Data Fusion to Improve the Concrete Diagnosis

  • V. Garnier
  • M. A. Ploix
  • D. Breysse
Conference paper
Part of the RILEM Bookseries book series (RILEM, volume 6)


Numerous non-destructive testing (NDT) methods are used for concrete structures to obtain relevant data about material properties and damage states for reliable condition assessment. Whether the objective is to determine physical properties such as the porosity and the water saturation rate, or mechanical properties such as the elastic modulus or the compressive strength, sensitivity of NDT techniques to many characteristics of the material and its environment is a commonly encountered problem. Thus, accurate and reliable information is often difficult to extract due to the high level of uncertainty involved. Complementary use of different NDT methods for coherent combination of information obtained from each method is a sensible strategy to improve evaluation. The data fusion methodology presented in this paper makes use of the complementary data obtained from different non-destructive or destructive techniques to improve diagnosis reliability. In the case of imprecise and uncertain data, an assessment can still be made with a quantitative measure of the uncertainty involved. The methodology is based on the possibility theory and allows the selection of the best combination of data and techniques to evaluate the material. Applications of the methodology are presented and the results are discussed. Results show good agreement between estimations by data fusion and measured values. Also shown by the results is that the selection of complementary techniques is essential for a better estimation of indicators and improved diagnosis.


Compressive strength Data fusion Diagnosis Quality 


  1. 1.
    Dromigny-Badin A., Rossato S. & Zhu.M., (1997), Traitement du Signa,l vol.14, p 499–510MATHGoogle Scholar
  2. 2.
    Kaftandjian V., Zhu Y.M., Dupuis O. & Babot D., (2005), IEEE Transactions on Instrumentation and Measurement, p 1968–1977Google Scholar
  3. 3.
    Gros X.E., Bousigue J. & Takahashi K., (1999), NDT&E International, vol. 32, p 283–292CrossRefGoogle Scholar
  4. 4.
    Kohl C. & Streicher D., (2006), Cement & Concrete Composites, vol. 28, p 402–413CrossRefGoogle Scholar
  5. 5.
    Bloch I., (1996), Pattern Recognition Letters, vol 17, p 905–919CrossRefGoogle Scholar
  6. 6.
    Moysan J., Durcher A., Gueudre C. & Corneloup G., (2007), NDT&E International, vol. 40, p 478–485CrossRefGoogle Scholar
  7. 7.
    Horn D., (2006), Proceedings, European Congress of Non Destructive Testing BerlinGoogle Scholar
  8. 8.
    Maierhoffer C., Zacher G., Kohl C. & Wostmann J., (2008), Journal of Nondestructive Evaluation, vol. 27, p 47–57CrossRefGoogle Scholar
  9. 9.
    Lmdc-Senso, (2009) Stratégie d’évaluation non destructive pour la surveillance des ouvrages en béton, final report, 274 p.Google Scholar
  10. 10.
    Zadeh L.A., (1999), Fuzzy Sets and Systems, vol. 100 Suppl. p 9–34MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bouchon-Meunier B., Marsala C., (2003), Logique Floue, Principes, Aide à la Décision, Hermes – LavoisierGoogle Scholar
  12. 12.
    Bloch I., (2003) Fusion d’informations en traitement du signal et des images, Hermes – LavoisierGoogle Scholar
  13. 13.
    Ploix M.A., Garnier V., Breysse D. & Moysan J., (2011), NDE data fusion to improve the evaluation of concrete structures, NDT&E International, vol. 44, p 442–448MATHGoogle Scholar
  14. 14.
    Delmotte F., (2000), Traitement du Signal, vol. 17, p 299–311Google Scholar

Copyright information

© RILEM 2013

Authors and Affiliations

  1. 1.Laboratory of Mechanics and Acoustics - LCND team - Aix Marseille UniversityAix en Provence, Cedex 01France
  2. 2.Laboratoire de Caractérisation Non DestructiveUniversité de la MéditerranéeAix en Provence, Cedex 01France
  3. 3.12MUniversité de Bordeaux 1Talence, CedexFrance

Personalised recommendations