Shear Behavior of Large Keyed Dry Joints in Segmental Precast Bridges: Experiment, Numerical Modeling, and Capacity Prediction

The mechanical properties of the joint are a key factor influencing the overall structural performance of segmental precast beams. This study investigates the shear performance of large keyed dry joints in segmental precast beam specimens under six different conditions, including variations in the base height of the key, depth-to-height ratio, number of keys, and prestressing reinforcement ratio, using direct shear tests and numerical simulations. The mechanical performance of the joints in segmental precast bridges under combined bending and shear forces is also studied using finite element analysis software. Additionally, a prediction model for the shear strength of the large keyed dry joints is established using machine learning methods. The results show that increasing the base height, depth-to-height ratio, and overall dimensions of the key can enhance the shear strength of dry joints. The depth-to-height ratio of the key not only affects the shear strength of the dry joint but also determines the failure mode of the joint. Furthermore, the shear bearing capacity and displacement stiffness of the keyed dry joint increase with the reinforcement ratio of the prestressing tendons. Compared to smaller keyed joints, larger keyed dry joints exhibit higher shear bearing capacity, smaller relative slip at failure, and a simpler casting process, making them more suitable for application in segmental precast bridges. The influence of bending moment on the shear bearing capacity of the joint section is limited, with the relative variation compared to the pure shear condition being less than 10%. The shear bearing capacity of the joint section in segmental precast bridges can be designed based on its direct shear performance. The developed interface shear strength prediction model effectively captures the nonlinear relationship between various parameters and shear strength, demonstrating strong adaptability and accuracy.