Toward reliable vibratory compaction control: Integrating full-scale testing and advanced finite element analysis

QC 260812

Abstract

Vibratory compaction is widely used for improving the bearing capacity and stiffness of earthworks. The quality of a compacted layer is traditionally verified by spot tests, such as the plate load test (PLT), which provide limited spatial coverage. Continuous compaction control (CCC) addresses this limitation by deriving intelligent compaction measurement values (ICMVs) from the roller response. However, the use of CCC for quality assurance is still limited by uncertain correlations between ICMVs and PLT moduli, by incomplete physical interpretation of the roller response, and by the small number of measurements normally available for calibration.

This thesis investigates the reliability of CCC-based quality assurance for vibratory compaction by combining full-scale testing, finite element analysis, and machine learning. Five full-scale trials were conducted at the Dynapac compaction laboratory in Karlskrona on a 1 m thick layer of well-graded gravel. Two single-drum rollers, two PLT plate diameters, and different compaction states were included, and sixteen ICMVs were compared with the deformation modulus from PLTs. A two-dimensional plane-strain finite element model of the roller–soil system was implemented with four constitutive descriptions, ranging from linear elasticity to hypoplasticity with intergranular strain. Finally, six regression algorithms were evaluated on 60 samples in a multioutput framework for estimating 𝐸𝑣1 and 𝐸𝑣2 simultaneously.

The full-scale trials show that mechanics-based ICMVs, in particular the loading-phase vibration modulus, provide the strongest correlation with 𝐸𝑣2. The 600 mm PLT plate gives stronger and less scattered correlations than the 300 mm plate, and the lighter roller gives stronger correlations than the heavier roller. The numerical study shows that hypoplasticity with intergranular strain gives the most consistent description of cyclic densification, stiffness evolution, and loading–unloading response. It also shows that the influence depth depends on the selected response quantity, with density-related criteria giving larger depths than settlement-based criteria. In the simulations, the vibration modulus is more closely related to the final density state than CMV or OMEGA, while normalized void-ratio reduction helps describe the consumed part of the available compaction potential. The machine-learning study shows that curated response characteristics improve the prediction of 𝐸𝑣2 compared with predefined ICMVs. Among the tested algorithms, TabPFN gives the best overall estimates of 𝐸𝑣2, while the multi-output framework also enables the 𝐸𝑣2/𝐸𝑣1 ratio to be estimated. The results demonstrate both the potential and the remaining limitations of CCC-based quality assurance for vibratory compaction.

Link to DiVA