Data-driven modeling of long temperature time-series to capture the thermal behavior of bridges for SHM purposes

Bridges experience complex heat propagation phenomena that are governed by external thermal loads, such as solar radiation and air convection, as well as internal factors, such as thermal inertia and geometrical properties of the various components. This dynamics produces internal temperature distributions which cause changes in some measurable structural responses that often surpass those produced by any other load acting on the structure or by the insurgence or growth of damage. This article advocates the use of regression models that are capable of capturing the dynamics buried within long sequences of temperature measurements and of relating that to some measured structural response, such as strain as in the test structure used in this study. Two such models are proposed, namely the multiple linear regression (MLR) and a deep learning (DL) method based on one-dimensional causal dilated convolutional neural networks, and their ability to predict strain is evaluated in terms of the coefficient of determination R 2. Simple linear regression (LR), which only uses a single temperature reading to predict the structural response, is also tested and used as a benchmark. It is shown that both MLR and the DL method largely outperform LR, with the DL method providing the best results overall, though at a higher computational cost. These findings confirm the need to consider the evolution of temperature if one wishes to setup a temperature-based data-driven strategy for the SHM of large structures such as bridges, an example of which is given and discussed towards the end of the article.</p

Reliability Assessment for PSC Box-Girder Bridges Based on SHM Strain Measurements

A reliability assessment method for prestressed concrete (PSC) continuous box-girder bridges based on structural health monitoring (SHM) strain measurements was proposed. First, due to the fact that measured strain was compositive and the variation periods of its components were different, a series of limit state equations under normal use limit state were given. Then, a linear fitting method was used to determine the relationship between the ambient temperature and the measured strain, which was aimed at extracting the vehicle load effect and the temperature load effect from the measured strain. Finally, according to the equivalent normalization method, the load effects unsatisfying the normal distribution by probability density function fitting were transformed, and the daily failure probabilities of monitored positions were calculated for evaluating the safety state of the girder. The results show that (1) the top plate of the box girder is more sensitive than the bottom plate to the high temperature, (2) the daily and seasonal strain variations induced by uniform temperature reveal an inconsistent tendency to the seasonal variation for mid-span cross sections, and (3) the generalized extreme value distribution is recommended for temperature gradient stress and vehicle induced stress fitting for box-girder bridges