Particle swarm optimization model to predict scour depth around bridge pier

TL;DR

This paper introduces new particle swarm optimization-based equations to predict scour depth around bridge piers, offering improved accuracy and clarity over traditional regression and black box models for both laboratory and field data.

Contribution

Develops novel equations using particle swarm optimization to predict scour depth, with separate models for laboratory and field data, enhancing accuracy and usability.

Findings

Proposed models outperform existing regression equations in accuracy.

Pier width to flow depth ratio and d50 to flow depth are key parameters.

Equations are applicable to both experimental and real-world scenarios.

Abstract

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. Existing methods to predict scour depth are mainly based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.