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Giovanni Capellari   Mr.  Graduate Student or Post Graduate 
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Giovanni Capellari published an article in July 2018.
Top co-authors
Stefano Mariani

282 shared publications

Università di Modena e Reggio Emilia, Reggio Emilia, Italy ()

Eleni Chatzi

100 shared publications

Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland

V Papadopoulos

62 shared publications

Saeed Eftekhar Azam

25 shared publications

Department of Mechanical Engineering, University of Thessaly, Volos, Greece

Francesco Caimmi

17 shared publications

Dept. of Chem., Mater. & Chem. Eng., Politec. di Milano, Milan, Italy

13
Publications
38
Reads
7
Downloads
6
Citations
Publication Record
Distribution of Articles published per year 
(2015 - 2018)
Publications See all
Article 0 Reads 0 Citations Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 06 July 2018
Sensors, doi: 10.3390/s18072174
DOI See at publisher website
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Structural 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.
Article 2 Reads 3 Citations Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 01 June 2018
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, doi: 10.1061/ajrua6.0000966
DOI See at publisher website
Article 2 Reads 0 Citations Health Monitoring of Composite Structures via MEMS Sensor Networks: Numerical and Experimental Results Stefano Mariani, Giovanni Capellari, Francesco Caimmi, Matte... Published: 04 December 2017
Proceedings, doi: 10.3390/proceedings1080749
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Laminated composites often develop hidden damages, e.g., delamination. Such events can be effectively sensed through embedded structural health monitoring (SHM) systems, taking advantage of the interlaminar regions to place sensors; experimental campaigns proved that this approach may turn out to increase the sensitivity to small defects and reduce the remaining lifetime of the structure. In former studies, we proposed the adoption of a surface-mounted SHM system based on (inertial) MEMS sensors, which has the advantages of low cost and of suppressing the mentioned effects on lightweight structures. On the other hand, the relatively low accuracy of MEMS sensors may hinder reliable monitoring of the system state; this can be overcome through redundancy and an efficient sensor placement. An automatic approach is presented to define the optimal topology of a network featuring a limited number of sensors, wherein the extent and location of stiffness degradation due to damage are assumed to be unknown. The goal of the optimization procedure is to maximize the overall sensitivity to damage of the measurements collected through the whole SHM system. The method has been implemented in a multi-scale frame, to efficiently handle sensors, damaged regions and structural components of different sizes. Although based on deterministic modeling, results are provided to show how measurement noise can be dealt with; a comparison with a stochastic approach based on Bayesian experimental design is provided too. Experimental data collected by testing composite specimens and panels are finally discussed, to assess the identifiability of damage through the collected (noisy) measurements.
CONFERENCE-ARTICLE 10 Reads 1 Citation Cost-benefit optimization of sensor networks for SHM applications Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 14 November 2017
Proceedings, doi: 10.3390/ecsa-4-04891
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Structural 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.

Article 3 Reads 0 Citations A Multiscale Approach to the Smart Deployment of Micro-Sensors over Lightweight Structures Giovanni Capellari, Francesco Caimmi, Matteo Bruggi, Stefano... Published: 15 July 2017
Sensors, doi: 10.3390/s17071632
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A topology optimization approach has been recently proposed to maximize the sensitivity to damage of measurements, collected through a network of sensors to be deployed over thin plates for structural health monitoring purposes. Within such a frame, damage is meant as a change in the structural health characterized by a reduction of relevant stiffness and load-carrying properties. The sensitivity to a damage of unknown amplitude and location is computed by comparing the response to the external actions of the healthy structure and of a set of auxiliary damaged structures, each one featuring reduced mechanical properties in a small region only. The topology optimization scheme has been devised to properly account for the information coming from all of the sensors to be placed on the structure and for damage depending on its location. In this work, we extend the approach within a multiscale frame to account for three different length scales: a macroscopic one, linked to the dimensions of the whole structure to be monitored; a mesoscopic one, linked to the characteristic size of the damaged region; a microscopic one, linked to the size of inertial microelectromechanical systems (MEMS) to be used within a marginally-invasive health monitoring system. Results are provided for a square plate and for a section of fuselage with stiffeners, to show how the micro-sensors have to be deployed to maximize the capability to detect a damage, to assess the sensitivity of the results to the measurement noise and to also discuss the speedup in designing the network topology against a standard single-scale approach.
Conference 2 Reads 0 Citations PARAMETER IDENTIFIABILITY THROUGH INFORMATION THEORY Giovanni Capellari, Eleni Chatzi, Stefano Mariani, Manolis P... Published: 01 January 2017
1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering, doi: 10.7712/120217.5376.17179
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