Predicting Levels Of Damages To Buildings Caused  By Earthquake

Arulselvam C; Gowtham R; Pranavvarshan AT; Rajadurai P; Dr.R.Ragupathy

Volume 11, Issue 4 (April 2025)

Predicting Levels Of Damages To Buildings Caused By Earthquake

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Volume 12 Issue 07

July 2026

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Author(s)

Arulselvam C Gowtham R Pranavvarshan AT Rajadurai P Dr.R.Ragupathy

Abstract

Earthquakes Are Among The Most Devastating Natural Disasters, Causing Extensive Damage To Infrastructure And Posing Severe Threats To Human Life. Predicting The Level Of Building Damage Resulting From An Earthquake Can Greatly Enhance Disaster Preparedness And Response. This Project Explores The Use Of Advanced Deep Learning Techniques—such As CNN, BLSTM, GBNN, TabNet, TabPFN, And NODE—for Classifying Damage Levels Into Three Categories: Low, Medium, And High. Using The 2015 Nepal Earthquake Dataset, Which Includes Over 25,000 Records And 39 Features, Our Models Demonstrate Improved Performance, Achieving Accuracy Rates Of Over 74.5% In Some Cases. These Findings Highlight The Potential Of Deep Learning For Effective Structural Damage Assessment.

Keywords

Earthquake Building Damage Deep Learning CNN BLSTM GBNN TabNet TabPFN NODE Nepal Earthquake Dataset

Paper ID

IJSARTV11I4103219

Publication Date

April 20, 2025

Research Area

Computer Science And Engineering