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Item type:Publication, Access status: with files , Implementation and validation of robot-enabled embedded sensors for structural health monitoring(NDT.net, 2024-07-01); ; ; ; Bartholmai, MatthiasIn 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.Publicationtype: Conference Paper7 50 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Frequency-domain synchronization of structural health monitoring data(2024-02-17); ; ; Since the advent of structural health monitoring (SHM), vibration analysis has been one of the most popular strategies for yielding modal information of structures, such as eigenfrequencies and mode shapes. As a result of the relatively high sampling rates adopted in vibration analysis, obtaining accurate modal information requires absolute synchronization of structural response data. Despite the advances in sensing technologies and synchronization protocols, synchronization discrepancies in structural response data may occur due to drifting in internal clocks of independent data acquisition units. Particularly in wireless SHM, in which each wireless sensor node essentially operates as an independent data acquisition unit, mitigating clock drifting has been an area of intensive research. This paper addresses the issue of data synchronization from a physics-based perspective, aiming to add an additional synchronization check at the post-sampling stage. Specifically, a frequency-domain synchronization approach is reported, which builds upon the relationship between time lags in structural response data and the cross spectral density phase angles. Validation tests are conducted using simulations of a multi-degree-of-freedom oscillator and data collected from a full-scale road bridge, demonstrating the capability of the approach to estimate time lags between SHM data even in the presence of non-proportional damping, thus overcoming a common limitation of other synchronization methods. The frequency-domain synchronization approach is intended to complement synchronization protocols in extracting accurate modal information.Publicationtype: Journal Article26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Detection and correction of synchronization-induced errors in operational modal analysisOperational modal analysis (OMA) methods are frequently applied for obtaining information on structural dynamic parameters, such as natural frequencies and mode shapes. While synchronization between acceleration response data sets used in OMA is not a prerequisite for estimating natural frequencies, synchronization discrepancies may lead to non-negligible errors in the estimation of mode shapes. Synchronization discrepancies are predominantly associated with wireless structural health monitoring systems, where each wireless sensor node functions as a separate data acquisition unit (DAQ), and clock synchronization is therefore not intrinsic. However, synchronization discrepancies may also occur in cable-based systems, particularly when more than one DAQ is used. In this paper, a synchronization method for detecting and correcting synchronization-induced errors in OMA is proposed. Unlike existing approaches on removing synchronization-induced errors at the output stage, the method proposed herein yields synchronized structural response data at an intermediate stage of OMA. Specifically, time lags between acceleration response data sets are detected based on estimates of the mode shapes obtained either from preliminary structural analysis or from engineering judgment assuming classical damping. The applicability of the proposed method is verified through simulations of a multi-degree-of-freedom oscillator and validated through ambient vibration field tests on a pedestrian overpass bridge.Publicationtype: Journal Article87 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automated wireless structural health monitoring and control using tuned liquid column dampers(2019-06); ; Current trends in structural design and retrofit frequently dictate artificially modifying the structural behavior in an attempt to target unfavorable loading scenarios expected to compromise the fulfilment of stability and serviceability criteria. Particularly in structures where operational requirements result in adopting unconventional structural systems, usually falling outside the scope of codified regulations and guidelines, adapting the structural behavior by means of structural subsystems has been widespread. Referred to as “structural control”, enhancing the ability of structures to adapt to unfavorable loading conditions encompasses attaching structural subsystems, such as dampers, to the main structure. Moreover, the increasing application of structural health monitoring (SHM) for structural assessment and maintenance has fueled research in coupling SHM with structural control, thus paving the way for developing structural systems capable of autonomously adapting structural properties. In this context, this paper describes a preliminary approach to automated structural health monitoring and control by coupling a wireless SHM system with a tuned liquid column damper (TLCD). The wireless SHM system is equipped with embedded algorithms managing SHM tasks, such as data sampling and data processing, and with actuators able to modify the properties of the TLCD based on any user-defined control algorithm. The proposed approach serves as a first step towards fully automated structural health monitoring and control and is validated through laboratory tests on a four-story shear frame structure equipped with a wireless SHM system and a TLCD on the top story.Publicationtype: Conference Paper54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Semantic description of structural health monitoring algorithms using building information modelingInformation exchange among project stakeholders as part of structural life-cycle management has been gaining increasing interest in civil engineering. An integral part of structural life-cycle management is the operation and maintenance phase of structures, which is frequently associated with structural health monitoring (SHM). SHM has emerged as a novel methodology enabling the assessment of structural conditions by extracting information from structural response data and environmental data collected by sensors attached to structures. Representing a paradigm for exchanging information among stakeholders for structural life-cycle management, conventional building information, such as geometry, material and cost, is structured in so-called “building information models”. These models are defined within building information modeling (BIM) standards, such as the Industry Foundation Classes (IFC). Furthermore, in recent research efforts, IFC-compliant descriptions of “monitoring-related information”, i.e. information on SHM systems, have been reported. However, semantic descriptions of algorithms employed for SHM (“SHM-related algorithms”) have not yet received adequate research attention. This paper introduces a semantic description approach for modeling and integrating SHM-related algorithms into IFC-based building information models. Specifically, this study focuses on algorithms embedded into wireless sensor nodes for automatically processing SHM data on board. The semantic description approach is validated by describing a wireless SHM system installed on a laboratory test structure designed and implemented with an embedded algorithm (fast Fourier transform). The expected outcome of this study is essentially an extension to the current IFC schema enabling the description of SHM-related algorithms in conjunction with SHM systems and structures to be monitored.Publicationtype: Conference Paper64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A hybrid system identification methodology for wireless structural health monitoring systems based on dynamic substructuring(2016-03); System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. The entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.Publicationtype: Conference Paper31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Machine learning techniques for structural health monitoring(2016); ; Data-driven approaches are particularly useful for computer-supported assessment of civil engineering structures (i) if large quantities of sensor data are available, (ii) if the physical characteristics of the structure are complex to model (or even unknown), or (iii) if the computational efforts are to be reduced. This paper, upon a classificational review of machine learning techniques in structural health monitoring, reports on an embedded machine learning approach for decentralized, autonomous sensor fault detection in wireless sensor networks, facilitating reliable and accurate structural health monitoring. Based on decentralized artificial neural networks, the embedded machine learning approach is applied to perform autonomous detection of sensor faults injected in the acceleration response data collected by a prototype structural health monitoring system. As demonstrated through laboratory tests, the results highlight the ability of the embedded machine learning approach to autonomously detect sensor faults in a decentralized manner, thus enhancing the reliability and accuracy of structural health monitoring systems.Publicationtype: Conference Paper57