A nonparametric seismic reliability analysis method based on Bayesian compressive sensing-Stochastic harmonic function method and probability density evolution method

In engineering practice, the evaluation of seismic reliability of high-rise reinforced concrete structure relies on the probabilistic modelling of material properties. However, the measured data of material parameters in engineering practice is usually inadequate to determine the probability model accurately, especially for the problems involving spatial variability. Traditional parametric reliability analysis methods can be biased for this kind of problem. Therefore, a nonparametric seismic reliability analysis method is proposed based on the Bayesian compressive sensing - stochastic harmonic function method and the probability density evolution method. In this method, the conditional random fields are generated and applied to represent material properties of concrete. As a result, the seismic reliability of a high-rise reinforced concrete shear wall model structure is analyzed using the probability density evolution method.

Automated summary

In engineering practice, the seismic reliability of high-rise reinforced concrete structures is often evaluated using probabilistic modeling of material properties. However, the availability of measured material parameter data is usually insufficient for accurate probability modeling, particularly in situations involving spatial variability. Traditional parametric reliability analysis methods may be biased for such problems. Therefore, a nonparametric seismic reliability analysis method is proposed based on the Bayesian compressive sensing - stochastic harmonic function method and the probability density evolution method. This method utilizes conditional random fields to represent the material properties of concrete and analyzes the seismic reliability of a high-rise reinforced concrete shear wall model structure using the probability density evolution method.