A novel hybrid model for bridge dynamic early warning using LSTM-EM-GMM

Abstract

Early warning of existing bridges is now predominated by deterministic methods. However, these methods face challenges in expressing uncertain factors (such as wind load, temperature load, and other variables, etc.). These problems directly impact the timeliness and accuracy of bridge early warning. This study develops an innovative method for bridge dynamic early warning with high versatility and accuracy. Long short-term memory network model (LSTM), expectation maximization (EM) and Gaussian mixture model (GMM) were employed in the proposed method. Firstly, the LSTM model is used to predict the measured monitoring data (such as deflection, strain, cable force, etc.) in real time to obtain the predicted results. Next, the number of clusters for the EM-GMM model is determined using the Calinski-Harabasz (CH) index. The method aims to comprehensively consider the internal cohesion of the clustering, ensuring accurate and reliable clustering results. Then, the EM-GMM model is used to cluster the random influence error and the predicted value, which can get the probabilistic prediction result of each corresponding random influence error. On this basis, the dynamic early warning interval under 95% confidence level is constructed. This facilitates early warning and decision-making for potential structural abnormalities. Finally, the accuracy and practicability of the method are verified by the comparison of engineering applications and existing specifications. The results demonstrate that the probabilistic early warning method considering the uncertain factors in the complex service environment can accurately achieve the dynamic early warning of bridges.