###### Abstract
As the roller's drum penetrates into the soil, the subgrade impedes roller's movement, leading to an increase in the energy required for forward motion. This additional energy consumption is directly correlated with the strength of the subgrade, such that greater subgrade strength results in less penetration and additional energy consumption [7]. Essentially, energetic method ICMV is a promotion of the net power required for a compact to pass through a compacted subgrade, without the need to obtain vertical vibration information from the roller [8]. This also makes energetic method ICMV suitable for various types of compactors [9]. Due to the ICMV are calculated by drum vibration response, it is incapable of directly measuring the density and stiffness of subgrade that have been used as standardized QC/QA indicator for decades in road construction industry [10], namely in-situ measurement values (ISMV). Therefore, to implement IC technology as a primary QC/QA measure of compaction, it is essential to establish the effective regression correlations between ICMV and ISMV (including compaction degree and subgrade modulus \(E_{\text{LWD}}\)) [11]. By using established regression correlations, the conventional ISMV can be replaced with the ICMV in construction, thereby reducing laborious efforts and enabling real-time control of subgrade quality. Furthermore, these correlations can assist road project headquarters in evaluating the potential reliance on IC technology for subgrade QC/QA and in developing equivalent-criteria specifications for the utilization of IC as a QC/QA tool [12; 13; 14; 15; 16]. For instance, the determination coefficient R\({}^{2}\) should be above 0.49 in Chinese/German specification or should be above 0.36 in Swedish specification [14; 17]. However, the regression correlations between ICMV and ISMV are complicated because the correlations are not only affected by roller parameters but also by subgrade properties [18; 19; 20; 21]. It primarily includes roller speed and roller vibration amplitude in terms of roller parameters. In terms of soil properties, it mainly involves moisture content and underlying soil stiffness [22]. Therefore, investigating the influence of above factors on the correlation between ICMV and ISMV will contribute to the further development of IC technology. The studies of IC technology have mainly focused on the monadic or multiple linear relationship between ICMV and ISMV so far [23; 24]. Nevertheless, when multiple parameters are considered, the effect of linear fitting is limited. In fact, the essence of all regression algorithms is to assign corresponding weights to each feature, and to iteratively adjust the weights through a loss function, so that the predicted result approaches the real value (Figure 1). In this circumstance, nonlinear regression algorithms can be introduced in IC technology application to solve the above issues [13; 25; 26; 27]. Nonlinear algorithms based on artificial neural networks (ANN) were widely used in the field of IC technology in the existing literature [13; 25; 28]. Since the model construction can be completed by adjusting the number of network layers and node parameters, the advantage of ANN is that it does not require manual feature engineering and can achieve satisfactory regression results. In fact, for intelligent compaction data, which is a kind of tabular data sets, the use of gradient boosting tree algorithms such as Light Gradient Boosting Model (LGBM) or extreme gradient boosting (XGBT) for processing often leads to superior results [29; 30]. Compared to linear regression algorithms, the advantage of nonlinear algorithms lies in their ability to fully consider the nonlinear impact mechanisms of the influence factors, resulting in predictions that are closer to the true values.