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In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM.

Structural health monitoring strategies in civil engineering increasingly leverage digital twins, supporting predictive maintenance and data-driven decision-making. Despite the widespread adoption of digital twin applications in structural health monitoring (SHM), there is a lack of agreement on a common definition of digital twins. Although a significant number of digital twin definitions have been proposed and a plethora of reviews have been published, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Furthermore, drawing from the review results, a digital twin definition is formulated and a generic digital twin reference architecture is proposed. It is expected that the definition and the generic reference architecture proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.

Artificial intelligence (AI) algorithms have proven effective in implementing sensor fault diagnosis (FD) for wireless structural health monitoring (SHM). However, FD models based on AI are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. This paper proposes a decentralized framework for sensor fault diagnosis in wireless SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Within the decentralized framework, FD models are embedded into wireless sensor nodes to ensure that the data collected from engineering structures is fault-free. Thus, only the condition of SHM systems, instead of raw data, is transmitted from SHM systems to centralized servers via Internet-of-Things communication. To validate the decentralized framework proposed in this paper, an SHM system is implemented using (i) a portable main station containing the FD models and (ii) four tailor-made wireless sensor nodes equipped with microcontrollers and accelerometers deployed on a test structure. The results of the validation tests show that the SHM system successfully collects acceleration data and diagnoses, in real-time, sensor faults that are inserted into the sensor nodes. In future work, the decentralized framework and the SHM system presented in this paper may be deployed on a bridge for structural condition assessment, while ensuring early detection of sensor faults.

Wastewater monitoring for SARS-CoV-2 is a valuable tool for surveillance in public health. However, reliable analytical methods and appropriate approaches for the normalization of results are important requirements for implementing state-wide monitoring programs. In times of insufficient case reporting, the evaluation of wastewater data is challenging. Between December 2021 and July 2022, we analyzed 646 samples from 23 WWTPs in Thuringia, Germany. We investigated the performance of a direct capture-based method for RNA extraction (4S-method) and evaluated four normalization methods (NH4-N, COD, N-tot, and PMMoV) in a pooled analysis using different epidemiological metrics. The performance requirements of the 4S method were well met. The method could be successfully applied to implement a state-wide wastewater monitoring program including a large number of medium and small wastewater treatment plants (<100,000 p.e) in high spatial density. Correlations between wastewater data and 7-day incidence or 7-day-hospitalization incidence were strong and independent from the normalization method. For the test positivity rate, PMMoV-normalized data showed a better correlation than data normalized with chemical markers. In times of low testing frequency and insufficient case reporting, 7-day-incidence data might become less reliable. Alternative epidemiological metrics like hospital admissions and test positivity data are increasingly important for evaluating wastewater monitoring data and normalization methods. Furthermore, future studies need to address the variance in biological replicates of wastewater.

In the context of Industry 4.0, construction processes are shifting towards automation by implementing additive manufacturing (AM) of concrete structures, also referred to as concrete printing. Adapting concrete as a printing material entails complex material–process interactions between concrete and manufacturing processes that require specialized data modeling. However, data modeling for the AM of concrete structures has not kept up with concrete printing research and development. Aiming to enhance data modeling for the AM of concrete structures, this paper proposes a semantic modeling approach, referred to as “printing information modeling” (PIM). The PIM approach defines input parameters and material–process interaction in a generic printing information model for designing and planning concrete printing projects. Exchange requirements for concrete printing are identified and evaluated, serving as the basis for the printing information model. The printing information model, as a semantic (or meta) model, is conceptualized using object-oriented modeling concepts, formalized as an ontology, verified as an instantiable semantic model, and validated with a software tool developed as a plug-in for BIM platforms. As a result, a printing information model is developed to serve as a generally valid semantic model for the AM of concrete structures and has the potential to improve data modeling concepts currently deployed for concrete printing.

With the advancements in information, communication, and sensing technologies, structural health monitoring (SHM) has matured into a substantial pillar of infrastructure maintenance. In particular, wireless sensor networks have gradually been incorporated into SHM, leveraging new opportunities towards reduced installation efforts and enhanced flexibility and scalability, as compared to cable-based SHM systems. However, wireless sensor nodes are installed at fixed locations and need to be employed at high density to reliably monitor large infrastructure, which may cause high installation costs. Furthermore, the limited power autonomy of wireless sensor networks, installed at fixed locations for unattended long-term operation, still represents a significant constraint when deploying stationary wireless sensor nodes for SHM. To resolve the critical constraints stemming from costly high-density deployment and limited power autonomy, a mobile structural health monitoring concept based on legged robots is proposed in the study reported in this paper. The study explores the accuracy and cost-efficiency of deploying legged robots in dense measurement setups for wireless SHM of civil infrastructure, aiming to gain insights into the advantages of mobile wireless sensor nodes in general and of legged robots in particular, in terms of obtaining rich information on the structural condition. As is shown in this paper, the legged robots, as compared to stationary wireless sensor nodes, require a smaller number of nodes to be deployed in civil infrastructure to achieve rich sensor information, entailing more cost-efficient, yet accurate, SHM. In conclusion, this study represents a first step towards autonomous robotic fleets advancing structural health monitoring.

Modern civil engineering structures, instrumented with Internet-of-Things-enabled smart sensors and actuators, are considered cyber-physical systems that integrate physical processes with computational and communication elements. This short communication aims to portray a milestone in the field of monitoring and inspection of civil infrastructure, collaboratively conducted by autonomous, robotic devices orchestrated in robotic fleets. It is expected that robot-based civil infrastructure assessment will revolutionize structural maintenance of the deteriorating building stock, which is increasingly exacerbated by the effects of climate change and develops into a major societal challenge.

Für die Kurzstudie zur Lokalisierung von mobilen Robotersystemen wurden Prozesse und Technologien untersucht, die in Zukunft vermehrt im Hochbau zum Einsatz kommen. Die praxisnahen Analysen zeigten nicht nur unterschiedliche Erkenntnisse zu verschiedenen Lasermessgeräten, auch konnten Einsichten zur Präzision Verortung von mobilen Robotern gewonnen werden. Durch die Untersuchung der Sensortechnologien war es zudem möglich, Unterschiede zu Lokalisierungsgenauigkeiten festzuhalten. Da Laserscanner zukünftig häufiger in Planung, Ausführung und Betrieb von Gebäuden eingesetzt werden, liefert die Kurzstudie richtungsweisende Erkenntnisse zur Positionierung beweglicher Elemente., For the case study on the localization of mobile robot systems, processes and technologies, which will increasingly be used in the construction engineering, have been examined. The practice-oriented analysis has revealed findings on different laser measuring devices and insights about aspects of precision and localization of mobile robots. By testing the sensor technologies, differences in localization accuracy have been determined. Since laser scanners will increasingly be used in the planning, execution, and operation phases of buildings, the case study provides essential insights into positioning of mobile elements., Behörde für Stadtentwicklung und Wohnen, Hamburg

Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously., Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously., Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Digitales und Verkehr, Bundesministerium für Bildung und Forschung (BMBF)

Jüngste Phänomene wie Urbanisierung, Klimawandel und Digitalisierung beeinflussen die Art und Weise, wie wir heute und in Zukunft wohnen. Gebäude unter sozialen, ökologischen und digitalen Gesichtspunkten zu konzipieren, ist daher Teil der stetigen gesellschaftlichen Herausforderung, angemessenen Wohnraum bereitzustellen. Diese Forschungsarbeit zielt darauf ab, eine Verbindung zwischen diesen Anforderungen zu schlagen und herauszufinden, wie in der Praxis zukunftsfähiges Wohnen aussehen kann. Eine Methode zur Gebäudebeurteilung wird vorgestellt, anhand der zunächst soziale und ökologische Gebäudeanforderungen evaluiert werden. Darüber hinaus wird untersucht, wie ein Smart Building umzusetzen ist, um eine menschorientierte, umweltfreundliche und zukunftsorientierte Gestaltung zu verwirklichen. Zunächst werden die Verankerung von angemessenem Wohnen als Menschenrecht sowie verschiedene Probleme und zeitgenössische Entwicklungen in Bezug auf Wohnraum beleuchtet. Gemeinsam mit einer Analyse der enormen Umweltauswirkungen der Bauindustrie werden wesentliche Anforderungen an die Gestaltung von Wohngebäuden aus sozialer und ökologischer Perspektive ermittelt und mit gängigen Gebäudebewertungssystemen verglichen. Auf der Grundlage dieser Erkenntnisse wird ein praxisorientiertes Gebäudebeispiel unter Verwendung von Building Information Modeling modelliert und mithilfe des EU-Berichtsrahmens Level(s) sowie ausgewählten Life Cycle Assessment-Tools optimiert. Das Planungsergebnis ist ein Mehrfamilienhaus aus überwiegend biobasierten Materialien mit zwölf Wohneinheiten und großzügig gestalteten Gemeinschaftsflächen. Zusätzlich ist ein intelligentes Gebäudesystem mit Berücksichtigung von Datensicherheit und -schutz unter Verwendung einer Internet of Things-Fog-Cloud-Architektur integriert. Die Nutzung von smarten Geräten verbessert die vorher festgelegten Gebäudeanforderungen und erlaubt eine dynamische Reaktion auf verschiedene Zukunftsszenarien, wie extreme Wetterereignisse und, Recent phenomena like urbanization, climate change and digitalization are influencing the way we dwell today and in the future. Designing buildings from a social, environmental and digital perspective is therefore part of the societal challenge of providing adequate housing. This research aims to bridge the gap between these design objectives and to find out what a future-proof residential building can look like in practice. A building assessment methodology is presented that first evaluates social and environmental building requirements. Additionally, it is investigated how a smart building can be implemented to realize a human-centered, ecological and future-oriented design. First, the enshrinement of adequate housing as a human right, possible problems and contemporary developments related to housing are elaborated. Together with an analysis of the enormous environmental impact of the construction industry, essential requirements for the design of residential buildings from a social and environmental perspective are identified and compared with common building sustainability assessment methods. Based on these findings, a practical building example is modeled using Building Information Modeling and optimized by means of the EU Level(s) framework and selected life cycle assessment tools. The design result is an apartment building made of predominantly biobased materials with twelve residential units and spacious common areas. In addition, a smart building system is integrated with consideration of data security and protection using an Internet of Things-fog-cloud architecture. The use of smart devices enhances the predetermined objectives for building design and allows for a dynamic reaction to various future scenarios, such as extreme weather events and pandemics, for the benefit of the residents. The findings of this research provide a guideline for addressing social and environmental requirements for building design and highlights the benefits of implementing a