Development of seismic demand prediction models for bridges based on probability approach using symbolic regression method

•Developing seismic demand prediction models via SR methods in a probabilistic manner.•Proposing the MGGP and MBBP based models for seismic demand prediction.•Considering aleatoric and epistemic uncertainties for SR model development.•Using evolutionary correlation coefficient for the feature selection process.•Using closed-form mathematical expressions for generating bridge fragility curves. The resilience assessment of existing structures and designing new resilient ones need to have a precise estimation of seismic responses of the structures traditionally carried out by time-consuming calculations of finite element method. This paper presents interpretable, reliable, and fast prediction models developed by two novel branches of the symbolic regression approach (multigene genetic programming and multi biogeography-based programming) in a probability manner for curved bridges. These newly developed techniques seek to find interpretable equations with arbitrary forms that conform to a specific dataset. The reliable prediction models were developed considering the different uncertainties involved, including mechanical, geometrical, structural, and seismic uncertainties. Due to a large number of input variables for the bridge, the evolutionary correlation coefficient was employed to identify the most influential parameters for seismic demands of bridge components. Parameters with the highest correlation were set as inputs for symbolic regression algorithms, generating closed-form mathematical expressions to predict seismic demands. The resulting explicit models present a fast and accurate model that do not require further simulation or the seismic demand model assumptions to develop bridge fragility curves. In comparison to cloud approach and multiple strips analysis, the new method generates fragility curves with less dispersion.