Regression model for structural health monitoring of a lab scaled bridge

The interest in observation of the dynamic behavior of bridges have been increasing in the recent years. The movement of bridge deck plays a significant role in the safety of bridges. In this project work, a direct and indirect sensor mounted on the bridge structure and on the passing vehicle are used for structural health monitoring. The overall study has been implemented based on six reliable approaches, including Gradient Boosting regression, Random Forest Regression, Ridge Regression, Support Vector Regression, Elastic Net Regression, XGBoost Regression and Support Vector Regression to get accurate results of prediction for structural health condition. For each of these regression models, the following performance evaluations are obtained: Mean Square Error (MSE), Root Mean Square Error (RMSE) and Rsquared. After obtaining all performance evaluations, the comparison of each of these metrics are done for all the six regressors. Finally, by using a Voting Regression, these six regression models are combined and used to train the entire dataset and predict on the test set. By using voting regression an ensemble model is proposed for this experiment.