Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning

Abstract

Continuous and real-time tension force monitoring is a key point in fatigue damage evaluation for bridge suspenders or hangers. Usually, effective sensors are not equipped in suspenders or hangers of in-service bridges to obtain tension force responses. Bridge-site-specified traffic loading information collected by Weigh-in-motion (WIM) system offers an opportunity to address this issue. The daily fatigue damage of hangers can be estimated by combination of the traffic loading data with finite element analysis. Support vector machine (SVM) is adopted to establish the regression models between daily fatigue damage and collected traffic loading parameters. Consequently, the future fatigue damage of cables or hangers can be predicted by feeding the subsequent WIM data into the regression models. This proposed method is validated in the fatigue life prediction of hangers on a suspension bridge. The SVM model configuration and generalisation ability are investigated in this study. This study presents a novel way to estimate the fatigue damage of the hanger without direct stress sensing equipment and provides new thoughts in interpreting the monitoring data to provide useful information for engineering decision makers.

Keywords:

• Fatigue damage
• hanger
• structural health monitoring
• support vector machine
• suspension bridge
• traffic load

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported, in part, by Grant from the National Natural Science Foundation of China (Project 51878027), Beijing Municipal Education Commission (CIT&TCD201904060 and KM201910016013) and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (X18004). These supports are gratefully acknowledged.