| 種別 | paper |
| 主題 | Localized Identification of Structural Parameters by Kalman Filter |
| 副題 | |
| 筆頭著者 | Andres W.C. ORETA（Graduate School of Nagoya University） |
| 連名者1 | Tada-aki TANABE（Nagoya University） |
| 連名者2 | |
| 連名者3 | |
| 連名者4 | |
| 連名者5 | |
| キーワード | |
| 巻 | 14 |
| 号 | 2 |
| 先頭ページ | 1029 |
| 末尾ページ | 1034 |
| 年度 | 1992 |
| 要旨 | INTRODUCTION The problem of system identification and parameter estimation as applied to structural engineering is complicated and expensive (in terms of computing time) especially in the analysis of structures modelled with a large number of degrees of freedom. To reduce the size of the system under consideration, the substructure approach in the analysis of structures looks attractive. In substructuring, a structure is divided into a number of smaller subsystems called substructures whose boundaries are specified. This approach when applied to system identification is efficient and practical because the analysis can be concentrated on the local and critical parts of the structure. The identification of a local and critical part of structures is useful in the evaluation of the condition of structures. The overall performance of structures is dependent on the individual members. If one member is damaged, the load carrying capacity of the structure will certainly decrease and total collapse may occur especially if the damaged member is a critical part of the structure. In this regard, the identification of the structural parameters of the local part of structures becomes significant. The extended Kalman filter has been applied by a number of researchers in many problems in structural dynamics. However, unlike the past applications where the total structure is analysed, this paper presents a localized approach in the identification of structural parameters. By substructuring, a structure was divided into primary and secondary systems with common boundary and the identification was concentrated on the secondary system. Incorporating the state and observation equations formulated from the equation of motion of the secondary system in the EKF algorithm, the stiffness and damping parameters were estimated. To illustrate the localized identification approach, a simple shear building model subjected to ground motion was analyzed and the identification was concentrated on the first story. CONCLUSION A procedure for localized identification of structural parameters using the extended Kalman filter was presented. In the procedure, a structure was decomposed into two substructures which were attached at a common boundary, and three systems, primary, boundary and secondary systems, were formed and the identification of parameters was concentrated on the secondary system. The localized identification procedure was illustrated by analyzing a simple shear building where the identification was concentrated on the first story. The application to the shear building was useful and practical especially for highrise buildings since the structural parameters at the lower levels can be estimated without considering the response at the higher levels. Although it was not shown in this paper, the proposed method can also be used to identify the total structure by dividing the structure into a number of substructures and applying the method to each substructure. To verify the capability and usefulness of the localized identification procedure, applications to more complicated structures must be conducted. |
| PDFファイル名 | 014-01-2178.pdf |