Tunnel leakage rate prediction method based on neural network

A prediction method and neural network technology, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve problems such as tunnel structure safety risk prediction, leakage rate prediction and analysis, etc., to achieve rich results and operational efficiency high effect

Active Publication Date: 2020-05-12
SHANGHAI RAIL TRANSIT MAINTENANCE SUPPORT
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Problems solved by technology

[0006] The above-mentioned patents all use machine learning methods to detect and study tunnel water leakage diseases. Although the automatic identification of water leakage diseases is realized, the rate of water leakage is not predicted and analyzed, and the risk prediction of tunnel structural safety cannot be carried out.

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  • Tunnel leakage rate prediction method based on neural network
  • Tunnel leakage rate prediction method based on neural network
  • Tunnel leakage rate prediction method based on neural network

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Embodiment Construction

[0026] based on the following Figure 1 ~ Figure 3 , specifically explain the preferred embodiment of the present invention.

[0027] Such as figure 1 As shown, the present invention provides a neural network-based tunnel leakage rate prediction method, comprising the following steps:

[0028] Step S1, collecting tunnel images related to the flow rate of seepage water to form a data set, the data set includes a training data set, a test data set, and a prediction data set;

[0029] Step S2, constructing a tunnel leakage rate prediction model based on a convolutional neural network and a long-short-term memory network;

[0030] Step S3, using the training data set and the test data set to train the tunnel leakage rate prediction model;

[0031] Step S4. Input the prediction data set into the trained tunnel leakage rate prediction model to obtain the leakage water velocity corresponding to the tunnel image.

[0032] Further, the method for collecting data sets includes:

[...

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Abstract

The invention discloses a tunnel leakage rate prediction method based on a neural network. According to the method, a tunnel leakage rate prediction model based on a convolutional neural network and along-term and short-term memory network is constructed; tunnel three-dimensional laser scanning images are taken as a data source; the tunnel leakage rate prediction model is trained by using a training data set and a test data set; a prediction data set is inputted into the trained tunnel leakage rate prediction model, so that a leakage water flow rate corresponding to the tunnel image can be obtained. According to the method, tunnel diseases are detected by utilizing a three-dimensional laser technology, and therefore, operation efficiency is high, achievements are rich, and the tunnel leakage water flow velocity prediction model based on the convolutional neural network and the long-short-term memory network is established to predict and analyze the rate of leakage water.

Description

technical field [0001] The invention relates to the field of tunnel disease detection, in particular to a tunnel leakage rate prediction method based on a convolutional neural network and a long-short-term memory network. Background technique [0002] Due to changes in natural conditions (groundwater, materials, strata, freeze-thaw, etc.), various variations (such as cracks, dislocations, etc.) occur in the tunnel structure, resulting in direct or indirect groundwater or surface water in the surrounding rocks in the form of leakage or gushing. Forms enter the tunnel, forming tunnel leakage disease, eroding the tunnel structure, and affecting the normal operation of the tunnel and the use of equipment in the cave. [0003] At present, the investigation of leakage diseases in tunnels mainly adopts the methods of manual patrolling, photographing and on-site recording, focusing on the area of ​​seepage and the rate of leakage. The workload is heavy, the efficiency is low, and it...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N15/08G06N3/04G06N3/08
CPCG01N15/0826G06N3/08G06N3/044G06N3/045Y02A90/30
Inventor 李筱旻邹文豪卫追沈玺沈佳雨王嘉鸿周群
Owner SHANGHAI RAIL TRANSIT MAINTENANCE SUPPORT
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