ARIMA-BP neutral network-based bridge monitoring data prediction method

Abstract

The invention discloses an ARIMA-BP neutral network-based bridge monitoring data prediction method. The method comprises the following steps of 1) building an ARIMA(p,d,q) model of bridge monitoring data; 2) performing monitoring value estimation on a monitoring cycle of existent actual measurement data through the ARIMA(p,d,q) model to obtain an estimated value Ypn; performing monitoring value estimation on an expected prediction cycle of inexistent actual measurement data to obtain an estimated value Ynm; 3) obtaining a residual error of the monitoring data: performing subtraction on an actual value of the monitoring data and the estimated value Ypn of the ARIMA(p,d,q) to obtain a residual error Epn of a prediction result; 4) performing a process for predicting a residual error by a BP neutral network; 5) predicting a residual error in a prediction cycle through the established network to obtain an estimated value Enm; and 6) performing superposition on linear trend information predicted by the ARIMA(p,d,q) and a prediction result of the residual error by the BP network to obtain a combined prediction model result. According to the method, nonlinear information is effectively analyzed and a prediction error is relatively small.

Description

technical field [0001] The invention relates to the field of long-span bridge monitoring data analysis and processing, in particular to a prediction method for long-span bridge monitoring data. Background technique [0002] my country's "Measures for the Safety Operation and Management of Highway Long Bridges and Tunnels (Draft for Comment)" proposes that the safety operation management of national highways and provincial highway super-large bridges should implement the work policy of "safety first, prevention first", and recommends that the management and maintenance units adopt modern information technology , Gradually establish a safety monitoring system for the long bridge and tunnel, grasp the overall technical status and operating conditions of the long bridge and tunnel in a timely manner, and provide a basis for the operation management, maintenance and repair, reliability assessment and related scientific research of the long bridge and tunnel. In order to monitor th...

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Problems solved by technology

[0027] In order to overcome the shortcomings of the existing bridge monitoring data prediction methods that cannot analyze nonlinear information well and have large prediction errors, the present invention provides a bridge based on ARIMA-BP neural network that can effectively analyze nonlinear information and have small prediction errors Surveillance Data Prediction Method

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