基于ANN的增强组合方法用于使用有限数据集进行桥梁损伤特征化
Ivan Izonin1,2, Illia Nesterenko3, Athanasia K Kazantzi4
1Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham, B15 2TT, UK. i.izonin@bham.ac.uk.
Scientific reports
|October 17, 2024
概括
本研究介绍了一种增强的输入加倍技术和人工神经网络 (ANN) 组合,用于使用小型数据集识别桥梁损坏. 该方法准确地检测桥梁中的肌损失,优于现有技术.
科学领域:
- 结构工程 结构工程
- 在土木工程中的人工智能.
背景情况:
- 桥梁是关键基础设施,需要对安全进行强有力的损害评估.
- 非破坏性方法对于评估桥梁状况而不会造成服务中断至关重要.
- 评估桥梁恶化,如肌损失,对于维护和适应至关重要.
研究的目的:
- 介绍一个增强的输入加倍技术和基于人工神经网络 (ANN) 的级联组合,用于识别桥梁损坏.
- 为了应对结构性评估中常见的有限数据的挑战.
- 提高桥梁损坏状态识别的准确性.
主要方法:
- 开发了一种基于线性化响应表面的新型数据增强方案.
- 改进了基于ANN的两步组合方法用于损害识别.
- 整合了改进的输入加倍方法作为ANN级联组合中的预测器.
主要成果:
- 在预测肌损失的准确性显著提高了7%,0.5%和8% (R2) 在一个真正的桥梁甲板的三个关键区域.
- 与国际文献中现有的方法相比,表现出更高的性能.
- 验证了拟议方法在损坏的预应力平衡悬臂桥上的有效性.
结论:
- 提议的增强输入加倍和ANN级联组合方法对于识别桥梁损坏状态非常准确,特别是在数据有限的情况下.
- 数据增强方案有效地改善了用于结构评估的智能数据分析.
- 这种方法为确保桥梁安全和基础设施管理提供了可靠和准确的解决方案.
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