5 results
Now showing1 - 5 of 5
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A system identification method applied to forced vibrations of pylons with time-dependent mass attachments(2022-08); ; ; System identification, either stand-alone or in the context of structural health monitoring (SHM), typically entails extracting information based on the dynamic response of monitored structures. Standard practice utilizes free vibration regimes, recorded during monitoring, to recover the structural key dynamic parameters, i.e., natural frequencies and mode shapes. In this work, we propose a methodology to transform the forced vibration regime, based on Gabor transform, to trace the time evolution of dominant eigenfrequencies of a reference pylon with an additional, time-variable mass attached at the top. The external forcing functions are harmonic ground motions. Two cases are considered, one where the mass decreases to a nearly zero value, starting from a reference value, plus the opposite case where the additional mass increases from zero to the same reference value, which can be substantial, i.e., reaching 20% of the pylon mass. The eigenfrequencies converge at the end of the monitoring time interval, when ground motion ceases, to the standard values computed when the additional mass has a fixed value on the pylon. The methodology presented herein is useful in extracting as much information as possible from dynamic responses and is also of interest in the SHM of typical civil engineering infrastructure ranging from pylons to bridges and buildings.Publicationtype: Conference Paper36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An explainable artificial intelligence approach for damage detection in structural health monitoring(2021-09-09); ; ; Artificial intelligence (AI) has been used in recent years as a novel approach towards damage detection in modern structural health monitoring (SHM) systems. Nevertheless, the so-called “black-box nature” of several AI algorithms has limited the trust of practitioners in using AI for real-world SHM applications. This study proposes an explainable artificial intelligence (XAI) approach for SHM systems, providing the tools required to overcome the lack of trust of practitioners in AI algorithms. A one-class support vector machine for outlier detection is used to identify damage in structural response data. Targeting the need for trust in AI in real-world SHM applications, the Shapley additive explanations (SHAP) XAI method is used to explain features in structural response data indicative of damage. For validation, structural response data from simulations of a pedestrian bridge are used, in which damage may or may not be present. As a result of this study, damage detection is achieved with the one-class SVM algorithm, and explanations of the reasoning behind damage detection in structural response data is demonstrated to be possible with the XAI-SHM approach. It is expected that the approach proposed in this study will serve as a basis for the application of XAI in real-world SHM applications.Publicationtype: Conference Paper137 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automated decision making in structural health monitoring using explainable artificial intelligence(2021-06-30); ; ; ; he need for processing large amounts of data recorded by structural health monitoring (SHM) systems has been fostering interdisciplinary SHM strategies employing artificial intelligence (AI) algorithms for detecting damage. However, the opacity of several AI algorithms hinders their widespread adoption in SHM practice. To enhance the trust of practitioners in AI algorithms, this paper proposes an explainable artificial intelligence (XAI) approach for SHM. The approach builds upon the capabilities of unsupervised learning algorithms for detecting outliers indicative of structural damage in structural response data. Moreover, features in the data governing outlier detection are “explained” to the user, thus ensuring transparency in decision making. The XAI-SHM approach is validated via simulations of a pedestrian bridge that may or may not include damage. The results show that the XAI-SHM approach is capable of distinguishing between damage and random fluctuations of structural properties, while decisions made by the XAI-SHM approach are clearly explained.Publicationtype: Conference Paper113 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Analytical modeling of flexible structures for health monitoring under environmentally induced loadsWireless structural health monitoring (SHM) represents an emerging paradigm in non-destructive testing evaluation, which is regularly performed within the framework of structural maintenance of critical infrastructure. While traditional cable-based SHM strategies have largely relied on centralized structural response data collection and processing, in wireless SHM the sensor nodes essentially operate as stand-alone processing units. Furthermore, the on-board processing capabilities of wireless sensor nodes have been widely exploited for decentralizing SHM tasks, thus avoiding power-consuming wireless transmission of entire structural response data sets. Evidently, on-board processing requires approaches tailored to the limited computational resources of wireless sensor nodes, particularly for computationally intensive, state-of-the-art strategies for SHM that rely on artificial intelligence (AI). Specifically, in model-based SHM/AI strategies, AI algorithms are trained by running the so-called what-if scenarios using numerical models of monitored structures. However, the use of numerical models in a decentralized wireless SHM scheme might be prohibitive from a computational resources point of view. To overcome this limitation, analytical modeling techniques using elastic waveguides are developed here that carry a low computational burden. These are specifically tailored for flexible structures of variable cross section, which comprise typical components of critical infrastructure such as masts, antennas and pylons, and can be used for simulating axial, torsional and flexural vibrations. The accuracy and efficiency of the proposed analytical modeling approach are then demonstrated through comparisons with conventional numerical models based on the finite element method.Publicationtype: Journal Article30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Analytical-numerical modeling of flexible pylons for structural health monitoringThe monitoring of flexible structures such towers, antennas and pylons comprising critical infrastructure for power transmission and telecommunications is necessary in order to ensure their continuous operation. This particular field of engineering comes under the umbrella of structural health monitoring, which in recent years has been given high priority in developed countries. There, the built infrastructure is both extensive, encompassing diverse groups of systems such as energy, telecommunication, utility and transportation networks, while at the same time is ageing rapidly. The goal is to set up monitoring schemes with a stand-alone configuration and a wireless transmission of data to central locations for further processing. Thus, it becomes necessary for the sensors employed to have some structural analysis capabilities that can be used in tandem with their data processing functions, all within an artificial intelligence environment. To this end, we develop analytical models for vibrations in elastic waveguides that model flexible structures of variable cross-section and demonstrate their efficiency and accuracy through comparisons with finite element models.Publicationtype: Conference Paper36