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Stefano Mariani   Dr.  Research or Laboratory Scientist 
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Stefano Mariani published an article in June 2018.
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Top co-authors See all
Nicola Bonora

80 shared publications

TECHDYN Engineering, Via A. Rendano 18, I-00199 Rome, Italy

Robert Wilson

78 shared publications

University of Tennessee SimCenter at Chattanooga, 701 East M.L. King Boulevard, Chattanooga, TN 37403, U.S.A.

Anna Pandolfi

61 shared publications

Politecnico di Milano, Milano, Italy

Aldo Ghisi

52 shared publications

Politecnico di Milano IT Italy

Robin S. Waples

49 shared publications

NOAA Fisheries; Northwest Fisheries Science Center; 2725 Montlake Blvd. East Seattle WA 98112

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Publication Record
Distribution of Articles published per year 
(1970 - 2018)
Total number of journals
published in
 
23
 
Publications See all
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 3 Citations Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters Saeed Eftekhar Azam, Stefano Mariani, Nader Khajeh Ahmad Att... Published: 28 April 2017
Nonlinear Dynamics, doi: 10.1007/s11071-017-3530-1
DOI See at publisher website
CONFERENCE-ARTICLE 2 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.

Article 2 Reads 0 Citations A systematic analysis across North Atlantic countries unveils subtleties in cod product labelling Amanda L. Bréchon, Robert Hanner, Stefano Mariani Published: 01 July 2016
Marine Policy, doi: 10.1016/j.marpol.2016.04.014
DOI See at publisher website
Conference 2 Reads 0 Citations Extraction of thermal Green's function using diffuse fields: a passive approach applied to thermography Margherita Capriotti, Simone Sternini, Francesco Lanza Di Sc... Published: 20 April 2016
Smart Materials and Nondestructive Evaluation for Energy Systems 2015, doi: 10.1117/12.2218998
DOI See at publisher website
Conference 2 Reads 0 Citations Defect detection performance of the UCSD non-contact air-coupled ultrasonic guided wave inspection of rails prototype Stefano Mariani, Thompson V. Nguyen, Simone Sternini, France... Published: 08 April 2016
Smart Materials and Nondestructive Evaluation for Energy Systems 2015, doi: 10.1117/12.2218989
DOI See at publisher website
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