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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

78 shared publications

Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. da Vinci 32, 20133 Milano, Italy

Eleni N. Chatzi

56 shared publications

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

Matteo Bruggi

49 shared publications

Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy

Saeed Eftekhar Azam

20 shared publications

Department of Civil Engineering; University of Nebraska-Lincoln; Lincoln Nebraska United States

Francesco Caimmi

13 shared publications

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

Publication Record
Distribution of Articles published per year 
(2015 - 2018)
Total number of journals
published in
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 ABS Show/hide abstract
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.
CONFERENCE-ARTICLE 15 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
DOI See at publisher website ABS Show/hide abstract

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.

CONFERENCE-ARTICLE 3 Reads 1 Citation Optimal sensor placement through Bayesian experimental design: effect of measurement error and number of sensors Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 14 November 2016
Proceedings, doi: 10.3390/ecsa-3-D006
DOI See at publisher website ABS Show/hide abstract

Sensors networks for the health monitoring of structural systems have to be designed to achieve both accurate estimations of the relevant mechanical parameters and low cost of the experimental equipment. 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.

CONFERENCE-ARTICLE 4 Reads 0 Citations A multiscale approach to the smart deployment of micro-sensors over flexible plates Giovanni Capellari, Francesco Caimmi, Matteo Bruggi, Stefano... Published: 14 November 2016
Proceedings, doi: 10.3390/ecsa-3-D005
DOI See at publisher website ABS Show/hide abstract

In former studies, we proposed a topology optimization approach to maximize the sensitivity to damage of measurements collected through a network of sensors deployed over flexible, thin plates. Within such frame, a damage must be intended as a change of the structural health characterized by a reduction of the relevant load-carrying capacity. By properly comparing the response of the healthy, undamaged structure and the response of the damaged one, independently of the location of the source of damage, a procedure to optimally deploy a given set of sensors was provided.

In this work we extend the aforementioned approach within a multi-scale frame, to account for (at least) three different length-scales: a macroscopic one, linked to the dimensions of the structure to be monitored; a mesoscopic one, linked to the characteristic size of the damaged region(s); 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 simply supported along its border, to show how the micro-sensors are to be deployed to maximize the sensitivity of measurements to damage, and to also discuss the speedup obtained with the proposed multiscale approach in comparison with a standard single-scale one.

Article 4 Reads 7 Citations Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering Giovanni Capellari, Saeed Eftekhar Azam, Stefano Mariani Published: 22 December 2015
Sensors, doi: 10.3390/s16010002
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.