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Seismic Model Parameter Optimization for Building Structures.

Lengyel Károly1, Ovidiu Stan1, Liviu Miclea1

  • 1Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
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Summary

This study introduces a nonlinear dynamic model for seismic building analysis. The Differential Evolution (DE) algorithm proved superior to Particle Swarm Optimization (PSO) in estimating model parameters, with the model showing strong predictive performance.

Keywords:
DEPSOextended Kalman filterinverted pendulumoptimizationparameter estimationstructural dynamic modeling

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Area of Science:

  • Structural Engineering
  • Computational Mechanics
  • Earthquake Engineering

Background:

  • Traditional static analysis in seismic design is insufficient for capturing complex building dynamics.
  • Advanced analytical procedures are necessary due to the inherent nonlinear response of structures during earthquakes.
  • Accurate structural dynamic modeling is crucial for evaluating building behavior under various environmental factors, especially seismic events.

Purpose of the Study:

  • To propose a simple nonlinear dynamic model for analyzing building behavior during earthquakes.
  • To evaluate the performance of the proposed model in prediction scenarios.
  • To compare the effectiveness of Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms for parameter estimation in structural models.

Main Methods:

  • Development of a nonlinear dynamic model represented as a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints.
  • Parameter estimation of the building model using two optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE).
  • Utilizing at least two datasets of a building for model calibration and validation.

Main Results:

  • The Differential Evolution (DE) algorithm demonstrated superior performance compared to Particle Swarm Optimization (PSO) in most tested situations for parameter estimation.
  • The proposed nonlinear dynamic model exhibited good performance in prediction scenarios, indicating its utility for seismic analysis.
  • The study focused on assessing both the predictive capabilities of the structural model and the efficiency of the optimization algorithms employed.

Conclusions:

  • The developed nonlinear dynamic model is effective for predicting building behavior during seismic events.
  • The Differential Evolution (DE) algorithm is a more effective optimization tool than PSO for this specific structural modeling task.
  • This approach offers a valuable method for seismic design and evaluation, enhancing the understanding of structural dynamics.