1,721,437 research outputs found
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
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
Uncertainty propagation in satellite InSAR data analysis for structural health monitoring
Full text linkISSN:0926-580
STRUCTURAL DAMAGE LOCALIZATION UNDER VARYING ENVIRONMENTAL CONDITIONS
No full textStructural condition assessment by means of structural health monitoring has in recent years evolved into an actionable practice. For diagnosing structural health, a number of damage detection methods have been proposed, relying on vibration response data, for extraction of features that are characteristic of the intact or unsound structure. In this context, environmental variation comprises a severe challenge, since it induces deviations in the measured structural vibration characteristics, often masking the changes induced by damage. This work offers a remedy to this issue by adoption a Principal Component Analysis (PCA) based approach to account for variations induced from environmental condition variation and separate these from contributions corresponding to damage. Beyond mere detection, the proposed framework offers the possibility for localizing damage
Influence of post-processing methods on bond-slip behavior of nonlinear Fe-SMA lap-shear joints
No full textPrestressed bonded strengthening for structures employing iron-based shape memory alloy (Fe-SMA) has been proven promising. Analyzing adhesively bonded joints necessitates a thorough understanding of the bond-slip behavior. However, when examining the bond-slip behavior of Fe-SMA-to-steel joints comprising nonlinear adhesives, the “forward” and “backward” post-processing methods, representing the current state-of-the-art, produce a trilinear and a trapezoidal bond-slip pattern, respectively, which is inconsistent. To address this inconsistency, the current study investigates the bond behavior of Fe-SMA-to-steel joints, with a particular focus on the bond-slip behavior. Two finite element (FE) joints, one featuring a linear adhesive and the other comprising a nonlinear adhesive, are modeled and compared against physical tests from literature. The “forward” and “backward” processing methods are used to analyze the bond behavior of the two FE joints. Eventually, the aforementioned inconsistency is resolved; a triangular and a trapezoidal bond-slip pattern are characterized for Fe-SMA-to-steel lap-shear joints with linear and nonlinear adhesives, respectively. The trilinear bond-slip behavior is concluded as a result of error accumulation and propagation during the “forward” processing. A hybrid post-processing method, which takes the advantages of both the “forward” and “backward” processing methods, is further proposed for inferring the full-range behavior; the resulting experimental behaviors closely align with simulations using a trapezoidal bond-slip model as input. A comparison against carbon fiber reinforced polymer (CFRP) lap-shear joints demonstrates similar bond-slip characteristics between Fe-SMA and CFRP lap-shear joints
System Identification at the Extreme Edge for Network Load Reduction in Vibration-based Monitoring
Full text linkMechanical complexity, wide dimensions and big data volume may hamper the implementation of Internet–of–Things (IoT)–enabled Structural Health Monitoring (SHM) systems. In particular, one of the most important challenges is the reduction of the data payload to be transmitted over the monitoring network. Addressing the problem in the context of vibration–based SHM, this work explores System Identification (SysId) as an innovative strategy for data compression at the extreme edge. Indeed, SysId is a signal processing technique aiming at finding a very reduced (i.e., less then one tenth of the total signal length) set of meaningful parameters, which can provide an alternative, but yet completely equivalent, frequency characterization of the structure. In the proposed approach, an embedded system–oriented adaptation of the Sequential Tall–Skinny QR decomposition (eS–TSQR) from the dense linear algebra domain has been exploited to tackle both the memory and computational complexity of the involved algorithms. This yielded to the embodiment of input–output and output–only SysId models into a resource constrained device (i.e., an STM32L5 microcontoller unit), targeted on low–power and low–cost SHM applications, proving high effectiveness for the structural assessment of civil and industrial plants. Besides, a cost–benefit analysis is also presented, in which the energy saving brought by SysId running in a sensor–near manner is comprehensively measured against the power consumption due to data transmission, as implied by state–of–the–art communication protocols for IoT. Results demonstrate that SysId is 1.19x and 2.78x less energy demanding (with a payload reduction of 9x and 45x) w.r.t. compressed sensing-driven and compression–free solutions, respectively
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
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
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