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RISS 인기검색어
- 검색키워드
- 저자 : Egidio Rizzi (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Closed-form optimum tuning formulas for passive Tuned Mass Dampers under benchmark excitations
Jonathan Salvi,Egidio Rizzi 국제구조공학회 2016 Smart Structures and Systems, An International Jou Vol.17 No.2
This study concerns the derivation of optimum tuning formulas for a passive Tuned Mass Damper (TMD) device, for the case of benchmark ideal excitations acting on a single-degree-of-freedom (SDOF) damped primary structure. The free TMD parameters are tuned first through a non-linear gradient-based optimisation algorithm, for the case of harmonic or white noise excitations, acting either as force on the SDOF primary structure or as base acceleration. The achieved optimum TMD parameters are successively interpolated according to appropriate analytical fitting proposals, by non-linear least squares, in order to produce simple and effective TMD tuning formulas. In particular, two fitting models are presented. The main proposal is composed of a simple polynomial relationship, refined within the fitting process, and constitutes the optimum choice. A second model refers to proper modifications of literature formulas for the case of an undamped primary structure. The results in terms of final (interpolated) optimum TMD parameters and of device effectiveness in reducing the structural dynamic response are finally displayed and discussed in detail, showing the wide and ready-to-use validity of the proposed optimisation procedure and achieved tuning formulas. Several post-tuning trials have been carried out as well on SDOF and MDOF shear-type frame buildings, by confirming the effective benefit provided by the proposed optimum TMD.
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Effective Heterogeneous Data Fusion procedure via Kalman filtering
Ravizza, Gabriele,Ferrari, Rosalba,Rizzi, Egidio,Chatzi, Eleni N. Techno-Press 2018 Smart Structures and Systems, An International Jou Vol.22 No.5
This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.
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Effective Heterogeneous Data Fusion procedure via Kalman filtering
Gabriele Ravizza,Rosalba Ferrari,Egidio Rizzi,Eleni N. Chatzi 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.5
This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.
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Mechanical behaviour of a syntactic foam/glass fibre composite sandwich: experimental results
Papa, Enrico,Corigliano, Alberto,Rizzi, Egidio Techno-Press 2001 Structural Engineering and Mechanics, An Int'l Jou Vol.12 No.2
This note presents the main results of an experimental investigation into the mechanical behaviour of a composite sandwich conceived as a lightweight material for naval engineering applications. The sandwich structure is formed by a three-dimensional glass fibre/polymer matrix fabric with transverse piles interconnecting the skins; the core is filled with a polymer matrix/glass microspheres syntactic foam; additional Glass Fibre Reinforced Plastics extra-skins are laminated on the external facings of the filled fabric. The main features of the experimental tests on syntactic foam, skins and sandwich panels are presented and discussed, with focus on both in-plane and out-of-plane responses. This work is part of a broader research investigation aimed at a complete characterisation, both experimental and numerical, of the complex mechanical behaviour of this composite sandwich.
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