implementierte Talsperren-Monitoring-System begleitet die beteiligten Fachleute von der Erfassung der Daten vor Ort bis hin zur Aufbereitung und Analyse der Messdaten an zentraler Stelle: Die Mitarbeiter werden durch einen einfachen Zugriff auf die installierte Messelektronik, automatisierte Messungen und umfangreiche Analysefunktionalitäten bei ihren spezifischen Aufgaben unterstützt. Der bisherige manuelle Arbeitsaufwand für Datenerfassung, -transfer und Analyse wird somit deutlich reduziert.