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Case Study: Finite Element Method and Artificial Neural Network Models for Flow through Jeziorsko Earthfill Dam in Poland

机译:案例研究:流经波兰Jeziorsko填土坝的水流的有限元方法和人工神经网络模型

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摘要

A finite element method (FEM) and an artificial neural network (ANN) model were developed to simulate flow throughJeziorsko earthfill dam in Poland. The developed FEM is capable of simulating two-dimensional unsteady and nonuniform flow through a nonhomogenous and anisotropic saturated and unsaturated porous body of an earthfill dam. For Jeziorsko dam, the FEM model had 5,497 triangular elements and 3,010 nodes, with the FEM network being made denser in the dam body and in the neighborhood of the drainage ditches. The ANN model developed for Jeziorsko dam was a feedforward three layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the ANN model. The two models were calibrated and verified using the piezometer data collected on a section of the Jeziorsko dam. The water levels computed by the models satisfactorily compared with those measured by the piezometers. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM model. This case study offers insight into the adequacy of ANN as well as its competitiveness against FEM for predicting seepage through an earthfill dam body.
机译:开发了有限元方法(FEM)和人工神经网络(ANN)模型来模拟流经波兰Jeziorsko土坝的水流。开发的有限元软件能够模拟流过土坝非均质各向异性各向异性饱和和不饱和多孔体的二维不稳定和非均匀流动。对于Jeziorsko大坝,FEM模型具有5,497个三角形单元和3,010个节点,FEM网络在大坝主体和排水沟附近变得更加密集。为Jeziorsko大坝开发的ANN模型是前馈三层网络,采用S形函数作为激励器,并采用反向传播算法进行网络学习。大坝上游和下游侧的水位是输入变量,而压力计中的水位是ANN模型中的目标输出。这两个模型使用在Jeziorsko大坝截面上收集的压计数据进行了校准和验证。通过模型计算出的水位与通过压力计测得的水位相比令人满意。模型结果还显示,ANN模型的性能与FEM模型一样好,在某些情况下还优于FEM模型。该案例研究提供了关于人工神经网络的充分性及其在预测通过填土坝体渗流方面与有限元方法的竞争性的见解。

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