Structural Health Monitoring (SHM) is vital to guarantee the early identification of any deterioration in bridge structures and to prevent unexpected collapses. As structures degrade, one of the most efficient techniques in SHM is vibrationbased damage diagnosis, which evaluates damage through analysing the dynamic features of structures. Identification of bridge deterioration may be done by measuring the vibration response caused by cars crossing a bridge. This paper proposes a unique Vehicle-Bridge Interaction (VBI)-based damage detection method to assess the bridge's condition.First, the method's foundations of theory are studied. A half-car dynamic model represents the vehicle and its suspension system, while the Finite Element Method is used to describe the bridge superstructure. The equations of motion for the bridge and the vehicle are obtained, respectively, using the mode superposition technique, D'Alembert's principle, and the finite element method. After that, the Newmark-beta approach is used to solve the linked dynamic issue. The roughness of the bridge road surface is taken into consideration during the examination.The proposed method for determining bridge damage is based on an analysis of the pitching rotation of a passing vehicle. The three main theoretical elements of this response are the bridge natural frequency, the vehicle frequency, and a pseudo-frequency component related to the vehicle speed. The Empirical Mode Decomposition (EMD) approach dissects the signal into its constituent parts, or intrinsic mode functions. A recommended damage detection method makes use of the intrinsic mode functions (IMFs) corresponding to the vehicle speed component of the response obtained from the passing vehicle. Through the study, it is observed that the proposed technique is capable of effectively identifying various damage cases considered in this research.

This study aims to explore the integration of historical weather data with machine learning methods for better rainfall forecasting in Bangladesh. It has the potential to identify rainfall early by observing their prediction on preventing natural disasters. Weather forecasting protects human lives and property but often struggles with manual, error-prone calculations due to the complex data involved. This research combines weather forecasting with machine learning techniques to improve the accuracy and precision of predictions. The main goal is to develop an accurate prediction system of rainfall forecasting in Bangladesh for the future. The model training data set is from historical meteorologic records of Bangladesh. After neatly hyper-tuning the Random Forest model, we achieve some impressive RMSE metrics. The results are compared with a baseline model, Randomized Search CV, using different evaluation metrics such as MAE, MSLE, and RMSLE, etc., and the observations of significantly better performance is observable. An extensive analysis of machine learning techniques was performed in the domain of rainfall prediction. Among all the tried models, the Random Forest model gave perfect predictions, as noted by its MSE of 12245.52 and absolute error value of 64.25. Furthermore, there was excellent precision of 95% and recall 92%, respectively, for the classification of wet days, corresponding to a total accuracy score of 91%. Based on the above result, we can conclude that the Random Forest model is both robust and promising for rainfall prediction problems. Various models such as Linear Regression, Support Vector Regressor, Decision Tree, K-Nearest Neighbors, AdaBoost, XG Boost, Ridge, Linear SVR and MLP Regressor showed variations in prediction accuracies and errors. Nevertheless, the Random Forest came out as a better choice in this case, showing its superiority.