Stochastic analysis for bending capacity of precast prestressed concrete bridge piers using Monte-Carlo simulation and gradient boosted regression trees algorithm

Abstract The use of precast prestressed concrete bridge piers is rapidly evolving and widely applied. Nevertheless, the probabilistic behavior of the bending performance of precast prestressed concrete bridge piers has often been overlooked. This study aims to address this issue by utilizing actual precast bridge piers as the engineering context. Through the implementation of the Monte-Carlo simulation and Gradient Boosted Regression Trees (GBRT) algorithm, the stochastic distribution of the bending performance and their critical factors are identified. The results show that the normal distribution is the most suitable for the random distribution of bending performance indicators. The variability of the elastic modulus of ordinary steel bars, initial strain of prestressed steel hinge wires, and constant load axial force has little effect on the bending moment performance, while the yield stress of ordinary steel bars, elastic modulus of concrete, compressive strength of unrestrained concrete, and elastic modulus of prestressed steel hinge wires have a greater impact on the bending performance. Additionally, the compressive strength of unrestrained concrete has a significant influence on the equivalent bending moment of the cross-section that concerns designers