In order to analyse the changing quality evaluation indexes in the compaction process of cement-stabilised gravel subgrade, and to analyse its change mechanism from the perspective of its calculation principle, the secondary development of Abaqus finite element analysis software was further carried out by writing USDFLD subroutine; at the same time, the experimental data of large-thickness water-stabilised layer compaction were brought into the finite element software analysis to realise the The data from the large-thickness water-stabilized layer compaction experiments were brought into the FEA software to analyze the dynamic changes in material parameters and strain changes during the cement-stabilized subgrade compaction process, effectively improving the accuracy of the numerical simulation results. This will provide a comprehensive understanding of the changes in the compaction of the subgrade during the vertical vibratory rolling process, which is of practical significance for the analysis of the subgrade rolling process and the reduction of damage caused by compaction testing.
In road construction, the compaction of the large-thickness water-stable layer is mainly done in layers. However, the construction period is long, and the integrity of the water-stable layer after compaction is not good. To determine the maximum false pavement thickness and rolling combination that can be compacted at one time by LSV220 single drum vertical vibratory roller. In this paper, two test sections with a virtual pavement thickness of 100 cm were set up to bury the sensors in layers. Through the sensor and sand filling method, the maximum thickness of the compacted pavement is 47cm, and the combination of rolling is "one static compaction, one Weak vibration, and three Strong vibrations" The BP neural network was constructed using the experimental data was used to predict the compaction by sensor data. The error between the predicted and measured values of this neural network was verified to be 1.44%.
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