ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.
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