MR 댐퍼를 이용한 지진하중을 받는 지진격리 벤치마크 구조물의 신경망제어

1. 2006 춘계 지진공학회 학술발표회 17~18, Mar, 2006 MR 댐퍼를 이용한 지진하중을 받는 지진격리 벤치마크 구조물의 신경망제어 Heon-Jae Lee*: Ph.D. Candidate, KAIST, Korea Sang-Won Cho: Post Doctoral Fellow, UWO, Canada Ju-Won Oh: Professor, Hannam University, Korea In-Won Lee: Professor, KAIST, Korea

2. OUTLINE • Introduction • Benchmark Problem • Proposed Method • Training Neuro-Control System • Performance Evaluation • Conclusion Structural Dynamics & Vibration Control Lab., KAIST, Korea

3. Kodiak, Alaska San Francisco Introduction • Base Isolation • One of the most widely implemented seismic protection system • Mitigates the effects of an earthquake by isolating the structure from ground motion Structural Dynamics & Vibration Control Lab., KAIST, Korea

4. Lead Rubber Bearing Friction Pendulum Bearing Introduction • Nonlinear Devices for Base Isolation • Benefit • Restoring force and adequate damping capacity can be obtained in one device • Drawbacks • Strongly nonlinear • Poor adaptability for a wide range of ground motion • Especially strong impulsive ground motions generated at near-source location Structural Dynamics & Vibration Control Lab., KAIST, Korea

5. Introduction • Hybrid Control Strategies • Consisting of a base isolation system combined with actively controlled actuators • Advantages • High performance in reducing vibration • High adaptability for different ground excitation • Ability to control of multiple vibration modes • Drawbacks • Requirement of a large external power supply • Active systems may have the risk of instability Structural Dynamics & Vibration Control Lab., KAIST, Korea

6. Introduction • Semi-Active Base Isolation System • Consisting of a base isolation system that employs semi-active control devices (e.g. MR dampers, controllable friction dampers) • Similar high adaptability to the active system • No requirement of large power supplies Structural Dynamics & Vibration Control Lab., KAIST, Korea

7. Introduction • Semi-Active Neuro-Controller • Improved neuro-controller (Kim et al., 2000, 2001) • New training algorithm based on cost function • Sensitivity evaluation algorithm to replace an emulator neural network • Clipped algorithm • Clips the control force that cannot be achieved by MR damper Structural Dynamics & Vibration Control Lab., KAIST, Korea

8. Benchmark Problem • Purpose • To provide systematic and standardized means • By making direct comparisons between competing control strategies, including devices, algorithms, sensors, etc. • To allow researchers in structural control to test their algorithms and devices • This benchmark problem is about a smart base isolation system. Structural Dynamics & Vibration Control Lab., KAIST, Korea

9. Benchmark Problem • Benchmark Structure • A base-isolated eight-story, steel-braced framed building • Similar to existing building in Los Angeles, California • In this investigation, only the linear elastomeric isolation system which consists of 92 bearings is considered. • Modeled using three master degrees of freedom per floor • 16 MR dampers (8 along the x-axis and 8 along the y-axis) are also installed Structural Dynamics & Vibration Control Lab., KAIST, Korea

10. Benchmark Problem • Sample Earthquakes • Both the fault-normal (FN) and fault-parallel (FP) components of Newhall, Sylmar, El Centro, Rinaldi, Kobe, Ji-ji, Erzinkan • Control Cases • Case I : x-direction (FP), y-direction (FN) • Case II: x-direction (FN), y-direction (FP) Structural Dynamics & Vibration Control Lab., KAIST, Korea

11. STRUCTURE Clipped Algorithm MR Damper Proposed Method Neural Network Clipped Neuro-Controller Block diagram of the proposed method Structural Dynamics & Vibration Control Lab., KAIST, Korea

12. Proposed Method • Structure of the neural network Output layer Input layer Hidden layer Structural Dynamics & Vibration Control Lab., KAIST, Korea

13. Proposed Method • Improved Neuro-Controller • New training algorithm • The neuro-controller is trained by minimizing a cost function where, is specific state, is control signal and are weighting matrices Structural Dynamics & Vibration Control Lab., KAIST, Korea

14. Proposed Method • Improved Neuro-Controller • The update of weights and biases between the output layer and hidden layer can be simply expressed as where, :learning rate :sensitivity :unit vector :activation function of the output layer Structural Dynamics & Vibration Control Lab., KAIST, Korea

15. Proposed Method • Improved Neuro-Controller • In the same manner, update of those between the hidden layer and input layer can be obtained as where, Structural Dynamics & Vibration Control Lab., KAIST, Korea

16. Proposed Method • Clipped Algorithm • Desired force (by neural network): • Generated force (by MR damper): Structural Dynamics & Vibration Control Lab., KAIST, Korea

17. where, Training Neuro-Controller • Training Data • Filtered artificial earthquake record • Magnitude: scaled to match the maximum acceleration of the given earthquakes • Shaping filter Ground acceleration (m/sec2) Time (sec) Structural Dynamics & Vibration Control Lab., KAIST, Korea

18. Training Neuro-Controller • Structure of Neuro-Controller • Two neuro-controllers are employed for x and y-directions, respectively. Structure of each direction’s neuro-controller Structural Dynamics & Vibration Control Lab., KAIST, Korea

19. Training Neuro-Controller • Cost Function • In cost function, are included. • Optimal weighting matrices Structural Dynamics & Vibration Control Lab., KAIST, Korea

20. Training Neuro-Controller • Cost function vs. epoch • The cost function converges in both x and y-directions, which means the training is successful. x-direction Cost function y-direction epoch Structural Dynamics & Vibration Control Lab., KAIST, Korea

21. Performance Evaluation • Evaluation Criteria • Based on both maximum and RMS responses of the building J1 - Peak base shear J2 - Peak structure shear J3 - Peak base displacement J4 - Peak inter-story drift J5 - Peak absolute floor acceleration J6 - Peak generated force J7 - RMS base displacement J8 - RMS absolute floor acceleration J9 - Total energy absorbed Structural Dynamics & Vibration Control Lab., KAIST, Korea

22. Performance Evaluation • Performance Comparison (Case I) Sylmar El Centro Newhall Rinaldi Kobe Jiji Erzinkan Passive (Vmax) Clipped optimal Proposed Structural Dynamics & Vibration Control Lab., KAIST, Korea

23. Performance Evaluation • Performance Comparison (Case II) Sylmar El Centro Newhall Rinaldi Kobe Jiji Erzinkan Passive (Vmax) Clipped optimal Proposed Structural Dynamics & Vibration Control Lab., KAIST, Korea

24. Performance Evaluation • Corner drift (normalized by uncontrolled values) Structural Dynamics & Vibration Control Lab., KAIST, Korea

25. Conclusion • A new semi-active control strategy for seismic response reduction using neuro-controller and MR dampers is proposed. • The proposed strategy was applied to a benchmark building installed with linear elastomeric isolation system. • In numerical simulation results, the proposed strategy can significantly reduce the floor acceleration, base shear and building corner drift with only a slight increase of the base displacement. Structural Dynamics & Vibration Control Lab., KAIST, Korea

26. Thank you for your attention!!! Structural Dynamics & Vibration Control Lab., KAIST, Korea

27. Sensitivity evaluation algorithm (Kim et al., 2001) State space equation of structure (9) In the discrete-time domain, (10) where represents the sensitivity, . Structural Dynamics & Vibration Control Lab., KAIST, Korea