A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Nonlinear system identification on shallow foundation using Extended Kalman Filter
Abstract This study employs system identification using the Extended Kalman Filter to investigate variations in the stiffness and damping of shallow foundations during earthquakes. System identification results showed that the elastic stiffness of different foundations was significantly smaller than specifications proposed by FEMA 356 for the SE site class. As the earthquake load increased, a partial uplift of the foundation occurred. Following this uplift, the time domain inelastic stiffness decreased due to variations in contact area between the foundation and sub-soil. The inelastic stiffness at the maximum response was less than the elastic stiffness, according to the effective peak ground acceleration (EPGA) and the contact area ratio. After uplift in the foundation, the EPGA increased, the contact area ratio decreased, and the damping ratio increased by up to 20%. On the basis of these system identification results, we determined relationships between elastic stiffness and the ratio of bearing stress demand to the soil-foundation system capacity.
Highlights This paper employs system identification using the Extended Kalman Filter to investigate variations in the stiffness and damping of shallow foundations during earthquakes. This paper contributes to better understanding of the seismic behavior of a shallow foundation. It was found that a partial uplift of the foundation occurred, as the earthquake load increased. Following this uplift, the time domain inelastic stiffness decreased due to variations in contact area between the foundation and sub-soil. On the basis of these system identification results, the relationships between elastic stiffness and the ratio of bearing stress demand to the soil-foundation system capacity were proposed.
Nonlinear system identification on shallow foundation using Extended Kalman Filter
Abstract This study employs system identification using the Extended Kalman Filter to investigate variations in the stiffness and damping of shallow foundations during earthquakes. System identification results showed that the elastic stiffness of different foundations was significantly smaller than specifications proposed by FEMA 356 for the SE site class. As the earthquake load increased, a partial uplift of the foundation occurred. Following this uplift, the time domain inelastic stiffness decreased due to variations in contact area between the foundation and sub-soil. The inelastic stiffness at the maximum response was less than the elastic stiffness, according to the effective peak ground acceleration (EPGA) and the contact area ratio. After uplift in the foundation, the EPGA increased, the contact area ratio decreased, and the damping ratio increased by up to 20%. On the basis of these system identification results, we determined relationships between elastic stiffness and the ratio of bearing stress demand to the soil-foundation system capacity.
Highlights This paper employs system identification using the Extended Kalman Filter to investigate variations in the stiffness and damping of shallow foundations during earthquakes. This paper contributes to better understanding of the seismic behavior of a shallow foundation. It was found that a partial uplift of the foundation occurred, as the earthquake load increased. Following this uplift, the time domain inelastic stiffness decreased due to variations in contact area between the foundation and sub-soil. On the basis of these system identification results, the relationships between elastic stiffness and the ratio of bearing stress demand to the soil-foundation system capacity were proposed.
Nonlinear system identification on shallow foundation using Extended Kalman Filter
Kim, Dong-Kwan (author) / Park, Hong-Gun (author) / Kim, Dong-Soo (author) / Lee, Hyerin (author)
2019-09-06
Article (Journal)
Electronic Resource
English
Identification of nonlinear behavior of RC bridge piers using extended Kalman filter
| British Library Conference Proceedings | 2010
Parameter Identification in Unconfined Aquifer Using Extended Kalman Filter Algorithm
| British Library Online Contents | 1993
System identification of an experimental model by extended Kalman filter
| British Library Conference Proceedings | 2001