Evaluation of Concrete Performance of Risky Buildings in Earthquakes using Machine Learning Depremdeki Riskli Yapilarin Beton Performansinin Yapay grenme Y ntemleri Ile Degerlendirilmesi

Saglam M. M., Onal O. Y., Keskin M., Kus Z., Goncu S., ÇAKIR Ö., ...More

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Turkey, 25 - 28 June 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu66497.2025.11112070
  • City: İstanbul
  • Country: Turkey
  • Keywords: Concrete Sample Classification, Machine Learning, Structural Health Monitoring, Structural Strength Prediction
  • Yıldız Technical University Affiliated: Yes

Abstract

Predicting the strength of building materials is nowadays a vital process for taking precautions against earthquakes. Increasing the efficiency of this process by reducing its cost can prevent possible loss of life. In this study, machine learning based models were used. First, vibration data were collected on 6 cylinder and 33 standard cube concrete specimens using accelerometers and piezoelectric sensors, followed by preprocessing steps such as noise reduction and normalization. XGBoost and Random Forest regression algorithms were used in the model training process. The cross-validation value MAE (Mean Absolute Error) obtained from the XGBoost model trained with data from cube samples was 6.45, compared to the MAE of 10.9 obtained from the Random Forest model. However, due to the limited data set, it is predicted that there may be variability in the results of the models with larger data sets.