Sensing time-variant risk of rainfall-induced landslides using slope digital twins - Liu, Xin; Wang, Yu; ICASP14 (My University, 2023)
Digital twin is deemed as a core technology for building a smart city and Industry 4.0, and it has great potential to be applied to quantitatively sense the risk of a rainfall-induced landslide by developing a slope digital twin. A slope digital twin is a virtual slope model that can continuously learn from various field observations (e.g., monitoring data, site investigation data, slope performance records) collected from its physical counterpart to enhance the performance of the slope model. This study develops a slope digital twin for a real slope in Hong Kong using monitoring data and slope survival records, and uses the slope digital twin to predict time-variant soil hydraulic responses (e.g., pore water pressure) and slope stability (e.g., factor of safety and slope failure probability) under rainfall infiltration. A two-level Bayesian updating scheme is adopted to learn from field observations and update the slope model. Results show that incorporating monitoring data and slope survival records leads to reduced uncertainties in soil hydraulic and strength parameters. The updated slope model predicts time-variant soil hydraulic responses under rainfall infiltration consistent with the field measurements. The predicted time-variant slope factor of safety and slope failure probability properly reflect rainfall infiltration mechanism and are verified by field observations. Slope digital twins provide a novel tool for sensing the time-variant risk of rainfall-induced landslides.
14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
PUBLISHED