Yuan Xing, Zhang Yao, Lu Qinggang, Zhang Shuhang, Liu Hua, Jin Mingchang, Xu Feng
College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China.
Beijing Institute of Architectural Design, Beijing 100045, China.
Comput Intell Neurosci. 2022 Jun 3;2022:3968607. doi: 10.1155/2022/3968607. eCollection 2022.
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.
本文旨在探究新开发的装配式加气轻质混凝土（ALC）墙板的抗震力学性能，以阐明墙板与结构之间的相互作用机制。首先介绍了轻量化深度学习目标检测算法，并基于卷积神经网络构建了运行速度更快的网络模型。其次，结合深度学习目标检测算法，采用拟静力加载系统对两块装配式ALC墙板进行反复加载试验。最后，记录各试验的滞回荷载-位移曲线。试验结果表明，所提出的深度学习算法在不降低精度的情况下，大大提高了运行速度并压缩了模型尺寸。将轻量化深度学习算法应用于墙板滑移性能的研究。连接螺杆的预紧力表征了墙板与结构梁之间的滑移性能，从而影响层间位移增大时墙板的变形响应。ALC墙板的滞回曲线有明显的捏拢效应，表明连接件的滑移可卸载部分外部荷载，延缓墙板的破坏。骨架曲线表明，装配式无窗ALC墙板比装配式有窗墙板具有更高的正负初始刚度和承载力。然而，刚度曲线的退化分析表明，装配式无窗ALC墙板的侧向刚度偏差更为明显。证实了基于轻量化深度学习模型所提出的连接方法可提高ALC墙板的抗震性能，为嵌入式装配式ALC墙板的结构分析提供参考。这项工作对于探究嵌入式ALC墙板的应用效果具有重要的实际价值。
