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Sampling-simulation-based spatial analysis and strategy optimization for compaction quality assurance
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DOI：10.1016/j.asej.2026.104329.png)
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Abstract
En 中文
Compaction quality is crucial for geotechnical performance, but spatial autocorrelation introduces risks of misjudgment. This study presents a simulation-based framework to optimize sampling schemes for quality assurance. First, a practical algorithm is introduced to estimate the scale of fluctuation using the geostatistical range, confirming spatial autocorrelation. Subsequently, a Monte Carlo-based simulation method is developed to evaluate the impact of spatial autocorrelation on characterization accuracy. Ultimately, an a posteriori optimization strategy, based on Dempster–Shafer (D–S) evidence theory and requiring no prior knowledge, mitigates the spatial aggregation of sample points. The results reveal significant spatial autocorrelation in compaction quality. Spatial autocorrelation is a critical factor contributing to the risk of misjudgment, which can be amplified through its synergistic interaction with the spatial aggregation of sample points. The standalone application of spatial aggregation indicators risks yielding misjudgment, thereby necessitating the establishment of a conflict resolution mechanism and the integration of requisite multisource information.
Keywords:
Compaction quality assurance
Attribute sampling
Monte Carlo method
Spatial autocorrelation
Spatial aggregation indices
Dempster–Shafer (D–S) evidence theory
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