1,619 research outputs found
Aventa wind turbine in Winterthur (Switzerland)
No full textThe wind turbine’s rated power is 6.5kW, the rotor diameter is 12.9m and the hub height is 18m. The tower is a tubular steel-reinforced concrete structure, and the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.
The measurements/instrumentation setup, type and layout is provided in the pdf files.
Data available upon request, please contact:
Prof. Dr. Eleni Chatzi ([email protected])
Dr. Imad Abdallah ([email protected])
For further details or questions, please contact:
Prof. Dr. Eleni Chatzi
Chair of Structural Mechanics & Monitoring
ETH Zürich
http://www.chatzi.ibk.ethz.ch
Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks
Full text linkStructural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared
Cost-Benefit Optimization of Sensor Networks for SHM Applications
Full text linkStructural health monitoring (SHM) is aimed to obtain information about the structural integrity of a system, e.g., via the estimation of its mechanical properties through observations collected with a network of sensors. In the present work, we provide a method to optimally design sensor networks in terms of spatial configuration, number and accuracy of sensors. The utility of the sensor network is quantified through the expected Shannon information gain of the measurements with respect to the parameters to be estimated. At assigned number of sensors to be deployed over the structure, the optimal sensor placement problem is ruled by the objective function computed and maximized by combining surrogate models and stochastic optimization algorithms. For a general case, two formulations are introduced and compared: (i) the maximization of the information obtained through the measurements, given the appropriate constraints (i.e., identifiability, technological and budgetary ones); (ii) the maximization of the utility efficiency, defined as the ratio between the information provided by the sensor network and its cost. The method is applied to a large-scale structural problem, and the outcomes of the two different approaches are discussed
Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design
No full textStructural health monitoring (SHM) may be exploited to estimate the mechanical properties of existing structures and check for
potential damage. Among commonly used methodologies for property characterization, the Bayesian approach holds the lead because it is
endowed with the particular advantage of quantifying associated uncertainties. These uncertainties arise owing to diverse factors including
(1) sensor accuracy and positioning, (2) environmental influences, and (3) modeling errors. In minimizing the influence of sensor-related
uncertainties, an optimal design may be adopted for the SHM campaign to maximize the information content of the measurements. Here, a
procedure based on Bayesian experimental design is proposed to quantify the expected utility of the sensor network. The positions of the used
sensors are selected in a way that maximizes the Shannon information gain between the prior and posterior probability distributions of the
parameters to be estimated. In order to numerically solve the resulting optimization problem, surrogate models based on polynomial chaos
expansion (PCE) and stochastic optimization methods are used. The use of surrogates allows one to reduce the computational cost of the
associated model runs. The method is applied to a large-scale example, namely the Pirelli Tower in Milan
Optimal sensor placement through Bayesian experimental design: effect of measurement error and number of sensors
Full text linkSensors networks for the health monitoring of structural systems ought to be designed to
render both accurate estimations of the relevant mechanical parameters and an affordable
experimental setup. Therefore, the number, type and location of the sensors have to be chosen so
that the uncertainties related to the estimated health are minimized. Several deterministic methods
based on the sensitivity of measures with respect to the parameters to be tuned are widely used.
Despite their low computational cost, these methods do not take into account the uncertainties
related to the measurement process. In former studies, a method based on the maximization of the
information associated with the available measurements has been proposed and the use of
approximate solutions has been extensively discussed. Here we propose a robust numerical
procedure to solve the optimization problem: in order to reduce the computational cost of the
overall procedure, Polynomial Chaos Expansion and a stochastic optimization method are
employed. The method is applied to a flexible plate. First of all, we investigate how the information
changes with the number of sensors; then we analyze the effect of choosing different types of sensors
(with their relevant accuracy) on the information provided by the structural health monitoring
system
Condition assessment of roadway bridges: from performance parameters to performance goals
Full text linkDeterioration of bridges due to ageing and higher demands, induced by increased traffic load, require the development of effective maintenance policies and intervention strategies. Such concern should be aimed at ensuring the required levels of safety, while optimally managing the limited economic resources. This approach requires a transversal advance; from the element level, through the system level, all the way to the network level. At the same time intervention prioritisation based on the importance of the system (bridge) inside the network (e.g. highway), or of the single structural element inside the bridge is dependent. The first step in bridge condition assessment is the verification of safety and reliability requirements that is carried out using the traditional prescriptive (deterministic) approach or the current performance- based (probabilistic) approach. A critical issue for efficient management of infrastructures lies in the available knowledge on condition and performance of bridge asset. This information is obtained using a collection of significant Performance Parameters at one or more of the three levels (element, system, and network). Traditional techniques for estimation of Performance Parameters rely on already established visual inspection. However, a more reliable description of the system performance is obtained through Non-Destructive Testing and Structural Health Monitoring. Condition assessment essentially pertains to the check of compliance with Performance Goals and requires the definition and computation of Performance Indicators. They are calculated directly from Performance Parameters or from physical models calibrated using the Performance Parameters collected on the structure. Paper overviews the steps to bridge condition assessment regarding safety and reliability
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On the use of mode shape curvatures for damage localization under varying environmental conditions
Full text linkA novel damage localization method is introduced in this study, which exploits mode shape curvatures as damage features, while accounting for operational variability. The developed framework operates in an output-only regime,that is, it does not assume availability of records from the influencing environmental/operational quantities but rather from response quantities alone. The introduced tool comprises 3 stages pertaining to training, validation, and diagnostics. During the training stage, a representation of the healthy, or baseline, structural state is acquired over varying operational conditions. A data matrix is formulated, whose individual columns correspond to mode shape curvatures at distinct operational conditions, and principal component analysis (PCA) is applied for extraction of the imprints of separate operational sources on these curvatures. To this end, a residual matrix between the original and the PCA mapped data is formed serving for statistical characterization of each mode. Subsequently, during the validation and diagnostics stages, the mode shape curvature matrices for the currently inspected structural state are assembled and the same PCA mapping is enforced. A typical hypothesis test and a corresponding damage index are then adopted in order to firstly detect damage, and to secondly localize damage, should this exist. The implementation of the proposed method in 2 numerical case studies confirms its effectiveness and the encouraging results suggest further investigation on operating structural systems
Stable 3D XFEM/vector-level sets for non-planar 3D crack propagation and comparison of enrichment schemes
No full textpeer reviewedWe present a three-dimensional (3D) vector level set method coupled to a recently developed stable extended finite element method (XFEM). We further investigate a new enrichment approach for XFEM adopting discontinuous linear enrichment functions in place of the asymptotic near-tip functions. Through the vector level set method, level set values for propagating cracks are obtained via simple geometrical operations, eliminating the need for solution of differential evolution equations. The first XFEM variant ensures optimal convergence rates by means of geometrical enrichment, i.e., the use of enriched elements in a fixed volume around the crack front, without giving rise to conditioning problems. The linear enrichment approach significantly simplifies implementation and reduces the computational cost associated with numerical integration. The two dicretization schemes are tested for different benchmark problems, and their combination to the vector level set method is verified for non-planar crack propagation problems
Condition assessment of bridges using monitoring data
No full textIn the last years, significant interest has been vested in the possibility of using Structural health monitoring (SHM) as a standardized tool in bridge engineering. Structural Health Monitoring is an emerging field in engineering that gathers together several techniques such as structural dynamics, materials, signal processing, or microelectronics. Sensors, data, and simulation tools come together under the umbrella of SHM to offer - at every moment during the life of a structure - a diagnosis of its health and prognosis of its remaining life.
This thesis has been developed within the group of Structural dynamics and monitoring at ETH Zurich, under the supervision of Prof.Dr. Eleni Chatzi. The aim of this thesis has been to try to assess the condition of a bridge, used as case study, in order to detect changes in modal parameters and correlate them with possible types of damage, especially the ones caused by environmental (temperature) and operational (traffic) variability of modal parameters. Furthermore, a performance indicator of the health of a structure, such as fatigue, has been studied in this work in order to relate possible damage accumulation to the environmental and operational condition. For the purposes of these studies, a major bridge infrastructure in Switzerland has been analysed. Additional knowledge and information were essential though, before the intended calculations could be performed.
This study contributes to the development of an analysis process for SHM-based condition assessment for an existing structure, thereby defining further methodologies and strategies for maintenance and repair of bridges. In addition, it provides relevant information for design purposes about the influence of seasonal behaviour of action effects and traffic loads on the stiffness and different fatigue damage throughout a reference period for the long span bridge under investigation, pointing the way to implementing such methods for bridge infrastructure
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