For this project, I focused on the application of machine learning models, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, to time-series forecasting in structural health monitoring (SHM). These models were selected due to their ability to capture temporal dependencies in data, which is critical for predicting future behavior based on historical sensor readings. The project also incorporated attention mechanisms, which further improved the model’s ability to focus on the most relevant parts of the data, leading to more accurate forecasts.
The dataset consisted of sensor readings from infrastructure like bridges and buildings, and the goal was to detect early signs of structural deterioration. By training the models on historical data, I was able to develop a predictive system capable of identifying anomalies in real-time. This allowed for proactive maintenance, reducing the risk of catastrophic failures and extending the lifespan of critical infrastructure.
Attention mechanisms added an additional layer of accuracy by allowing the model to focus on specific time frames or data points that were more relevant to predicting future behavior. The result was a robust forecasting tool that outperformed traditional models in terms of both accuracy and reliability. This project highlights the power of machine learning in the field of structural health monitoring, offering a data-driven approach to infrastructure maintenance.