This work describes a vibration-based structural health monitoring of a prestressed-concrete box girder bridge on the A100 Highway in Berlin by applying statistical pattern recognition technique to a huge amount of data continuously collected by an integrated monitoring system during the period from 2000 to 2013. Firstly, the general condition and potential damage of the bridge is described. Then, the dynamic properties are extracted from 20 velocity sensors. Environmental variability captured by five thermal transducers and traffic intensity approximately estimated by strain measurements are also reported. Nonlinear influences of temperature on natural frequencies are observed. Subsequently, the measurements during the first year are used to build a baseline health index. The multiple linear regression (MLR) method is used to characterize the nonlinear relationship between natural frequencies and temperatures. The Euclidean distance of the residual errors is calculated to build a statistical health index. Finally, the indices extracted from the following years gradually deviate; which may indicate structural deterioration due to loss of prestress in the prestressed tendons.

In this study, the mechanism of the effect of temperature on structural frequency is investigated by integrating correlation analysis, numerical simulation, and neural network techniques. First, the different spatial–temporal influence patterns of the temperature field on frequency are observed using correlation analysis based on the long-term monitoring of an arch bridge. Subsequently, a numerical simulation is performed to quantitatively analyze the effect of temperature on frequency in terms of internal force, geometric size, elastic modulus, and boundary condition. It is observed that an unknown factor—except for the elastic modulus—leads to variation in the frequency of the beam mode. The spatial effect of the temperature field is separated by a genetic-algorithm-optimized backpropagation neural network. It is inferred that the frequency of the beam mode is influenced in terms of the boundary condition and the elastic modulus because of temperature, whereas the frequency of the arch mode is only influenced by the changing elastic modulus.

The implementation of continuous dynamic monitoring systems in two bridges, in Portugal, is enabled to detect the occurrence of very significant environmental and operational effects on the modal properties of these bridges, based on automated processing of massive amounts of monitoring data collected by a set of accelerometers and thermal sensors over several years. In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different statistical methods have been adopted. One of them is the multiple linear regression by performing nonlinear correlation analysis between measured modal properties and environmental/operational variables. Another one is principal component regression based on the identification of the linear subspace within the modal properties without using measured values of environmental and operational variables. This paper presents a comparison of the performance of these two alternative approaches on the basis of continuous monitoring data acquired from two instrumented bridges and simulated damage scenarios. It is observed that different methods show similar capacity in removing environmental effects, and the multiple linear regression method is slightly more sensitive to structural damage.