Deep learning-augmented crack mapping and SPH-based dynamic simulation for landslide kinematic prediction
DOI:
https://doi.org/10.64862/Keywords:
Deep learning, Post-failure kinematics, smoothed particle hydrodynamics (SPH), spatiotemporal evolution, kinematic run-out predictionAbstract
To investigate the landslide's spatiotemporal evolution and failure mechanisms, a comprehensive field campaign was conducted using multi-source data. Deep learning models (U-Net and ResU-Net) were employed to automatically extract ground cracks from UAV orthophotos, with ResU-Net achieving superior accuracy (89.7%). Stability analysis via the limit equilibrium method and Monte Carlo simulations revealed a significant safety factor reduction to 0.822 and a 72.82% increase in failure probability under extreme rainfall. Smoothed particle hydrodynamics (SPH) simulations predicted a 48-s run-out with a peak velocity of 28 m/s. This integrated framework demonstrates the potential of combining deep learning, probabilistic analysis, and particle-based modeling for quantitative landslide hazard assessment and early warning.
References
Abraham, M.T., Satyam, N., Reddy, S.K.P., Pradhan, B., 2021. Runout modeling and calibration of friction parameters of Kurichermala debris flow, India. Landslides 18 (2), 737– 754.
https://doi.org/10.1007/s10346-020-01540-1
Aminpour, M., Alaie, R., Khosravi, S., Kardani, N., Moridpour, S., Nazem, M., 2023. Slope stability machine learning predictions on spatially variable random fields with and without factor of safety calculations. Comput. Geotech. 153, 105094.
https://doi.org/10.1016/j.compgeo.2022.105094
Jie D, Ali P. Y, Abdelaziz M, et al., 2020. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning, Science of The Total Environment, 720, 137320.
https://doi.org/10.1016/j.scitotenv.2020.137320.
Meng W, Wanqing S, et al., 2022. Phase-field modeling of cracking process in partially saturated porous media and application to rainfall-induced landslides, Engineering Geology, 310, 106884.
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Data Availability Statement
Data will be made available on request.
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