Next Article in Journal
Protein-Mediated Precipitation of Calcium Carbonate
Next Article in Special Issue
Monitoring the Damage State of Fiber Reinforced Composites Using an FBG Network for Failure Prediction
Previous Article in Journal
Experimental and Theoretical Analysis of Sound Absorption Properties of Finely Perforated Wooden Panels
Previous Article in Special Issue
Linear and Nonlinear Guided Wave Imaging of Impact Damage in CFRP Using a Probabilistic Approach
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Materials 2016, 9(11), 946; doi:10.3390/ma9110946

A Bayesian Approach for Sensor Optimisation in Impact Identification

Department of Architecture, University of Ferrara, Via Quartieri 8, 44121 Ferrara, Italy
Department of Aeronautics, Imperial College London, London SW7 2AZ, UK
Author to whom correspondence should be addressed.
Academic Editor: Dirk Lehmus
Received: 15 September 2016 / Revised: 25 October 2016 / Accepted: 9 November 2016 / Published: 22 November 2016
(This article belongs to the Special Issue Advances in Structural Health Monitoring for Aerospace Structures)
View Full-Text   |   Download PDF [1213 KB, uploaded 22 November 2016]   |  


This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence. View Full-Text
Keywords: probability of detection; sensor malfunctioning; genetic algorithm; non-linear finite element method; artificial neural network; structural health monitoring probability of detection; sensor malfunctioning; genetic algorithm; non-linear finite element method; artificial neural network; structural health monitoring

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Mallardo, V.; Sharif Khodaei, Z.; Aliabadi, F.M.H. A Bayesian Approach for Sensor Optimisation in Impact Identification. Materials 2016, 9, 946.

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



[Return to top]
Materials EISSN 1996-1944 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top