Dam deformation prediction system and method based on convolutional neural network

A convolutional neural network and prediction system technology, applied in the field of dam deformation prediction, can solve different problems, achieve the effects of improving efficiency and speed, improving automation level, improving effectiveness and accuracy

Active Publication Date: 2020-05-05
HOHAI UNIV CHANGZHOU
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  • Abstract
  • Description
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Problems solved by technology

[0006] However, although the dam deformation prediction technology disclosed by the above-mentioned technologies is helpful to improve the prediction efficiency, the following problems still exist: there are many factors that affect the dam deformation in reality, and the main factors in different ...

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  • Dam deformation prediction system and method based on convolutional neural network
  • Dam deformation prediction system and method based on convolutional neural network
  • Dam deformation prediction system and method based on convolutional neural network

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Embodiment

[0071] Embodiment: a method for predicting deformation of a dam based on a convolutional neural network, comprising: collecting datum body reference point data;

[0072] Input the input data set into the pre-trained convolutional neural network, and output the prediction result of the dam deformation;

[0073] The convolutional neural network includes a data set building module that selects an input data set for constructing a convolutional neural network, a feature extraction module for extracting features, and a prediction module for dam prediction deformation;

[0074] The feature extraction module includes at least a first feature extraction path and a second feature extraction path, and the first feature extraction path includes at least one feature extraction module (feature extraction module such as Figure 7 Shown in (a), the feature extraction module is a convolutional layer and a pooling layer alternate structure, and the second feature extraction path is only cascad...

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Abstract

The invention discloses a dam deformation prediction system and method based on a convolutional neural network. According to the convolutional neural network for dam deformation prediction, the network parallel computing capacity is fully used, two-dimensional data is input into multiple paths in parallel, multiple convolutional layers and pooling layers are alternately cascaded in each path, andin order to obtain more characteristics, different convolutional kernel sizes can be set; a simple path only cascading a convolution layer and a pooling layer is added to extract features different from other paths, and meanwhile, the features extracted by the paths are not necessarily complementary, so that additive operation is used in the system when output results of the paths are combined. According to the method, the automation level of dam deformation prediction is improved, and the efficiency and speed of deformation prediction are improved.

Description

technical field [0001] The invention belongs to the field of dam deformation prediction, in particular to a convolutional neural network-based dam deformation prediction system and method. Background technique [0002] In order to make full use of water resources and alleviate the imbalance of water resources in space and time, my country has built more than 85,000 dams of various types. These dams have produced huge economic benefits in the fields of irrigation, flood control, and power generation. Especially with the gradual deepening of projects such as the Western Development, South-to-North Water Diversion, and West-to-East Electricity Transmission, the dams have played an extremely important role. However, with the passage of time, internal and external factors of the dam (environment, basic structure) have gradually changed, and some dam bodies may be deformed to varying degrees, which will bring huge casualties and economic losses to the downstream. [0003] Therefo...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/048G06N3/045
Inventor 陈俊风王玉浩王家豪杜静静张学武
Owner HOHAI UNIV CHANGZHOU
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