一个多策略的融合识别模型,用于钢筋混凝土柱的故障模式
Tongtong Gai1, Dehu Yu2, Sen Zeng1
1School of Civil Engineering, Qingdao University of Technology, Qingdao, China.
ISA transactions
|April 25, 2024
概括
本研究引入了新的方法,以准确识别钢筋混凝土 (RC) 柱的故障模式,改善抗地震和结构安全评估. 开发的融合模型达到97%的准确性,超过现有技术.
科学领域:
- 结构工程 结构工程
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 精确识别钢筋混凝土 (RC) 柱的故障模式对于地震设计和安全评估至关重要.
- 现有的方法面临挑战,包括高成本,有限的因素考虑,以及切割或柔性切割故障的低精度/回忆.
- 在故障模式数据样本中的类失衡是开发可靠识别模型的一个重要问题.
研究的目的:
- 分析影响RC柱故障模式的关键因素.
- 为了解决数据类失衡,提高故障模式识别准确度.
- 开发和验证一个具有成本效益的,准确的模型,用于RC列故障模式的识别.
主要方法:
- 分析影响RC柱故障模式的主要因素.
- 开发数据过量采样 (BSB-FMC),模型组合 (RFB-FMC) 和成本敏感学习 (CSB-FMC) 策略.
- 实现一个融合模型 (BSFCB-FMC) 结合这些策略和使用SHapley添加式解释 (SHAP) 的解释.
主要成果:
- 与单个模型 (ANN,SVM,RF,AdaBoost) 相比,提出的策略显著提高了RC柱故障模式识别的准确性.
- 核聚变模型 (BSFCB-FMC) 实现了97%的整体准确性.
- 使用BSFCB-FMC模型,所有三种故障模式的精度和回忆都超过了90%.
结论:
- 开发的BSFCB-FMC模型提供了一个快速,准确和具有成本效益的解决方案,用于识别RC柱的故障模式.
- 该研究表明,在不平衡的结构数据中,结合过量采样,组合和成本敏感学习的有效性.
- 这些发现有助于加强现有RC结构的抗地震设计和安全评估.
相关概念视频
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