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The Maluka Earthquake, the earthquake at Japan’s Hokkaido, and the Turkey-Syria Earthquakes of 2023 are the recent quakes that caused a great impact on the lives of people. The Prediction of damage levels caused by an earthquake is essential to a greater extent so that facilities the for recovery of buildings can be arranged in advance based on the consequences of medium to strong earthquakes. A recent ML boosting technique called CatBoost has been used to build the classification model and the model’s performance is tested. CatBoost algorithm handles multi-class imbalance conventional and big datasets well superior compared to other boosting algorithms. This work uses the data of constructions in Nepal that were affected by the 2015 Gorkha Earthquake that befell Nepal.

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Earthquake-Induced Damage Grade Prediction in Buildings Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • J. Blessy Karunya,
  • S. Varshini,
  • R. Jasmitha,
  • G. S. R. Emil Selvan,
  • M. P. Ramkumar

摘要

The Maluka Earthquake, the earthquake at Japan’s Hokkaido, and the Turkey-Syria Earthquakes of 2023 are the recent quakes that caused a great impact on the lives of people. The Prediction of damage levels caused by an earthquake is essential to a greater extent so that facilities the for recovery of buildings can be arranged in advance based on the consequences of medium to strong earthquakes. A recent ML boosting technique called CatBoost has been used to build the classification model and the model’s performance is tested. CatBoost algorithm handles multi-class imbalance conventional and big datasets well superior compared to other boosting algorithms. This work uses the data of constructions in Nepal that were affected by the 2015 Gorkha Earthquake that befell Nepal.