Abstract
Seismic analysis of concrete‐filled steel tube (CFST) arch bridge based on finite element method is a time‐consuming work. Especially when uncertainty of material and structural parameters are involved, the computational requirements may exceed the computational power of high performance computers. In this paper, a seismic analysis method of CFST arch bridge based on artificial neural network is presented. The ANN is trained by these seismic damage and corresponding sample parameters based on finite element analysis. In order to obtain more efficient training samples, a uniform design method is used to select sample parameters. By comparing the damage probabilities under different seismic intensities, it is found that the damage probabilities of the neural network method and the finite element method are basically the same. The method based on ANN can save a lot of computing time.
Highlights
Concrete-filled steel tubular arch bridge is the most common type of highway bridge, which plays an important role in the traffic network
E dynamic response of concrete-filled steel tube (CFST) arch bridge under earthquake is very complex [2]. e seismic performance of the CFST arch bridge is influenced by various parameters [3, 4]. erefore, it is very difficult to consider both efficiency and accuracy in seismic damage analysis of CFST arch bridges. e seismic damage prediction model based on finite element method will face huge computational complexity, and the amount of calculation increases exponentially with the increase of parameters. e introduction of some new technologies may lead to new solutions. e artificial neural network (ANN) may be the most useful method [5]
Because this method is based on experiential data, ANNs can solve complex and nonlinear problems replace traditional time-consuming and low-efficiency seismic damage analysis methods [6, 7]
Summary
Concrete-filled steel tubular arch bridge is the most common type of highway bridge, which plays an important role in the traffic network. Erefore, it is very difficult to consider both efficiency and accuracy in seismic damage analysis of CFST arch bridges. An ANN can be regarded as an organic combination of a large number of artificial neurons; it can be used to learn the inherent laws of data Because this method is based on experiential data, ANNs can solve complex and nonlinear problems replace traditional time-consuming and low-efficiency seismic damage analysis methods [6, 7]. With the development of computer technology, ANNs are more widely used in earthquake damage prediction of bridges. 2. Artificial Neural Network e ANN can be used to predict the nonlinear systems of complex systems, and it is especially suitable for seismic damage identification of complex structures. K·m where Yij and Tij are the predicted value and the real value of the output, respectively; k is the number of patterns in the test data and m is the number of dimensions of the output vector
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