Experimental Study on Vertical Bearing and Deformation Characteristics of Qiantang River Ancient Seawall

Abstract

Situated on the northern bank of the Qiantang River estuary, the ancient seawall serves not only as a national cultural relic but also as an active agent in flood and tide prevention. This seawall features a trapezoidal cross-section and is constructed with layered stone blocks and a sticky rice mortar. To investigate the load-bearing and deformation attributes of this ancient structure, a scaled-down specimen with a ratio of 1:4 was created. Monotonic and cyclic vertical loadings were then applied to the wall’s top surface. During these loading procedures, measurements of the loading force, seawall displacement, and front and side deformation fields were taken. Experimental findings reveal that the seawall tends to lean towards the soil-retaining side under vertical loading. After ten loading cycles, the vertical rigidity of the wall was reduced by 10%. Upon application of a uniformly distributed vertical load of 1.6 MPa at the top of the wall, significant cracks began to materialize in the blocks at the base of the seawall. When the loading at the top increased to 2 MPa, a vertical crack that cut through the mortar layer at the wall’s center was observed. By comparing it to a three-dimensional finite element model, the load-bearing and deformation characteristics of the ancient seawall observed in the experiments were confirmed, which could contribute to the scientifically informed conservation and protection of the seawall.

Research on Bridge Integrity Assessment and Early Warning Monitoring Methods Based on Bearing Reaction Force

“…Therefore, with the surge in overloaded heavy vehicles, real-time monitoring and management of bridge loads become imperative. Simultaneously, the growing concern among researchers regarding vertical load-bearing capacity has injected new vitality into the study of support reaction monitoring systems [28,29]. When establishing warning thresholds for vehicle loads, initial warning thresholds can be determined based on statistical data for vehicle loads.…”