Bayesian network-based modal frequency–multiple environmental factors pattern recognition for the Xinguang Bridge using long-term monitoring data

Abstract

Modal frequency is an important indicator reflecting the health status of a structure. Numerous investigations have shown that its fluctuations are related to the changing environmental factors. Thus, modelling the modal frequency–multiple environmental factors relation is essential for making reliable inference in structural health monitoring. In this study, the Bayesian network (BN)-based algorithm is developed for recognizing the pattern between modal frequency and multiple environmental factors. Different candidates of network structure of the BN are proposed to describe the possible statistical relations of different variables. In the BN-based pattern recognition, the learning phase conducts uncertainty quantification in both parameter and model levels; and the prediction phase makes inference under complete and incomplete observed information. Based on the long-term monitoring data, the most plausible network structure is selected, and its associated parameters are identified. The developed algorithm is then utilized for analyzing the long-term monitoring data (modal frequencies, temperature, humidity, wind speed and traffic volume) of the Xinguang Bridge (a 782-m three-span half-through arch bridge). It turns out that the selected network structure properly captures the pattern of modal frequency–multiple environmental factors.

A Review on Damage Monitoring and Identification Methods for Arch Bridges

“…Due to the certain impact of environmental factors on the modal characteristics of structures, the existing literature has studied variations in the modal characteristics of steel arch bridges under environmental influences, aiming to further improve the accuracy of damage identification by eliminating the impact of environmental factors. For example, Mu et al [161] used Bayesian network to model the relationship between the long-term monitoring data (temperature, humidity, wind speed and traffic volume) and the modal frequency of Xinguang Bridge (a 782 m steel arch bridge). The results showed that the selected network structure adequately captured the relationship between modal frequency and multiple environmental factors.…”

Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

“…To consider multiple sources of environmental and loading factors, Zhang et al 42 used GPR to effectively quantify the adverse influences of temperature, wind speed and peak acceleration from modal frequencies; Soria et al 67 established a dynamic MLR model and concluded that the corrected frequency has a closer range of variability than the original; Bayesian network model (BNM) was developed to quantify the uncertainty of different environmental variables (temperature, humidity, wind speed, and traffic volume) of Xinguang Bridge at both the parameter and model levels. 68 Furthermore, linear response surface model (LRSM) and linear superposition model (LSM) were applied on the Tamar Bridge for accurate frequency estimation. 23,69 In practice, often only a few environmental variables are measurable.…”

“…To characterize the dependency of modal frequency on temperature and wind speed, the NLPCA‐ANN and NLPCA‐SVR combined models were conducted on the Yonghe Bridge and the Guangzhou New TV Tower 63 ; Ma et al 49 successfully established PCA‐GPR model with consideration of the temperature, humidity, wind speed, and direction. To consider multiple sources of environmental and loading factors, Zhang et al 42 used GPR to effectively quantify the adverse influences of temperature, wind speed and peak acceleration from modal frequencies; Soria et al 67 established a dynamic MLR model and concluded that the corrected frequency has a closer range of variability than the original; Bayesian network model (BNM) was developed to quantify the uncertainty of different environmental variables (temperature, humidity, wind speed, and traffic volume) of Xinguang Bridge at both the parameter and model levels 68 . Furthermore, linear response surface model (LRSM) and linear superposition model (LSM) were applied on the Tamar Bridge for accurate frequency estimation 23,69 …”

Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring

“…Temperature is a critical loading factor for structures [ 32 ]. Variation of temperatures in structures significantly influences the material properties (for example, Young’s modulus [ 32 ]), static characteristics (for example, deflection and deformation [ 32 ]), dynamic characteristics (for example, structural frequencies [ 33 , 34 , 35 ], damping ratios and mode shapes [ 36 ]) and boundary conditions [ 37 ]. Temperatures, including ambient air temperature and structural component temperature, of a multi-sensor of a structure are uncertain due to the fact that they are affected by not only the ambient factors, including air temperature variation, solar radiation intensity, humidity and wind speed, but also the complex processes of heat transfer [ 38 ].…”