Bridge structural health assessment based on gaussian mixture model-hidden Markov model

Abstract To address the critical need for reliable safety assurance and efficient health assessment of in-service bridge structures, this study proposes a structural health evaluation method based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), aiming to tackle key challenges in automated monitoring and intelligent assessment of bridges. First, an automated monitoring system is established by deploying multi-type sensors (including displacement meters, strain gauges, inclinometers, and vibration sensors), enabling real-time data acquisition and forming a networked structural health monitoring (SHM) framework. Second, to overcome the issues of massive data volume and severe noise interference inherent in multi-sensor monitoring, a phased ‘anomaly detection-intelligent repair’ mechanism is developed for data preprocessing, ensuring the quality and reliability of input data. Subsequently, the proposed GMM-HMM is applied to practical bridge health assessment, using the Shangmadun Bridge in Jingmen (Hubei Province) as a case study. This application validates the method’s feasibility and effectiveness in quantifying structural health conditions, and a comparison with the GMM demonstrates the superiority of the GMM-HMM. Furthermore, to enhance model performance, the K-means algorithm is introduced to optimize the initial parameter selection of GMM-HMM. Compared with random initialization, this optimization reduces iterations by 48.4%, significantly mitigating the risk of local optima and improving convergence efficiency. The findings validate that the integrated approach provides a robust solution for automated, accurate, and efficient bridge health assessment, supporting data-driven decision-making in structural maintenance and safety management.