Seismic Fragility Analysis of Structures Based on Adaptive Gaussian Process Regression Metamodel

Metamodel-based seismic fragility analysis methods can overcome the challenge of high computational costs of problems considering the uncertainties of earthquakes and structural parameters; however, the accuracy of metamodels is difficult to control. To enhance the efficiency of analyses without compromising accuracy, a metamodeling method using Gaussian process regression (GPR) and active learning (AL) for seismic fragility analysis is proposed. In this method, a GPR metamodel is built to estimate the stochastic seismic response of a structure, in which the record-to-record variability is considered as in the dual-metamodel-based fragility analysis approach. The metamodel can also predict the estimation error. Taking advantage of this ability, we present an AL strategy for adaptive sampling, so that the metamodel can be improved adaptively according to the problem. Using this metamodel and Monte Carlo simulation, seismic fragility curves can be obtained with a small number of calls for time history analysis. To verify its effectiveness, the proposed method was applied to three examples of nonlinear structures and compared with existing methods. The results show that this method has high computational efficiency and can ensure the accuracy of fragility curves without making the metamodel globally accurate.

Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure

The Concrete Damaged Plasticity (CDP) constitutive is introduced to study the dynamic failure mechanism and the law of damage development to the aqueduct structure during the seismic duration using a large-scale aqueduct structure from the South-to-North Water Division Project (SNWDP) as a research object. Incremental dynamic analysis (IDA) and multiple stripe analysis (MSA) seismic fragility methods are introduced. The spectral acceleration is used as the scale of ground motion record intensity measure (IM), and the aqueduct pier top offset ratio quantifies the limit of structural damage measure (DM). The aqueduct structure’s seismic fragility evaluation curves are constructed with indicators of different seismic intensity measures to depict the damage characteristics of aqueduct structures under different seismic intensities through probability. The results show that penetrating damage is most likely to occur on both sides of the pier cap and around the pier shaft in the event of a rare earthquake, followed by the top of the aqueduct body, which requires the greatest care during an earthquake. The results of two fragility analysis methodologies reveal that the fragility curves are very similar. The aqueduct structure’s first limit state level (LS1) is quite steep and near the vertical line, indicating that maintaining the excellent condition without damage in the seismic analysis will be challenging. Except for individual results, the overall fragility results are in good agreement, and the curve change rule is the same. The exceedance probability in the case of any ground motion record IM may be estimated using only two factors when using the MSA approach, and the computation efficiency is higher. The study of seismic fragility analysis methods in this paper can provide a reference for the seismic safety evaluation of aqueducts and similar structures.

Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms

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.