研究基于计算机视觉的平面框架的自动解决方法
1School of Mechanics and Engineering Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种深度学习方法,用于从图像中对平面框架的自动内部力分析. 该方法达到90%以上的准确性,为传统结构力学计算提供了更快,更有效的替代方案.
科学领域:
- 结构工程 结构工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 对于平面框架的传统内部力分析是耗时且容易出错的.
- 一般用途的有限元分析 (FEA) 软件在预处理和学习曲线方面存在局限性.
- 在结构分析中需要快速而智能的解决方案.
研究的目的:
- 开发基于深度学习的自动解决方案,用于平面框架内部力分析.
- 使用计算机视觉从打印图表中提取结构信息.
- 为了实现快速准确的内部力计算和可视化.
主要方法:
- 使用智能手机采集平面框架图的图像采集.
- 图像预处理,包括纠正和增强.
- 使用YOLOv8算法来提取结构数据 (节点,负载,约束) 的对象检测和识别.
- 静态分析使用矩阵位移方法进行内部力计算.
- 内力图的生成. 内力图的生成.
主要成果:
- 对于结构原始体,实现了99.1%的检测准确度.
- 测试组中的机械问题总解决准确度超过了90%.
- 展示了用于平面框架分析的方便和高效的计算方法.
结论:
- 提出的深度学习方法显著提高了平面框架内部力分析的效率和方便性.
- 这种方法使得结构信息的提取和随后的分析自动化.
- 该方法显示出高精度和结构工程中的实际应用潜力.
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