Identification of Dynamic Motion of the Ground using the Kalman Filter Finite Element Method

The purpose of this paper is to investigate an identification method of magnitude of blasting force using the Kalman filter finite element method. The identification is carried out with estimation at the same time. Acceleration is estimated using actual observation data. As the state equation, the balance of stress equation, the strain - displacement equation, and the stress strain equation are used. For temporal discretization, the Newmark beta method is employed and for the spatial discretization the Galerkin method is applied. The Kalman filter finite element method is a combination of the Kalman filter and the finite element method. This is capable of estimation not only in time but also in space directions. However, long computational time is required for computation. To reduce the computational time, the computational domain is divided into two parts, the main and subsidiary domains. In the main domain, filtering procedures are carried out, whereas only a deterministic process is considered for the variables in the subsidiary domain. Eliminating the state variables in the subsidiary domain, a drastically effcient computation is carried out. This method is applied to Futatsuishi quarry site. The site is located in Mt. Minowa in Miyagi prefecture, Japan. The blasting examination was carried out on September, 19th through 22th, 2005. Acceleration is measured by the accelerometer, which was set at two points. One is used as a reference and the other is used as an observation. A velocity is measured by the speedometer, which was set also at two points. These are used as observation data. The acceleration is estimated by using observation data and a blasting force. It is necessary to identifiy the blasting force in advance. In this research, two computations are carried out to verify the present method. The identified values are used as blasting forces in the estimation. The estimation values are compared with observation values at estimation points.