Image processing-based evaluation of shallow foundation settlement during liquefaction using a 1g physical model
Image processing-based evaluation of shallow foundation settlement during liquefaction using a 1g physical model
The use of image processing techniques in civil engineering has grown significantly in recent years, offering innovative tools for the observation and analysis of physical phenomena. In geotechnical engineering, evaluating the behavior of shallow foundations resting on liquefiable sandy soils remains a critical research area, particularly under dynamic loading conditions. This study presents an experimental investigation into the settlement response of shallow foundations with varying aspect ratios placed on saturated loose sand, a material susceptible to liquefaction. A 1g shaking table physical model is developed to simulate seismic loading scenarios. The experimental setup includes conventional instrumentation, displacement transducers, accelerometers, and pore water pressure sensors, to monitor the response of the soil-foundation system. In parallel, an image processing technique is employed to capture and quantify foundation settlements over time, allowing for enhanced spatial and temporal resolution in data interpretation. The results indicate a direct relationship between the foundation's aspect ratio and its seismic-induced settlement: as the aspect ratio increases, the magnitude of settlement also increases. The findings emphasize the importance of geometric parameters in foundation design for liquefiable soils and highlight the potential of image processing as a reliable, non-intrusive tool for dynamic testing in geotechnical research. This study contributes to a better understanding of soil-structure interaction under seismic conditions and supports the integration of modern imaging techniques in experimental geotechnics.
A. Mohammad Tutunchian; Reza Rezvani; M. Payan; B. Stuyts
11th International Conference on Physical Modelling in Geotechnics (ICPMG2026)
Special Session 10: Liquefaction Experiments and Analysis Projects – Lesson Learned from LEAP-Asia-2025
https://doi.org/10.53243/ICPMG2026-56