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Item type:Publication, Access status: without files , Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning TechniquesSmart structures leverage intelligent structural health monitoring (SHM) systems, which comprise sensors and processing units deployed to transform sensor data into decisions. Faulty sensors may compromise the reliability of SHM systems, causing data corruption, data loss, and erroneous judgment of structural conditions. Fault diagnosis (FD) of SHM systems encompasses the detection, isolation, identification, and accommodation of sensor faults, aiming to ensure the reliability of SHM systems. Typically, FD is based on “analytical redundancy,” utilizing correlated sensor data inherent to the SHM system. However, most analytical redundancy FD approaches neglect the fault identification step and are tailored to specific types of sensor data. In this chapter, an analytical redundancy FD approach for SHM systems is presented, coupling methods for processing any type of sensor data and two machine learning (ML) techniques, (i) an ML regression algorithm used for fault detection, fault isolation, and fault accommodation, and (ii) an ML classification algorithm used for fault identification. The FD approach is validated using an artificial neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. Validation is performed through a real-world SHM system in operation at a railway bridge. The results demonstrate the suitability of the FD approach for ensuring reliable SHM systems.Publicationtype: Journal Article182 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Explainable Artificial Intelligence to Advance Structural Health MonitoringIn recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.Publicationtype: Journal Article151 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , BIM-Based Concrete Printing(Springer, 2020-08); ; ; ; Extrusion-based additive manufacturing (AM), or three-dimensional (3D) printing, has matured into a set of advanced methods to automate the construction of large-scale concrete structures, while minimizing cost and material waste. However, current AM data models are inadequate for 3D concrete printing due to insufficient incorporation of information on the relationships between process, material, and geometry, which may cause redundancy, information loss, and inconsistencies. Aiming at improving AM data modeling for concrete printing, this paper proposes a metamodeling approach for AM of concrete structures, referred to as “printing information modeling”, which takes advantage of building information modeling (BIM). As will be shown in this paper, the BIM-based printing information model, serving as a metamodel, incorporates the digital data triplet of process, material, and geometry parameters to generate computer numerical control (CNC) commands that may readily be used for concrete printing. A validation test is performed, which instantiates the printing information model, using a BIM model, for generating CNC commands, enabling optimal digital data exchange from BIM models to concrete printers. As a result of this study, it is demonstrated that printing information modeling adequately defines the information required for AM of concrete structures using a BIM-based approach, showing promising potential to improve current AM data modeling efforts.Publicationtype: Conference Paper142 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Validation of an ultra-low-cost wireless structural health monitoring system for civil infrastructureStructural health monitoring (SHM) is critical to ensure safety of civil infrastructure, such as bridges, railways, or pipelines. However, existing SHM techniques suffer from high investment, installation, and maintenance cost. This paper presents an ultra-low-cost and easy-to-use wireless SHM system for civil infrastructure, featuring real-time sensing, on-board data analysis, energy-efficient wireless communication, and Internet of Things (IoT) connectivity. To validate its performance, the proposed SHM system is installed at Itztal Bridge, Germany, for monitoring and analyzing quasi-static horizontal deck displacements in response to thermal loads. In parallel to the ultra-low-cost SHM system, a traditional tethered SHM system, also installed at the bridge, is used in this study for direct comparison and validation. As a result, the ultra-low-cost wireless SHM system is shown to be easy-to-install and sufficiently accurate for monitoring and analyzing the behavior of civil infrastructure under static loads, serving as a low-cost yet high-performance diagnostic tool with IoT connectivity.Publicationtype: Conference Paper46 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Agent-oriented development of hybrid wind turbine monitoring systems(2010-06); Structural health monitoring is a growing field of research and of paramount practical relevance. Backed by the recent advances in engineering technologies and computing methods, structural health monitoring has the potential to reliably identify damages and deteriorations of structures, at an early stage, resulting in significantly reduced repair and maintenance costs. Considering the emerging field of wind turbine monitoring, this paper presents an innovative hybrid (i.e. software-hardware) monitoring system based on multi-agent technology. Since this technology represents a novel approach towards developing fully decentralized and autonomous monitoring systems, adequate agent-oriented development strategies for structural health monitoring have not yet been proposed. Thus, a suitable development methodology for agent-based monitoring systems is introduced in this paper, which is exemplified by means of a prototype of a wind turbine monitoring system (WTMS). Focusing mainly on the software part of the WTMS prototype, various re-usable system models being part of the methodology are to be illuminated in detail. To this respect, requirements analysis, design, implementation and application of the monitoring system will be demonstrated. Also, an evaluation of the already implemented prototype using actual field data obtained from a wind turbine in operation is carried out.Publicationtype: Conference Paper64