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

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...

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