A real-time monitoring method of expressway compaction degree based on artificial neural network

Abstract

The invention is a real-time monitoring method of expressway compaction degree based on artificial neural network. The method uses historical data to train neural network to establish a compaction degree calculation model, and considers filling body parameters, road roller control parameters and sensor acquisition parameters, and is applied to Real-time calculation and real-time display of compaction degree during compaction construction to improve prediction accuracy and calculation efficiency. Obtain historical data according to the data platform, train the compaction degree calculation model, and transfer the trained compaction degree calculation model to the roller control system. The parameters are input into the compaction degree calculation model transferred by the roller control system, combined with the roller control parameters and sensor acquisition parameters collected during the compaction process, the compaction degree is fed back in real time. This method considers the different working conditions of various application scenarios, and creates conditions for the establishment of the whole process monitoring system of compaction, which is universal.

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 compaction degree, that is, the compaction degree calculation model, considering the parameters of the filling body, the control parameters of the road roller and the acquisition parameters of the sensor , The parameters of the filling body are inherent parameters, the control parameters of the road roller and the parameters obtained by the sensor are real-time parameters, which are applied to the real-time calculation and real-time display of the degree of compaction in the compaction construction, improving the prediction accuracy and calculation efficiency

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