Excess pore water pressure behavior of saturated soft clay under cyclic confining pressure with different frequencies

The excess pore water pressure is main factor affecting the dynamic response of saturated soft clay under traffic loads. Recognize that, cyclic triaxial tests with and without cyclic confining pressure were carried out to study the development of excess pore water pressure of saturated soft clay. The impacts of factors, such as cyclic stress ratio (CSR), loading frequency, and cyclic confining pressure, were analyzed. Excess pore water pressure increases with increasing cyclic stress ratios, while decreases with increasing loading frequencies. Nevertheless, with increasing cyclic confining pressures, the maximum excess pore water pressure increases, while the minimum excess pore water pressure remains unchanged. Besides that, the greater CSR and cyclic confining pressure deliver an increment of both maximum and minimum excess pore water pressure rate. However, the excess pore water pressure rate - time curves coincide under different loading frequencies. Meanwhile, both the normalized maximum and minimum excess pore water pressure rates are linearly proportional with time under different test conditions in logarithmic coordinates. Based on that, both maximum and minimum excess pore water pressure prediction models, relating to the above factors, are proposed, and the predicted results accord well with the measured data.

Automated summary

The excess pore water pressure is the main factor affecting the dynamic response of saturated soft clay under traffic loads. Cyclic triaxial tests were conducted to investigate the development of excess pore water pressure and its relationship with factors such as cyclic stress ratio, loading frequency, and cyclic confining pressure. The results showed that increasing cyclic stress ratios and cyclic confining pressures led to higher maximum excess pore water pressure, while increasing loading frequencies resulted in lower excess pore water pressure. The measured data was well predicted by proposed models.