提高高速公路桥梁系统的地震损害检测和评估:采用贝叶斯优化模式识别方法
1Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究介绍了一种基于数据的方法,用于检测和评估桥梁柱中的地震损害,使用基于累积强度的特征和机器学习. 该方法可以为弹性基础设施提供准确,近乎实时的结构健康监测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 地震工程的工程是地震工程.
背景情况:
- 高速公路桥梁是关键的基础设施,需要在地震等极端事件后迅速恢复.
- 接近实时的结构损坏诊断对于有效的事件后恢复和社区弹性至关重要.
- 桥梁柱是重要的组成部分,它们的损坏评估是整体结构完整性的关键.
研究的目的:
- 开发和验证基于数据的方法,用于检测和评估桥梁柱中的地震损害.
- 为了改进分析,引入基于累积强度的新型损害特征.
- 利用机器学习来准确分类损坏状态.
主要方法:
- 使用非线性时间历史分析模拟来生成结构响应数据.
- 基于累积强度的损伤特征被开发和评估.
- 应用了无监督和监督学习技术,包括支持向量机和贝叶斯优化与高斯过程.
主要成果:
- 拟议的方法在检测地震损害方面表现出高精度.
- 该方法成功地分类了损伤的存在/缺失 (二进制) 和损伤的严重程度 (多类).
- 基于累积强度的特征在损害评估中被证明是有效的.
结论:
- 数据驱动的方法提供了一种可靠的方法,用于近实时检测和评估桥梁柱中的地震损害.
- 这项研究支持用于结构健康监测的车载传感器计算的实际实施.
- 这些发现有助于提高交通基础设施对抗地震危害的弹性.
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