基于门控深度循环信念网络的边坡沉降预测

Slope settlement prediction based on gated deep recurrent belief network

本研究针对现有边坡沉降预测模型精度低、无法有效反映沉降值蕴含的时序信息等问题,提出基于门控深度循环信念网络(GDRBN)的边坡沉降混合预测模型。为提高训练效率,引入自适应学习率,并以广佛肇高速公路二期工程为实例,建立多种边坡沉降预测模型,并进行计算比较。研究结果表明:基于GDRBN的边坡预测模型的预测精度比GM、BP、RNN、DBN预测模型的分别提高了69%、54%、38%、26%,可为边坡预测提供更准确的计算方法。

The existing slope settlement prediction models have low accuracy and cannot effectively reflect the time series information contained in the settlement.A hybrid prediction model for slope settlement was proposed based on the gated deep recurrent belief network(GDRBN).The adaptive learning rate is introduced to improve the efficiency of model training.Taking the project of the second phase of Guangfo-Zhaoqing Expressway as an example,different settlement prediction models was established,and the outcomes were contrasted.The results show that the proposed models will increase the accuracy of the prediction by 69%,54%,38%,and 26%,respectively,which is compared with traditional prediction models(such as GM,BP,RNN,DBN).It can provide a accurate calculation method for slope prediction.