Abstract
This research paper highlights a novel hybrid model, LSTM-Enhanced Gradient Boosting Regressor (LE-GBR), which aims to improve prediction of failure in critical infrastructure like bridges and buildings. Structures naturally degrade over time due to many factors like material fatigue and environmental pressures, making accurate predictions essential for safety and maintenance planning. Traditional models often miss complex interactions between these factors, but our method integrates Gradient Boosting Regressor (GBR) with Long Short-Term Memory (LSTM) networks, allowing us to capture both fixed and evolving patterns in structural health data. Through experiments on a collected dataset, LE-GBR model outperformed other approaches, providing more accurate forecasts of structural health conditions. This model offers a promising tool for enhancing structural safety by enabling more precise failure predictions, thereby supporting improved maintenance strategies and risk mitigation for critical infrastructure.
Keywords
Failure Prediction, Gradient Boosting Regressor, Hybrid Model, Infrastructure Safety, LSTM, Predictive Modeling, Structural Health Monitoring, Structural Integrity
Citations
IRE Journals:
Aryan Kesarkar , Chrisil Dabre , Raghav Agarwal , Yash Chavan , Prof. Ruhina Karani
"Enhanced Structural Health Monitoring Through LSTM-Enhanced Gradient Boosting Regressor" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 1221-1231
IEEE:
Aryan Kesarkar , Chrisil Dabre , Raghav Agarwal , Yash Chavan , Prof. Ruhina Karani
"Enhanced Structural Health Monitoring Through LSTM-Enhanced Gradient Boosting Regressor" Iconic Research And Engineering Journals, 8(9)
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