模型更新方法用于检测和定位结构损坏,使用通用灵活性矩阵和改进的灰狼优化算法 (I-GWO).
Sina Sadraei1, Majid Gholhaki2, Omid Rezaifar1
1Faculty of Civil Engineering, Semnan University, Semnan, Iran.
这项研究引入了一个改进的灰狼优化器 (I-GWO) 用于结构健康监测 (SHM). 该方法准确地检测到土木工程结构中的损坏位置和程度,错误率小于3%.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 优化算法 优化算法
背景情况:
- 结构健康监测 (SHM) 系统的战略实施对于关键的土木工程基础设施至关重要.
- 在SHM的首要挑战是及时和自动识别和定位结构损坏.
- 优化算法越来越多地用于结构模型更新,以提高损坏检测能力.
研究的目的:
- 评估将结构频率和通用灵活性矩阵用于损害评估的有效性.
- 开发一种先进的优化方法,用于在民用结构中精确识别损坏.
- 为了比较Improved Grey Wolf Optimizer (I-GWO) 与其他SHM优化技术的性能.
主要方法:
- 利用结构的自然频率和通用灵活性矩阵的组合来确定损坏.
- 应用改进的灰狼优化器 (I-GWO) 在各种噪音条件下处理结构数据.
- 在各种结构模型上验证该方法,包括一个15层的剪切框架,一个2D木架桥和一个2D框架.
主要成果:
- 在所有测试的结构模型中,I-GWO方法在定位和量化损坏方面表现出高准确性.
- 模拟和估计结果之间的平均误差在多个损坏场景和运行中始终低于3%.
- 提出的方法显著优于传统的模式灵活性技术和其他优化器,如GWO和PSO.
结论:
- 使用I-GWO开发的SHM技术精确地识别了各种民用结构中损坏的位置和程度.
- 这种方法提高了结构系统中损害识别的整体有效性和可靠性.
- I-GWO提供了一种可靠且准确的方法,用于在结构健康监测应用中检测损坏.
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