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Long-term dynamic characteristics and cumulative strain prediction modeling of round gravel foundation under cyclic
Haijun Huang1, Shaokun Ma2, Cong Peng3
1School of Hydraulic Engineering, Guangxi Vocational College of Water Resources and Electric Power, Nanning, 530023, China.
Abstract:
A series of saturated undrained dynamic triaxial tests were carried out by using DYNTTS large-scale triaxial cyclic test system to investigate the long-term dynamic characteristics and cumulative strain evolution of round gravel foundation under cyclic subway loading. The influence mechanisms of relative density, confining pressure and dynamic stress amplitude on the long-term dynamic characteristics and cumulative deformation of round gravel were systematically investigated. A prediction model of cumulative plastic strain considering the coupling effect of 3 parameters was proposed. The results of the study show that for the subway tunnels with shallow burial depth and low relative density of the round gravel foundation, the train operation has potential safety hazards, while for the subway tunnels with deeper burial depths, the operation of the trains is relatively safe. Increasing relative density and confining pressure can effectively improve the resilient modulus of round gravel, reduce its cumulative plastic strain, elastic strain and dynamic pore-pressure ratio, and significantly enhance the foundation's liquefaction resistance. Therefore, for unfavorable working conditions such as Dr=0.3 and σ3 = 100 kPa, improvement measures such as increasing tunnel depth and implementing high-pressure grouting can be adopted to reduce the settlement of the subway foundation. Based on the time-hardening theory, a cumulative plastic strain prediction model was established with comprehensive consideration of relative density, confining pressure and dynamic stress amplitude. The research results can provide theoretical support and a design basis for the dynamic stability assessment of subway round gravel foundation and its long-term settlement prediction after construction.
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