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Bridge temperature prediction method based on long-term and short-term memory network, medium and equipment

A technology of long and short-term memory and prediction method, applied in the field of bridges, can solve the problems of structural modeling and model update easily getting into trouble, and achieve the effects of accurate peak prediction, back propagation avoidance, and good robustness.

Pending Publication Date: 2021-03-05
河南省高速公路联网管理中心 +1
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Problems solved by technology

[0003] Traditional time series forecasting methods have a high dependence on the selection of parameter models, and are prone to get stuck in structural modeling and model updating. Therefore, data-driven methods have attracted widespread attention in recent years.

Method used

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  • Bridge temperature prediction method based on long-term and short-term memory network, medium and equipment
  • Bridge temperature prediction method based on long-term and short-term memory network, medium and equipment
  • Bridge temperature prediction method based on long-term and short-term memory network, medium and equipment

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Experimental program
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Embodiment

[0058] The temperature data of the road surface at 1-4 north of the main span of the main bridge of the Sutong Bridge is used, and the data collection frequency is 10 times per second. Since the temperature changes periodically in days, in order to better combine the periodicity of the data and minimize the negative impact caused by the long input sequence, the data is sampled and screened, and the data is selected as 1 time / hour (choose median). The final experiment uses the data within 10 days as a single sample, and the input sequence length is 8*24. The early warning window is 24 hours to ensure sufficient time for processing after the early warning.

[0059] see Figure 4 , apply the trained model to the actual prediction, the prediction results are as follows Figure 4 shown. It can be seen from the prediction results that the LSTM model can better predict the future temperature trend under the guaranteed 24-hour early warning window. Especially in the prediction of...

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Abstract

The invention discloses a bridge temperature prediction method based on a long-term and short-term memory network, a medium and equipment. The method comprises the steps of dividing a bridge temperature data set into a training set, a verification set and a test set in proportion; constructing a network model based on Keras, and training the network model by using the training set and the verification set to obtain a prediction model; and sending the test set data into a prediction model, and carrying out early warning processing when an abnormal value is found in a prediction window. According to the method, the time window is divided, sufficient early warning time is provided to solve the discovered problem, and the neural network prediction model is more accurate and stable in peak prediction.

Description

technical field [0001] The invention belongs to the technical field of bridges, and in particular relates to a bridge temperature prediction method, medium and equipment based on a long-term and short-term memory network. Background technique [0002] As an important infrastructure, bridge is a complex system with extremely high requirements on safety and reliability. Since the 1980s, health monitoring technology has been introduced into bridge engineering and has become an important supplement to traditional manual inspection methods. So far, there is still a large gap between the informatization level of China's highway bridges and the world's first-class level. There are still some problems to be solved in the structural health monitoring system for large and medium-sized highway bridges, among which the monitoring and early warning of bridge status is the top priority. However, in order to achieve timely early warning of bridge status, it is not enough to rely solely on...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06Q10/04G06N3/04G06N3/08
CPCG06F30/27G06Q10/04G06N3/084G06N3/044G06N3/045Y02D10/00
Inventor 张建龙束景晓郑旭达赵东月傅磊王韶鹏王一戈崔潇
Owner 河南省高速公路联网管理中心
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