###### Abstract  
Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus \(E_{\text{LWD}}\)) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and \(E_{\text{vib}}\), were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and \(E_{\text{LWD}}\), were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction. intelligent compaction; influence factors; regression analysis; nonlinear algorithms +
Footnote †: journal: Medical Imaging  
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Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Medical Imaging  
(CCC) technology, can continuously assess the subgrade compaction quality through Intelligent Compaction Measurement Values (ICMV) integrated with positioning system to provide a complete compaction and geographic information of subgrade [3]. Through the utilization of IC technology, road projects headquarters can achieve more efficient performance of related QC/QA tasks during the construction of soil embankments. This technology effectively eliminates the limitations associated with traditional roller compaction inspection through in situ measurements. The ICMV, which are calculated based on the roller vibration signal, are consist of 3 types [4]: empirical method index (including compaction measurement value, CMV and continuous compaction value, CCV), energetic method index (mainly machine drive power, MDP) and mechanical method index (including vibration modulus \(E_{\text{vib}}\) and soil stiffness \(k_{\text{s}}\)). By means of the Fast Fourier Transform (FFT), time domain vibration signal of drum is transmitted into frequency domain, then the empirical method ICMV can be computed by the amplitude of harmonic. In field applications, the empirical method ICMV typically employs the average of the calculated results from two cycles in the domain curve. For example, when the roller's vibration frequency is 30 Hz, 5 empirical method ICMV can be calculated within a second [5]. For the mechanics perspective, the vibration drum and the compacted subgrade can be viewed as an interaction system during the vibration compaction process. By applying the kinematic equilibrium equation at every moment during the compaction, the vibration signal of the drum can be used to solve the material properties of the subgrade. This is the fundamental calculation principle of the mechanical method ICMV. In general, mechanical method ICMV is calculated on the basis of a two-degree-of-freedom model of vibratory compaction [6]. Energetic method ICMV is calculated by energy conservation principles.