Please login first
Stefano Mariani   Dr.  Research or Laboratory Scientist 
Timeline See timeline
Stefano Mariani published an article in June 2018.
Research Keywords & Expertise
0 A
0 Damage
0 Inspection
Top co-authors See all
Robert Wilson

78 shared publications

Nicola Bonora

77 shared publications

Anna Pandolfi

59 shared publications

Aldo Ghisi

52 shared publications

Politecnico di Milano

Robin S Waples

47 shared publications

Publication Record
Distribution of Articles published per year 
(1970 - 2018)
Total number of journals
published in
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
DOI See at publisher website
ABS Show/hide abstract
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 2 Citations Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters S. Eftekhar Azam, S. Mariani, N. K. A. Attari Published: 28 April 2017
Nonlinear Dynamics, doi: 10.1007/s11071-017-3530-1
DOI See at publisher website
ABS Show/hide abstract
In this paper, an approach based on the synergistic use of proper orthogonal decomposition and Kalman filtering is proposed for the online health monitoring of damaged structures. The reduced-order model of a structure is obtained during an (offline) initial training stage of monitoring; afterward, effective estimations of a possible structural damage are provided online by tracking the evolution in time of stiffness parameters and projection bases handled in the model order reduction procedure. Such tracking is accomplished via two Kalman filters: a first (extended) one to deal with the time evolution of a joint state vector, gathering the reduced-order state and the stiffness terms degraded by damage; a second one to deal with the update of the reduced-order model in case of damage evolution. Both filters exploit the information conveyed by measurements of the structural response to the external excitations. Results are reported for a (pseudo-experimental) benchmark test on an eight-story shear building. Capability and performance of the proposed approach are assessed in terms of tracked variation of the stiffness terms of the reduced-order model, identified damage location and speed-up of the whole health monitoring procedure.
CONFERENCE-ARTICLE 1 Read 0 Citations 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 1 Read 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 1 Read 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
SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, doi: 10.1117/12.2218998
DOI See at publisher website
ABS Show/hide abstract
In the field of non-destructive evaluation, defect detection and visualization can be performed exploiting different techniques relying either on an active or a passive approach. In the following paper the passive technique is investigated due to its numerous advantages and its application to thermography is explored. In previous works, it has been shown that it is possible to reconstruct the Green’s function between any pair of points of a sensing grid by using noise originated from diffuse fields in acoustic environments. The extraction of the Green’s function can be achieved by cross-correlating these random recorded waves. Averaging, filtering and length of the measured signals play an important role in this process. This concept is here applied in an NDE perspective utilizing thermal fluctuations present on structural materials. Temperature variations interacting with thermal properties of the specimen allow for the characterization of the material and its health condition. The exploitation of the thermographic image resolution as a dense grid of sensors constitutes the basic idea underlying passive thermography. Particular attention will be placed on the creation of a proper diffuse thermal field, studying the number, placement and excitation signal of heat sources. Results from numerical simulations will be presented to assess the capabilities and performances of the passive thermal technique devoted to defect detection and imaging of structural components. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Conference 1 Read 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
SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, doi: 10.1117/12.2218989
DOI See at publisher website
ABS Show/hide abstract
The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, paired with a real-time statistical analysis algorithm, has been realized. This system requires a specialized filtering approach based on electrical impedance matching due to the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. The system’s operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. The prototype based on this technology was tested in October 2014 at the Transportation Technology Center (TTC) in Pueblo, Colorado, and again in November 2015 after incorporating changes based on lessons learned. Results from the 2015 field test are discussed in this paper. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.