Abstract
Gravity-type anchorages (GTAs) have been extensively applied in large-span suspension bridges across the globe, but the load-bearing performance of composite GTA is not yet fully investigated. Here, the comprehensive load-bearing performance of GTA is investigated using a combination of physical modelling and numerical modelling methods, considering the impact of anchorage’s burial depth, notched sill, and pile. Six model tests are first conducted using a self-reversing force loading equipment, including flat bottom anchorage, notched sill anchorage, and notched sill anchorage with pile. Each type of anchorage is tested under two different burial depth conditions. The failure process, anchorage displacement, cable tension force, and the internal strain field of the foundation, were examined during the model tests. Additionally, through the numerical simulations, the evolution characteristics of contact stresses and load sharing ratio associated with various forms of anchorage are discussed. The results indicate that the anchorage’s burial depth and pile significantly impact on anchorage’s bearing capacity. The presence of the notched sill can change the distribution of contact stress between the anchorage and foundation, which can still resist the horizontal force until the foundation before the notched sill occurs shear failure. The failure process of the anchorage-foundation system was divided into four stages: stable bearing, sliding, sliding and overturning, and failure stage. The anchorage starts overturning with the applied load reaching to 3*Tm, except for the flat bottom anchorage of shallow burial depth condition. Under the same conditions, the greater the anchorage’s burial depth, the smaller the load shared by the friction between the anchorage bottom and foundation, the greater the load shared by the notched sill and foundation before the anchorage. In addition, the load shared by pile continuously increasing as the anchorage starts overturning. The findings from this study can provide a theoretical basis and valuable insights for the optimization design of GTA.
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