Evaluation of Compaction Quality Based on SVR with CFA: Case Study on Compaction Quality of Earth-Rock Dam

Comprehensive Evaluation for Real-Time Compaction Quality Using i-AHP and i-GAM: Case Study of Earth-Rock Dam

“…For example, based on the compaction parameters (i.e., rolling speed N s , compaction passes N p , vibration state N v and compaction thickness N t ) and material parameters (i.e., gradation M g and moisture content M m ), Liu et al [4] established a compactness prediction model to predict material compactness by using the multiple regression method. Wang et al estimated the compactness using support vector regression with bacterial foraging algorithm (SVR with BFA) [1] and the support vector regression with the chaotic firefly algorithm (SVR with CFA) [13] respectively, which improved the prediction accuracy to a great extent. In recent years, based on the RTCM technology, continuous measurement indices (CMI) were proposed as substitutes for physical indices.…”

“…To obtain the real-time physical and mechanical indices, two groups of independent variables are selected as the prediction inputs, respectively. The compaction parameters (i.e., rolling speed N s , compaction passes N p , vibration state N v and compaction thickness N t ) and the material parameters (i.e., gradation M g and moisture content M m ) are chosen to predict the distribution of material physical indices [1,4,13], and the drum vibration acceleration during roller travel is chosen to predict the distribution of material mechanical indices [19]. The SVR with CFA is employed to establish the compaction quality prediction model, and its efficiency and accuracy have been demonstrated in Reference [13].…”

“…The 3D compaction monitoring client will read above massages and visualize them as shown in Figure 3. The material parameters (i.e., M g and M m ) can be collected by the PDA collection system as illustrated in Reference [13]. Meanwhile, the acceleration amplitude spectrum can be obtained from the roller acceleration signal by Fast Fourier Transform [16], based on which the indices CMV and CCV can be acquired [28].…”

“…A 0.5Ω represents the first subharmonic, and A 1.5Ω , A 2.5Ω and A 3Ω denote the other higher-order harmonics. (3) Based on correlation analysis, the real-time physical indices across the working face can be predicted by compaction and material parameters [13]. Additionally, for the lack of a strict theoretical basis for, and physical significance of, CMV and CCV, the character parameters of the original acceleration amplitude spectrum (i.e., A 0.5Ω , A Ω , A 1.5Ω , A 2Ω , A 2.5Ω , A 3Ω ) are chosen as the original prediction inputs for the prediction of mechanical indices.…”

“…(3) Based on correlation analysis, the real-time physical indices across the working face can be predicted by compaction and material parameters [13]. Additionally, for the lack of a strict theoretical basis for, and physical significance of, and , the character parameters of the original acceleration amplitude spectrum (i.e., .…”