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)

Artificial neural networksbased-model for forecasting critical height of GRW

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。

This paper presents an artificial neural networksbased approach for predicting the critical height of GRW. Seven major affecting factors have been used for analyzing the general failure cause. A radial basis function neural network (RBFN), as well as a back propagation neural network (BPN) for comparison, is trained and tested using 23 series of centrifuge model test data, 2 fullscale test data, and prototype date of a practical project. The modeling results indicated that the RBFN is much better than the BPN on learning speed, prediction accuracy and generalization ability. The paper provides a reference for GRW design.