Next Article in Journal
UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil
Next Article in Special Issue
Graphene-Based Raman Spectroscopy for pH Sensing of X-rays Exposed and Unexposed Culture Media and Cells
Previous Article in Journal
Inverse Finite Element Method for Reconstruction of Deformation in the Gantry Structure of Heavy-Duty Machine Tool Using FBG Sensors
Previous Article in Special Issue
Acoustic Parametric Signal Generation for Underwater Communication
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

1
Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2
ETH Zürich, Institut für Baustatik und Konstruktion Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(7), 2174; https://doi.org/10.3390/s18072174
Received: 11 June 2018 / Revised: 4 July 2018 / Accepted: 4 July 2018 / Published: 6 July 2018
  |  
PDF [831 KB, uploaded 6 July 2018]
  |  

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. View Full-Text
Keywords: structural health monitoring; Bayesian inference; cost–benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction structural health monitoring; Bayesian inference; cost–benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Capellari, G.; Chatzi, E.; Mariani, S. Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks. Sensors 2018, 18, 2174.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top