0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Structural response reconstruction using inclinometer and velocimeter

 Structural response reconstruction using inclinometer and velocimeter
Auteur(s): , ORCID,
Présenté pendant IABSE Congress: The Evolving Metropolis, New York, NY, USA, 4-6 September 2019, publié dans , pp. 1977-1983
DOI: 10.2749/newyork.2019.1977
Prix: € 25,00 incl. TVA pour document PDF  
AJOUTER AU PANIER
Télécharger l'aperçu (fichier PDF) 0.23 MB

This paper proposes a structural dynamic response reconstruction algorithm using inclinometer and velocimeter, combining in-situ measured data with finite element model. Using a small number of inc...
Lire plus

Détails bibliographiques

Auteur(s): (Tongji University)
ORCID (Tongji University)
(Fujian academy of Building Research)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Congress: The Evolving Metropolis, New York, NY, USA, 4-6 September 2019
Publié dans:
Page(s): 1977-1983 Nombre total de pages (du PDF): 7
Page(s): 1977-1983
Nombre total de pages (du PDF): 7
DOI: 10.2749/newyork.2019.1977
Abstrait:

This paper proposes a structural dynamic response reconstruction algorithm using inclinometer and velocimeter, combining in-situ measured data with finite element model. Using a small number of inclination and velocity data, the dynamic deflection, rotation, and strain at any position of a structure can be estimated. Firstly, static structural deformation estimation method is introduced as the base. The key content is to solve an underdetermined static equation using partial least square regression and calculate equivalent nodal force. By rewriting dynamic balance equation into state space, an equivalent static balance equation is obtained. Use partial least square regression to solve this equation and compute time histogram of equivalent nodal force, in which dynamic distortion exists. Accordingly, a frequency response-based time interval correction method is proposed to correct the dynamic distortion and is validated to be effective. Finally, numerical simulation is adopted to validate accuracy and robustness of the algorithm through changing parameters including sampling time interval, input frequency components, model parameters and introducing measurement noise. All results have demonstrated that the algorithm is of good adaptability to various changes and maintains high accuracy.