Data-based models to investigate protective piles effects on the scour depth about oblong-shaped bridge pier

Abstract

The primary objective of this study is to examine the suitability of employing a machine learning models (MLMs) comprising Support Vector Machine (SVM), Gene Expression Programming (GEP), and Artificial Neural Network (ANN) algorithms to emulate the influence of geometric and geotechnical parameters of the piles on the scour depth reduction about the oblong-shaped bridge pier. A dataset consisting of 90 laboratory observations was utilized, allocating 70 % of the data for the training and 30 % for the testing the MLMs. An oblong-shaped bridge pier of diameter D, was positioned and subjected to a rectangular flume featuring an erodible bed comprising sand sediment particles of D50 = 1.7 mm under clear water and subcritical flow conditions. The spacing between the piles, LD, the angle of pile orientation with respect to the flow, θ, and the Froude number, Fr, were identified as independent parameters through the dimensional analysis and Buckingham-Π theory. The MLMs’ performance were assessed employing root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The outcomes, affirming the predictive capacity of all MLMs included, revealed that the GEP model employing a three-gene configuration exhibited superior accuracy compared to the other two MLMs. Specifically, the performance indicators (RMSE, MAE, R2, DDR) during the training and testing phases were (0.0433, 0.033, 0.981, 6.8) and (0.054, 0.045, 0.958, 4.81) respectively. The sensitivity analysis elucidated the hierarchy of influential variables as follows: Fr, LD and θ.

Keywords

• Machine learning models
• Bridge failure
• Pile
• Sensitivity analysis
• Performance assessment