Abstract:

To investigate the deformation characteristics of retaining piles in the footwall influenced by normal faults, a case study of a foundation pit project in Shenzhen City was conducted using a comprehensive approach that included numerical simulations and field measurements. The study examined how different fault slip amounts, dip angles, and positions affect the deformation of retaining piles in the footwall’s influence zone. Sensitivity analysis and orthogonal experiments were carried out to assess the impact of these fault parameters. Results revealed that deformation of the retaining piles decreased under the normal fault, with the center of gravity shifting downward. The upper sections of the piles experienced more significant deformation compared to the lower sections. Deformation was inversely proportional to both the fault slip amount and dip angle, and directly proportional to the distance from the fault to the foundation pit. Specifically, the maximum deformation rate, r(ΔZ_max/Δ), decreased exponentially with increasing fault slip amount and dip angle, but increased logarithmically with increasing distance from the fault. Sensitivity analysis showed that dip angle had the most significant impact on the maximum deformation of the retaining piles, followed by slip amount, with the fault position having the least influence. By fitting data from 64 orthogonal experiments, a strong linear relationship was established between the maximum deformation U_hm and the index η(\frac{\theta \mathrm{\pi }T}{180°S}).Consequently,a predictive model for the maximum deformation of retaining piles in the footwall’s influence zone was developed, along with a corresponding predictive equation for this project. These findings offer valuable insights for deformation control in foundation pit projects located in normal fault areas with similar geological conditions.

Key words: normal fault, footwall influence zone, deformation of foundation pit retaining pile, numerical simulation, field measurement, sensitivity analysis, prediction model