519.6 + 624.131.37 Mathematical modeling of the growth of pore pressure in soils under dynamic loads

Tishin N. R. (Bauman Moscow State Technical University), Proletarsky A. V. (Bauman Moscow State Technical University), Ozmidov O. R. (АО МОСТДОРГЕОТРЕСТ)

SOIL LIQUEFACTION, MATHEMATICAL MODELING, GEOTECHNICAL ENGINEERING, DYNAMIC LOADS


doi: 10.18698/2309-3684-2025-2-5067


This article addresses the mathematical modeling of pore pressure growth in soils under dynamic loads. The study focuses on evaluating and comparing three models: the Ma and Wang CPT model, an extended CPT model, and a logarithmic model, all designed to describe pore pressure behavior under prolonged cyclic loading. Laboratory data from storm liquefaction tests conducted on six soil samples from a single storm-affected site were used to validate the models. A normalized numerical integral of the error function was applied to assess the quality of the mathematical models. Results showed that the modified CPT model is suitable for modeling pore pressure growth under high cycle loading in asymptotically stationary systems, while the logarithmic model effectively captures non-stationary systems, allowing the description of processes such as slow pore pressure buildup and dissipation over extended loading cycles. The study also highlighted the limitations of exponential and hyperbolic models in describing prolonged dynamic processes and suggested ways to improve their application in complex geotechnical conditions. The authors emphasize the significance of these findings for assessing soil liquefaction risks and improving design methods for the stability of engineered structures subject to dynamic impacts, including seismic and vibrational loads. The article discusses further model developments, including adaptations for heterogeneous soils and multicycle loading, to more accurately account for the variability of soil’s physical and mechanical properties. The study supports the potential application of these models in engineering practice, enabling more precise forecasting and risk mitigation for soil degradation under diverse dynamic conditions.


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