Flat-jack tests have been employed for decades for the assessment of stresses and Young moduli in possibly deteriorated concrete dams and masonry structures. We propose a procedure for such tests that includes several innovations: identification of Young moduli and shear modulus in the presence of orthotropy, of pre-existing normal and shear stresses, and of tensile and compressive strength and fracture energy; use of full-field displacement measurements by digital image correlation (instead of extensometers); computer simulations performed once-and-for-all and productive of results which are subsequently processed out by proper orthogonal decomposition and its truncation; and identification of parameters in situ, soon after the tests, by portable computer with software able to perform inverse analyses by mathematical tools newly introduced into this context. The proposed procedure is validated by means of pseudoexperimental numerical exercises, by employing comparatively, as central computational tools, artificial neural networks and a trust region algorithm implying only first-order derivatives (with respect to the sought parameters) of the discrepancy function to minimize.

Diagnosis of concrete dams by flat-jack tests and inverse analyses based on proper orthogonal decomposition

MAIER, GIULIO;NOVATI, GIORGIO
2011

Abstract

Flat-jack tests have been employed for decades for the assessment of stresses and Young moduli in possibly deteriorated concrete dams and masonry structures. We propose a procedure for such tests that includes several innovations: identification of Young moduli and shear modulus in the presence of orthotropy, of pre-existing normal and shear stresses, and of tensile and compressive strength and fracture energy; use of full-field displacement measurements by digital image correlation (instead of extensometers); computer simulations performed once-and-for-all and productive of results which are subsequently processed out by proper orthogonal decomposition and its truncation; and identification of parameters in situ, soon after the tests, by portable computer with software able to perform inverse analyses by mathematical tools newly introduced into this context. The proposed procedure is validated by means of pseudoexperimental numerical exercises, by employing comparatively, as central computational tools, artificial neural networks and a trust region algorithm implying only first-order derivatives (with respect to the sought parameters) of the discrepancy function to minimize.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/608306
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