Abstract
In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud‐stripe method. Taking the pier of a three‐span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.
Highlights
In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. is problem needs to be solved urgently
Based on machine learning and symbolic regression fusion algorithms, structure and fragility analysis of composite earthquake IMs for reinforced concrete bridge piers was presented in this article. e main conclusions were as follows: (1) e existing seismic parameters were sorted out and 46 common earthquake IMs were given, which were used to describe seismic wave characteristics
(2) Based on the ridge regression algorithm, communication with local agent (CLA) clustering algorithm, comprehensive algorithm including Kendall’s correlation coefficient (KCC), distance correlation coefficient (DCC), and maximal information coefficient (MIC), and symbolic regression method, the grouping and selection of seismic IMs were complied by MATLAB software. e functional relationship between D and composite IMs was obtained
Summary
E bridge deck adopted 3 × 20m prestressed concrete hollow slab beams. Elastic_Beam_Column element was used to simulate the hollow slab. Hinge Beam_Column element was used to simulate reinforced concrete piers. E others were simulated by Elastic_Beam_Column element without considering nonlinearity. In Formula (2), Ci was the seismic importance coefficient, and the importance coefficient of two-level fortification was 0.5 and 1.7, respectively; the site coefficient Cs was 1.0; the damping ratio of the structure was 0.05, the damping adjustment coefficient Cd was 1.0, and the peak acceleration A of horizontal basic ground motion was 0.05 g. Compared with the damage state criterion prosed by Strong, the damage state criterion considering the threedimensional extension of the criterion through experiments proposed by Guo et al could better reflect the real damage state of structures, which was suitable for reinforced concrete piers in this article
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