Sensor placement driven by a model order reduction (MOR) reasoning

  • Received : 2013.09.15
  • Accepted : 2014.01.10
  • Published : 2014.03.25


Given a body undergoing a stress-strain status as consequence of external excitations, sensors can be deployed on the accessible lateral surface of the body. The sensor readings are regarded as input of a numerical model of reduced order (i.e., the number of sensors is lower than the number of the state variables the full model would require). The goal is to locate the sensors in such a way to minimize the deviations from the response of the true (full) model. One adopts either accelerometers as sensors or devices reading relative displacements. Two applications are studied: a plane frame is first investigated; the focus is eventually on a 3D body.


  1. Borzi, A. and Schulz, V. (2011), Computational optimization of systems governed by partial differential equations, SIAM, Philadelphia, (ISBN 978-1-611972-04-7)
  2. Calberg, K., Bou, Mosleh and Farhat, C. (2011), "Efficient non-linear model reduction via a least squares Petrov-Galerkin projection and compressive tensor approximations", Int. J. Numer. Meth. Eng., 86(2),155-181.
  3. Casciati, F., Casciati, S., Faravelli L. and Franchinotti, M. (2012), "Model order reduction vs. structural control", Proceedings of the 5th EACS, Genoa, July.
  4. Casciati, S. (2008), "Stiffness identification and damage localization via differential evolution algorithms ", Struct. Control Health Monit., 15(3), 463-449.
  5. Casciati, S, and Borja, R.L. (2004), "Dynamic FE analysis of south memnon colossus including 3D soil-foundation-structure interaction" , Comput. Struct., 82(20-21), 1719-1736.
  6. Casciati, S. and Faravelli, L. (2014), "Quantity vs. quality in the Model Order Reduction (MOR) of a linear system", Smart Struct. Syst.,13 (1), 99-109.
  7. MSC (2013), Marc 2013 User's Guide ,
  8. Ohtori, Y., Christenson, R.E. and Spencer, B.F. (2004), "Benchmark control problems for seismically excited nonlinear buildings", J. Eng. Mech. - ASCE, 130, 366-385.
  9. Papadimitriou C. (2004), "Optimal sensor placement methodology for parametric identification of structural systems", J. Sound Vib., 278(4), 923-947
  10. Schilders, W.H.A., van der Vorst, H.A. and Rommes, J. (2008), Model Order Reduction : Theory, Research Aspects and Applications, Springer, Berlin.
  11. Yang , X.S. (2008), Nature-inspired metaheuristic algorithms, Luniver Press.

Cited by

  1. Bayesian dynamic linear models for structural health monitoring vol.24, pp.12, 2017,
  2. Optimal sensor placement methodology for uncertainty reduction in the assessment of structural condition vol.24, pp.6, 2017,
  3. Low-cost simulation using model order reduction in structural health monitoring: Application of balanced proper orthogonal decomposition vol.24, pp.11, 2017,
  4. Dynamic transient analysis of systems with material nonlinearity: a model order reduction approach vol.18, pp.1, 2016,
  5. Building structural health monitoring using dense and sparse topology wireless sensor network vol.16, pp.4, 2015,
  6. Energy-aware wireless sensor placement in structural health monitoring using hybrid discrete firefly algorithm vol.22, pp.4, 2015,
  7. Wireless structural control using multi-step TDMA communication patterning bandwidth allocation vol.24, pp.12, 2017,
  8. Optimum design of tuned mass damper floor system integrated into bending-shear type building based on H∞, H2, and stability maximization criteria vol.22, pp.6, 2015,
  9. Optimal sensor placement for damage detection of bridges subject to ship collision vol.24, pp.9, 2017,
  10. -means clustering: Application in health monitoring of plate using Lamb wave propagation and impedance method vol.25, pp.9, 2018,