An intelligent deflection prediction model based on bridge health monitoring big data
Year: 2026
Type: article
Abstract: As a key indicator reflecting the structure and performance variations of bridge， deflection can intuitively represent the stress and deformation characteristics of bridge and provide critical information for health assessment.This paper aims to develop an accurate and stable deflection prediction method. A hybrid model combining signal decomposition method with deep learning is proposed.First， deflection big data collected from structural health monitoring system are decomposed into multiple intrinsic mode functions （IMFs） using the complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）.Then the frequency-domain characteristics of each component are analyzed and used as the model inputs to enhance the feature representation.To further improve the predictive performance， the whale optimization algorithm （WOA） is applied to globally optimize the hyperparameters of the extended long short-term memory （xLSTM） network.Finally， the CEEMDAN-WOA-xLSTM model is constructed to perform deflection prediction intelligently.Simulation experiments are employed to demonstrate the performance of the model in long-term time series prediction.Actually， the model completes the prediction with R2 value of 0.934 4 and RMSE value of 0.239 9.In comparison to the conventional LSTM and xLSTM models， R2 values increased by 4.92% and 1.80%， while RMSE values decreased by 29.14% and 13.13%， respectively， thus indicating a significant reduction of prediction error.Meanwhile， the maximum values of the 95% confidence intervals of the model’s absolute and squared prediction errors are 0.192 9 and 0.063 6， respectively， thus demonstrating an excellent error control and generalization capability.It is expected that the proposed model can pave a way to the robust technical solutions for bridge health monitoring and informed maintenance decision-making.
Authors: HAO Yanjun, DONG Hongxia, ZHAO Xiaojin, XU Ting
Language: Chinese
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162727385