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

68 shared publications

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

V Papadopoulos

62 shared publications

Eleni Chatzi

38 shared publications

Associate Professor, Dept. of Civil, Environmental and Geomatic Engineering, Institute of Structural Engineering, Eidgenössische Technische Hochschule Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland

Saeed Eftekhar Azam

11 shared publications

Francesco Caimmi

7 shared publications

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Publication Record
Distribution of Articles published per year 
(2015 - 2018)
Total number of journals
published in
 
8
 
Publications See all
Article 1 Read 1 Citation 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
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Structural 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.
Article 1 Read 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 4 Reads 0 Citations 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 2 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.
Article 1 Read 0 Citations Optimal design of sensor networks for damage detection Giovanni Capellari, Saeed Eftekhar Azam, Eleni Chatzi, Stefa... Published: 01 January 2017
Procedia Engineering, doi: 10.1016/j.proeng.2017.09.115
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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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