Soil Particle Size Estimation via Optical Flow and Potential Function Analysis for Dam Seepage and Building Monitoring

Soil particle size distribution is a critical parameter in geotechnical and hydraulic engineering, particularly in applications such as dam seepage monitoring, building foundation assessments, and sediment transport. This study presents a novel algorithm for estimating soil particle sizes by analyzing their falling velocities in water, combining optical flow computation with chaotic motion analysis. To address the limitations of the classical Horn and Schunck method, particularly its sensitivity to large displacements and brightness variations, we introduced a coarse-to-fine warping strategy, an image decomposition step to separate dominant structures from fine textures, and the Charbonnier penalty function. The improved model achieved competitive accuracy compared to advanced optical flow algorithms. To manage turbulence and motion noise during particle settling, we incorporated a global flow analysis framework using streaklines, streak flow, and potential functions. This enabled the segmentation of laminar, turbulent, and rebound flow regions without requiring individual particle tracking. Soil particle sizes were then back-calculated from laminar flow velocities using Stokes’ Law. Experimental results confirmed the method’s accuracy for particle sizes ranging from 20 mm to 0.7 mm, significantly extending the measurable range of Sedimaging systems. The proposed approach shows strong potential for integration into dam-related particle monitoring applications and building-related monitoring systems requiring fine-resolution analysis.