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• W4385648575 abstract "The intelligent compaction technique uses the longitudinal acceleration signal of the roller’s vibrating steel wheel to judge the soil’s compaction quality. The low-frequency component of the signal is used to identify the surface stiffness of the soil and, thus, indirectly estimate the degree of compaction. High-frequency components are considered noise. However, the high-frequency component can reflect the intensity of the collisions between particles. Therefore, the high-frequency component may be more effective than the low-frequency component in evaluating the compaction quality of deep soil. This study establishes a nonlinear model using Morse wavelet transform and deep neural network to evaluate the compaction quality. The influence of high and low-frequency components on evaluation results is analyzed by controlling the frequency band range of input. The results show that, compared with the low-frequency component, the high-frequency component can more accurately evaluate the degree of soil compaction. In order to eliminate the influence of the “double jump phenomenon” of the roller, high-frequency and low-frequency components should be considered simultaneously. This method can not only accurately distinguish between under-compaction and over-compaction but also has the potential to take the actual degree of soil compaction as output." @default.
• W4385648575 created "2023-08-09" @default.
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• W4385648575 date "2023-10-01" @default.
• W4385648575 modified "2023-10-12" @default.
• W4385648575 title "Intelligent compaction quality evaluation using Morse wavelet transform and deep neural network" @default.
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• W4385648575 doi "https://doi.org/10.1016/j.conbuildmat.2023.132697" @default.
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