Particle swarm optimization model to predict scour depth around a bridge pier

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. The former approaches used in the prediction of scour depth are 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.     

Prediction of time‐dependent local scour around bridge piers

Most formulas for predicting the scour depth around bridge piers result in over‐predictions. This is partly due to ignoring the fact that the local scour is a time‐dependent process. Formulas that take into account time‐dependent local scour around bridge piers have been developed in the past. However, these formulas predict the scour process well, but only when applied to datasets from which they were constructed. Motivated by this, the present study proposes a relationship for time‐dependent local scour using dimensional analysis and nonlinear regression. Similarly, another relationship for the time to equilibrium is also developed. The findings indicate that the proposed relationship predicts time‐dependent local scour better than existing formulas. The proposed relationships are applicable to local scour around cylindrical bridge piers. It is assumed that the bed sediment is uniform and the bedform effect is minor and can be ignored.     

基于人工神经网络的土工合成材料加筋挡墙临界高度预测模型(英文)

提出了一种基于人工神经网络(ANN)技术的加筋挡墙设计高度预测方法。通过分析挡墙失效的原因,确定了7个主要因素作为网络的输入神经元。收集23组挡墙离心模型试验数据,2组足尺试验数据,1组实际工程的破坏数据,共26组样本作为训练及检验样本,建立了可用于加筋挡墙设计高度预测的径向基函数网络(RBFN)及误差反传网络(BPN)模型。结果表明径向基函数网络在学习速度,预测准确性及网络推广能力方面均优于BP网络,本文方法可用于加筋支挡结构的设计参考。