A novel copula-based probabilistic model is proposed to establish the temperature difference analysis model for a long-span suspension bridge’s steel box girder. The key idea is to express a two-dimensional function of the temperature difference in flat steel box girder by using copulas. The maximum and minimum values of daily temperature difference model was developed using long-terms structural health monitoring data. Then, the correlation between adjacent temperature differences is investigated using five types of copulas. Akaike information criterion (AIC) is used to select an optimal model from five types of copulas, and the optimal joint function (two-dimensional function) for steel box girder’s temperature difference is established. Finally, the structure’s temperature gradient model is extrapolated for the service life of the structure by using Monte Carlo method. Moreover, this paper discusses the temperature gradient models using five types of common copulas and four types of time-varying copulas. The result shows that the t-copula is the optimal function to build the two-dimensional functions for steel box girder’s temperature difference, and the temperature model along the transverse direction can offer useful information that is not available in the design codes.

This paper discusses the application of a connected pipe system (CPS) to a suspension bridge in China for the purpose of vertical deflection monitoring and assessment. The CPS mainly consists of three main parts: the pressure transmitters to detect the height change of liquid, the pipes to connect the reference point and measurement points, and the liquid to fill the connected pipes. Multiple pressure transmitters, taken as the measurement point, were mounted inside of the girder. A reference transmitter was mounted inside of a tower, taken as a fixed point. To verify the performance and reliability of the CPS, a controlled load test after the completion of the bridge construction and an uncontrolled load test under the actual traffic loading were carried out. The results showed that the CPS exhibited excellent capability in real‐time measurement of vertical deflection of the suspension bridge. With the long‐term deflection data monitored by the CPS, the extreme deflections with respect to vehicle loads were predicted by extreme value analysis. The generalized extreme value distribution was adopted to establish the probability models of the daily maximum and minimum deflection sequences, and then the extreme deflections within the design reference period of 100 years were determined based on the probability models. Comparison of the extreme deflections and the deflection thresholds was carried out. The deflection monitoring and assessment method proposed in this paper have shown high potential of applicability in the practice of health monitoring for long‐span bridges. Copyright © 2015 John Wiley & Sons, Ltd.

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.