Non-destructive indentation tests are more and more frequently employed for the mechanical characterization of structural metals. Three kinds of experimental data sets as inputs to inverse analyses for parameter identification are comparatively examined in this article: (A) indentation curve, namely the relationship\ud between penetration of the indenter tip versus the force applied on it; (B) both this curve and imprint geometry; (C) imprint profile only. The comparisons are based on two different parameter identification procedures. The novel information\ud source (C) turns out to be promising and advantageous in practical industrial applications for innovative diagnostic analysis methods centred on indentation

In this paper an experimental-numerica! methodology is proposed for the in situ assessment of possibly deteriorated elastic properties of dam concrete and for the estimation of the local stress state in a concrete dam. The methodology described herein consists of the following operative stages: excavation of two parallel small holes (instead of the single one generally employed for rock testing); measurements of diameter variations in both holes by dilatometers; combination of experimental tests with computer simulations and inverse analyses for the parameter identifìcation by means of artifìcial neural networks. Numerica] validation tests of this parameter identification methodology are presented and its novelties and potentialities are discussed

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In several existing dams alcali-silica reaction (ASR) during several decades of service life, or diffused micro-cracking (due to concrete ageing and/or past extreme loads, such as earthquakes) give rise to deterioration of concrete stiffness and to correlated reduction of its strength. An inverse methodology is presented herein apt to identify damage in concrete dams on the basis of hydrostatic loading, measurements by traditional monitoring instruments, such as pendulums and collimators, and artificial neural networks trained by means of finite-element simulations. The arch-gravity dam referred to in this study is sub-divided into homogeneous zones, to which a constant Young modulus is attributed as unknown parameter which quantifies possible damage. These elastic moduli are estimated on the basis of pseudo-experimental data and identification procedures. After a suitable 'training' process, artificial neural networks (ANNs) are employed for numerical solutions of the inverse problem, and their potentialities and limitations are examined to the present purposes. In particular, they turn out to be robust and practically useful in the presence of information which are scarce quantitatively (few available measurements) and/or qualitatively (large noise-to-signal ratio).

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