Nonlinear Uncertainty Modeling between Bridge Frequencies and Multiple Environmental Factors Based on Monitoring Data

Abstract

Due to the influence of multiple environmental factors, bridge frequencies can vary with time, which can affect the frequency variations that cause structural damage. However, the nonlinear effects of multiple environmental factors and other uncertain effects on structural frequencies cannot be properly considered, which is a major obstacle to achieving bridge damage detection based on structural frequency variations. Therefore, this paper focuses on establishing an appropriate mapping model between modal frequencies and multiple environmental factors, which can consider such nonlinear and uncertain effects simultaneously. Principal component analysis integrates the features of long-term environmental monitoring data into several principal components. To address nonlinearity and uncertainty in modeling, a Gaussian process regression model with principal components as inputs is developed to estimate the modal frequency distributions. Four groups of models with different inputs are validated in a cable-stayed bridge case. The proposed modeling method can map multiple environmental factors onto modal frequencies by considering both nonlinearity and uncertainty and accurately describe the environmental impacts on frequencies based on monitoring data.