STUDY OF ACTIVE STRUCTURAL VIBRATION CONTROL ON SUSPENSION BRIDGE PYLONS DUE TO EARTHQUAKE EXCITATION USING ACTIVE MASS DAMPER AND NEURO-FUZZY ALGORITHM

<p align="justify"> This final project consists of case study which discusses about active structural vibration control. The external excitation affecting the structure is earthquake excitation. The earthquake chosen for the case study is the El Centro earthquake. The structural mode...

Full description

Saved in:
Bibliographic Details
Main Author: AGUS PRIMATAMA (nim : 15006146), MUHAMMAD
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/29111
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:<p align="justify"> This final project consists of case study which discusses about active structural vibration control. The external excitation affecting the structure is earthquake excitation. The earthquake chosen for the case study is the El Centro earthquake. The structural model in the case study would be a linear suspension bridge. The suspension bridge uses one active mass damper on each pylon. The active mass damper is placed at the top of the pylon. The magnitude and the direction of the force are calculated using computer program that utilize a <br /> <br /> certain control algorithm. The control algorithm chosen for the base calculation is optimal control. Optimal <br /> <br /> control calculates the force required by the structure as a function of the condition of the structure. In closed-loop optimal control, the control force depends on the deflection and the velocity of the structure. Other algorithm is Artificial Neural Network. This system copies <br /> <br /> the optimal calculation by learning from the data calculated using optimal control. Based on literatures, the ANN system works better than optimal control since it requires less calculating time. In this final project, the response of the structure would be compared between using <br /> <br /> ANN and Neuro-Fuzzy algorithm. Neuro-Fuzzy combines the advantage of the ANN and Fuzzy Logic. It is shown in this final project that for a limited capacity of actuator and a <br /> <br /> large external force, the Neuro-Fuzzy provides a better control performance than the ANN. <p align="justify">