Born-Jordan frequency distribution based E-GMANN controller for vibration analysis in SSI based asymmetric floor...

Area of Science:

• Structural Engineering
• Seismic Analysis
• Control Systems

Background:

• Active Tuned Mass Damper (ATMD) systems are crucial for minimizing seismic vibrations in asymmetric buildings.
• Challenges exist in precise frequency matching and optimal control under dynamic conditions, leading to mode coupling and sub-optimal force estimation.
• Soil-Structure Interaction (SSI) in asymmetric buildings can cause liquefaction, twisting vibrations, and reduced structural resistance.

Purpose of the Study:

• To propose a novel Born Frequency Distribution-Ebola Neural Network Controller with Frequency Cyclic Linear Analysis for ATMD systems.
• To address challenges in frequency matching, optimal control, and real-time estimation of damper mass and control force.
• To improve seismic resilience and reduce vibration, acceleration, and displacement in asymmetric buildings.

Main Methods:

• A Born-Jordan Vibration Dose-based Eigensystem Frequency Distribution was used for optimal frequency matching and isolating Sole Causative Factors (SCF).
• An Ebola-optimized Graph Multivariable Adaptive Neural Network (E-GMANN) was employed for dynamic estimation of damper mass and real-time control force.
• Frequency Domain-Cyclic Equivalent Linear Analysis (TD-CELA) was utilized to address non-linear soil behavior and enhance deformation prediction.

Main Results:

• The proposed model significantly reduces vibration, acceleration, and displacement compared to existing methods.
• The novel controller enhances KPI tracking and system responsiveness through real-time adjustments.
• Improved frequency matching and isolation of causative factors led to enhanced structural resilience.

Conclusions:

• The developed ATMD control system, integrating advanced neural networks and analysis techniques, offers superior seismic vibration mitigation for asymmetric buildings.
• The study highlights the effectiveness of the proposed methods in overcoming limitations of traditional control algorithms and addressing SSI effects.
• This research contributes to more resilient and safer building designs in earthquake-prone regions.