There are large amounts of small-and medium-span girder bridges which bear structural similarity, while the large-scale bridge structures are generally limited in the timely applications of structural vibration characteristics. Therefore, in this study a framework based on machine learning models was proposed to analyze the vibration characteristics of specific line bridge groups. The probability distributions of structural, geometric, and material properties of bridge groups in specific lines were obtained using statistical tools and a Latin hypercube sampling method was used to generate reasonable sample sets for the bridges group, and parameterized finite element models of the bridges were established. Then, the optimal models were tuned and determined to predict fundamental mode and period by the 10-fold cross-validation method applying the numerical simulation results. This study’s results showed that the random forest models divided the vibration modes of the bridge groups into the longitudinal vibrations of the main girders and the longitudinal vibrations of the adjacent spans and side piers with a classification accuracy of greater than 90%, while the artificial neural network models exhibited the lowest normalized mean square error for the periods. The periods mainly ranged between 0.7 and 1.5 s. Furthermore, the bearing settings, ratios of the pier height to section diameters, and boundary types were determined to be the most significant properties influencing the fundamental modes and periods of the examined bridges, by respectively observing the reduced value of the random forest Gini indices and distribution of the generalized weight value of the input variables in artificial neural networks. This study provides an intelligent and efficient method for obtaining vibration characteristics of bridges group for a specific network.
Laminated rubber bearings are commonly adopted in small-to-medium span highway bridges in earthquake-prone areas. The accurate establishment of the mechanical model of laminated rubber bearings is one of most critical steps for the bridge seismic response analysis. A new constitutive model of bearing based on the artificial neural network (ANN) technique is established through the static cyclic test of laminated rubber bearings, considering the bearing initial stiffness, friction coefficient, and other parameters such as the bearing sectional area, height, loading velocity, vertical load, and aging time. Combined with the ANN method, the ANN-based bridge seismic demand model is built and applied to the rapid evaluation of the bridge seismic damage. The importance of the bearing affecting design factors in the bridge seismic demands are ranked. The results demonstrated that the dimensions of the bearing and vertical load are the main factors affecting the bearings constitutive model. Based on the partial dependency analysis with the ANN-based bridge seismic demand model, it is concluded that the height of bearing is the key design parameter which affects the bridge seismic response the most. The ANN seismic demands model can fit the complex function relationship between various factors and bridge seismic response with high precision, so as to achieve the rapid evaluation of bridge seismic damage.
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