Expressway compactness real-time monitoring method based on artificial neural network

Abstract

The invention relates to a highway compactness real-time monitoring method based on an artificial neural network. The method comprises the following steps: training a neural network by using historical data to establish a compactness calculation model, considering filling body parameters, road roller control parameters and sensor acquisition parameters, and applying to compactness real-time calculation and real-time display in compaction construction. The prediction precision and the calculation efficiency are improved. The method comprises the following steps: acquiring historical data according to a data platform, training a compactness calculation model, calling the trained compactness calculation model into a road roller control system, before the road roller works, inputting measuredfilling body parameters before compaction and road roller control parameters into the compactness calculation model called by the road roller control system, and calculating the compactness of the road roller according to the compactness calculation model. The compaction degree is fed back in real time in combination with the road roller control parameters collected in the compaction process and the sensor acquisition parameters. According to the method, different working conditions of various application scenes are considered, conditions are created for establishment of a compaction whole-process monitoring system, and universality is achieved.

Description

technical field [0001] The invention relates to the technical field of roadbed and pavement engineering, in particular to the application of an artificial intelligence method to display the degree of compaction in the expressway compaction process, which can be used for monitoring the compaction quality of expressway roadbed and pavement. Background technique [0002] The compaction of expressways is one of the most important construction links in expressway construction, which directly has a substantial impact on the use and maintenance of later roads. Therefore, it is particularly important to control the degree of compaction during the compaction process. The calculation of degree is the basis of compaction quality control. The traditional road compaction quality control mainly relies on the driver's experience and feeling to adjust various parameters in the road compaction process. The quality control is difficult and subjective, which will cause a series of "insufficien...

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned deficiencies in the present situation, the technical problem to be solved by the present invention is to establish a method for real-time monitoring of compaction degree of expressway based on artificial neural network, which can be applied to real-time monitoring of compaction degree in the actual construction of expressway. A real-time monitoring of rolling parameters, using historical data to train the neural network to establish a correlation analysis model between the compaction measurement index and the degree of compaction, that is, the calculation model of the degree of compaction, considering the parameters of the filling body (intrinsic parameters) and the control parameters of the road roller and sensors to obtain parameters (real-time parameters), which are applied to real-time calculation and real-time display of compaction degree during compaction construction, improving prediction accuracy and calculation efficiency

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