Correlation analysis of long-span cable-stayed bridge strain response with environmental and operational variations based on feature selection

The structural response of large-span bridges is significantly influenced by environmental and operational conditions. This study employs a data-driven approach, which utilizes various techniques such as multiple linear regression, best subset regression, stepwise regression, Lasso regression, Ridge regression, and ElasticNet regression to analyze in detail the effects of environmental variables (temperature variables including temperature principal components, lateral temperature gradients, and vertical temperature gradients), and traffic loading on bridge strain response. Based on the analysis of 21 months of health monitoring data from a newly constructed cable-stayed bridge, Lasso regression is shown to perform best in predicting strains on the bridge deck and bottom slab. Furthermore, sensitivity analysis identifies several key influencing factors and quantifies their relative contributions to the strain. Although the 10-min average of the strain due to traffic loading has a minimal impact, the root mean square of the strain shows a significant correlation with the cumulative gross vehicle weight during busy traffic periods. The methodology proposed in this study helps to understand the strain response patterns of large-span bridges from a data perspective, and provides an effective approach to establishing a strain baseline model that eliminates environmental and operational variations.