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Your search keyword '"Bartoli, Ivan"' showing total 244 results
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244 results on '"Bartoli, Ivan"'

6. Virtual Laboratory for Leveraging Technology for Bridges and Constructed Systems

Author

Aktan, Emin, Bartoli, Ivan, Moon, Franklin, Balduccini, Marcello, Sjoblom, Kurt, Kontsos, Antonios, Azari, Hoda, Mazzotti, Matteo, Braley, John, Young, Charles, Ye, Shi, Ellenberg, Andrew, di Prisco, Marco, Series editor, Chen, Sheng-Hong, Series editor, Solari, Giovanni, Series editor, Conte, Joel P., editor, Astroza, Rodrigo, editor, Benzoni, Gianmario, editor, Feltrin, Glauco, editor, Loh, Kenneth J., editor, and Moaveni, Babak, editor

Published
2018
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21. Identification of elastic constants by Bayesian approach

Author

Bartoli, Ivan, di Leo, Antonio, Viola, Erasmo, Velarde, Manuel Garcia, editor, Sayir, Mahir, editor, Schneider, Wilhelm, editor, Schrefler, Bernhard, editor, Bianchi, Giovanni, editor, Tasso, Carlo, editor, Davini, Cesare, editor, and Viola, Erasmo, editor

Published
2003
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23. A low/high frequency combined approach for the identification of mechanical properties of composite structural elements

Author

Bartoli Ivan and Marzaniy Alessandro

Subjects
Dynamic identification, composite plates, Mechanics of engineering. Applied mechanics, TA349-359
Abstract

A combined low/high frequency dynamic identification approach is presented. The proposed hybrid technique compares experimentally extracted dynamic properties of plates such as modal frequencies and guided wave dispersion properties with the correspondent numerically predicted ones. Identification of mechanical properties of the plates is achieved by iteratively adjusting the mechanical properties assumed in finite element and semi-analytical models through minimization of the difference between experimental and predicted dynamic features.

Published
2012
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29. Defect classification in pipes by neural networks using multiple guided ultrasonic wave features extracted after wavelet processing

Author

Rizzo, Piervincenzo, Bartoli, Ivan, Marzani, Alessandro, and Scalea, Francesco Lanza di

Subjects
Wavelet transforms -- Research, Pressure vessels -- Research, Neural networks -- Research, Steel pipe -- Research, Neural network, Engineering and manufacturing industries
Abstract

This paper casts pipe inspection by ultrasonic guided waves in a feature extraction and automatic classification framework. The specific defect under investigation is a small notch cut in an ASTM-A53-F steel pipe at depths ranging from 1% to 17% of the pipe cross-sectional area. A semi-analytical finite element method is first used to model wave propagation in the pipe. In the experiment, reflection measurements are taken and six features are extracted from the discrete wavelet decomposition of the raw signals and from the Hilbert and Fourier transforms of the reconstructed signals. A six-dimensional damage index is then constructed, and it is fed to an artificial neural network that classifies the size and the location of the notch. Overall the wavelet-based multifeature analysis demonstrates good classification performance and robustness against noise and changes in some of the operating parameters. [DOI: 10.1115/1.1990213] Keywords: Pipe Inspection, Guided Waves, Wavelet Transform, Feature Extraction, Neural Networks

Published
2005

34. Structural health monitoring by ultrasonic guided waves

Author

Bartoli, Ivan

Abstract

Guided ultrasonic waves provide a highly efficient method for the non-destructive evaluation (NDE) and the structural health monitoring (SHM) of solids with finite cross-sectional dimensions (waveguides). Compared to the widely used ultrasonic bulk waves, guided waves provide larger monitoring ranges and the complete coverage of the waveguide cross-section. Compared to global vibrations, guided waves offer increased sensitivity to smaller defects due to the smaller wavelengths involved. These advantages can be fully exploited only once the complexities of guided wave propagation (multimode, dispersion, frequency-dependent attenuation) are unveiled and managed for the given test structure. This doctoral dissertation is aimed at developing a Semi-Analytical Finite Element (SAFE) method for modeling wave propagation in waveguides of arbitrary cross-section. The method requires the finite element discretization of the cross- section of the waveguide, and assumes harmonic motion along the wave propagation direction. The general SAFE technique was extended to account for viscoelastic material damping by allowing for complex stiffness matrices for the material. The dispersive solutions are obtained in terms of phase velocity, group velocity (for undamped media), energy velocity (for damped media), attenuation, and cross-sectional mode shapes. Once the dispersive properties are computed, the wave motion can be interpreted and the forced response can be predicted. The proposed SAFE formulation was applied to enhance the use of ultrasonic guided waves in a number of applications and to interpret the corresponding experimental results. The following three applications were considered : defect detection in adhesively-bonded joints found in the wing skin-to-spar assemblies of Unmanned Aerial Vehicles; defect detection in railroad tracks; load monitoring and defect detection in seven-wire steel strands used in cables and prestressed concrete structures

Published
2007

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