Cycle Performance of Aerated Lightweight Concrete Windowed and Windowless Wall Panel from the Perspective of...

This paper aims to explore the seismic mechanical properties of newly developed fabricated aerated lightweight concrete (ALC) wall panels to clarify the interaction mechanism between wall panels and structures. It first introduces the lightweight deep learning object detection algorithm and constructs a network model with faster operation speed based on the convolutional neural network. Secondly, combined with the deep learning object detection algorithm, the quasi-static loading system is adopted to conduct the repeated loading test on two fabricated ALC wall panels. Finally, the hysteresis load-displacement curve of each test is recorded. The experimental results show that the proposed deep learning algorithm greatly improves the operation speed and compresses the model size without reducing the accuracy. The lightweight deep learning algorithm is applied to the study of the slip performance of the wall plate. The pretightening force of the connecting screw characterizes the slip performance between the wall plate and the structural beam, thereby affecting the deformation response of the wall plate when the interstory displacement increases. The hysteresis curve of the ALC wall panel has obvious squeezing effect, indicating that the slip of the connector can unload part of the external load and delay the damage of the wall panel. The skeleton curve suggests that the fabricated windowless ALC wall panel has higher positive and negative initial stiffness and bearing capacity than the fabricated windowed wall panel. However, the degradation analysis of the stiffness curve reveals that the lateral stiffness deviation of the fabricated windowless ALC wall panel is more obvious. It confirms that the proposed connection method based on the lightweight deep learning model can improve the seismic performance of ALC wall panels and provide reference for the structural analysis of embedding fabricated ALC wall panels. This work shows the important practical value for exploring the application effect of embedded ALC wall panels.