关于使用声辐射和机器学习监测基岩FRP钢筋混凝土板块损坏进展的实验研究
Tonghao Zhang1, Mohammad Mahdi1, Mohsen Issa1
1Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, 929 West Taylor Street, Chicago, IL 60607, USA.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
声波排放监测有效地追踪基岩纤维增强聚合物混凝土结构的损坏. 这种方法准确地识别了裂的开始,演变和宽度,提供了可靠的结构健康监测解决方案.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 结构健康监测 结构健康监测
背景情况:
- 与混凝土中的钢筋钢筋相比,岩纤维增强聚合物 (BFRP) 具有更高的抗拉强度和耐腐蚀性.
- 由于BFRP的脆弱性,需要强大的结构健康监测 (SHM),以防止BFRP钢筋混凝土结构的意外故障.
- 声波辐射 (AE) 是一种有前途的非破坏性技术,用于检测和分析材料中的损伤机制.
研究的目的:
- 评估声辐射 (AE) 方法的有效性,以监测BFRP钢筋混凝土板的损坏开始和进展.
- 通过AE数据和机器学习,识别和区分各种损伤机制,包括拉力和剪切裂纹.
- 量化AE特征和裂宽度之间的关系,以改善结构健康评估.
主要方法:
- 两个简单支的BFRP钢筋混凝土板被 subjected到负载直到故障,仪器与AE传感器,摄像头,和应变/位移仪表.
- 无监督的机器学习 (集群) 被应用于第一个板块的AE数据,以分类损伤机制.
- 使用K-近邻 (KNN) 监督学习模型来验证第二块AE数据集群,达到99.2%的准确性.
- 使用Statistical SHapley Additive exPlanations (SHAP) 分析来确定AE特征与裂宽度之间的相关性.
主要成果:
- 观察到的占主导地位的损伤机制是混凝土裂,这归因于过度增强的设计和强大的BFRP-混凝土粘合.
- 在AE监测中,成功地确定了损伤的开始,从拉力裂到剪切裂的进展,以及裂宽度的演变.
- 确定AE持续时间是与裂宽度相关的最重要的特征,裂附近的累积AE持续时间显示跟踪宽度的准确性接近100%.
- 该KNN模型准确地预测了三个损坏集群:拉力裂,剪切裂和噪声.
结论:
- 声辐射传感器,特别是当放置在中间时,为BFRP钢筋混凝土板提供有效的SHM解决方案.
- AE监测可以可靠地检测拉伸裂的开始,显著的结构反应变化,损伤演变和裂宽度的进展.
- 将AE数据与机器学习和SHAP分析集成,可以提高对BFRP钢筋混凝土结构损坏的理解和量化.
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