Stick-Slip Classification Based on Machine Learning Techniques for Building Damage Assessment

Yunsu Na*, Sherif El-Tawil, Ahmed Ibrahim, Ahmed Eltawil

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Accelerometers in smart devices have been used to successfully provide valuable information such as early warnings of earthquake activity and health monitoring of buildings. The next important step of using the acceleration measurements from smart devices is to assess building seismic damage, which is a more challenging application. A main challenge is related to the sliding motions of smart devices, which prevents acceleration measurements from directly representing the movement of underlying building floors. To detect and remove sliding motions in acceleration measurements, this paper presents an accurate and robust accelerometer-based stick-slip motion classification framework based on machine learning techniques. Three types of machine learning algorithms are introduced, and their classification performance are compared; support vector machine (SVM), multilayer perception (MLP), and recurrent neural networks (RNN). For the SVM and MLP, three classification conditions are considered: feature selection, non-linear discriminating analysis and classifier comparison. For the RNN, three hyperparameters are considered to find the best performing classification algorithm. Each algorithm is trained and validated with experimental acceleration data from a shaking table test.

Original languageEnglish (US)
Pages (from-to)5848-5865
Number of pages18
JournalJournal of Earthquake Engineering
Issue number11
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.


  • accelerometer
  • building damage assessment
  • Machine learning
  • stick-slip classification

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality


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