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Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings.

Xijun Ye1, Yingfeng Wu1, Liwen Zhang1

  • 1School of Civil Engineering, Guangzhou University, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|February 26, 2020
PubMed
Summary

This study models how ambient factors like temperature affect structural modal frequencies using nonlinear principal component analysis (NLPCA) and support vector regression (SVR). The NLPCA-SVR model accurately predicts these changes, enhancing structural health monitoring.

Keywords:
Guangzhou New TV Towerambient effectsmodal frequencynonlinear principal component analysissupport vector regression

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Area of Science:

  • Structural Engineering
  • Data Science
  • Environmental Monitoring

Background:

  • Modal frequencies of structures are sensitive to environmental changes (temperature, wind).
  • Accurate modeling is crucial for structural health monitoring and safety.
  • Existing methods may not fully capture complex, nonlinear relationships.

Purpose of the Study:

  • To develop a robust mathematical model correlating ambient factors with modal frequencies.
  • To effectively eliminate the influence of environmental variables on structural responses.
  • To enhance the accuracy and generalization performance of modal frequency prediction.

Main Methods:

  • Nonlinear Principal Component Analysis (NLPCA) for feature extraction and dimensionality reduction of ambient factors.
  • Support Vector Regression (SVR) for modeling the relationship between extracted components and modal frequencies.
  • Optimization of SVR hyperparameters using Grid Search Method (GSM), Genetic Algorithm (GA), and Fruit Fly Optimization Algorithm (FOA), with FOA showing superior performance.

Main Results:

  • The proposed NLPCA-SVR model demonstrated high generalization performance in predicting modal frequencies.
  • The model effectively captured and reflected the strong correlation between ambient factors and modal frequencies.
  • Validation using Guangzhou New TV Tower (GNTVT) Benchmark data confirmed the method's efficacy.

Conclusions:

  • The integrated NLPCA-SVR approach provides an effective means to model and mitigate the impact of ambient factors on structural modal frequencies.
  • This method offers a significant advancement for accurate structural health monitoring systems.
  • The study highlights the potential of advanced machine learning techniques in civil engineering applications.