|
|
| |
| 基于自适应蝗虫算法的结构损伤稀疏正则化评估 |
| An adaptive grasshopper algorithm for sparse-regularization-based structural damage assessment |
| 投稿时间：2024-06-19 修订日期：2024-07-27 |
| DOI： |
| 中文关键词: 结构状态评估 结构损伤识别 自适应蝗虫算法 稀疏正则化 不完备测量 |
| 英文关键词:structural condition assessment structural damage detection adaptive grasshopper algorithm sparse regularization incomplete measurement |
| 基金项目:国家自然科学基金（52008109）；国家自然科学基金（12072120） |
|
| 摘要点击次数: 99 |
| 全文下载次数: 0 |
| 中文摘要: |
| 结构状态评估对于保障结构安全服役至关重要，其中结构损伤识别是核心环节。针对测量不确定性及不完备性容易引发的结构损伤识别不适定性问题，提出一种基于自适应蝗虫算法与稀疏正则化的结构损伤识别方法，以获得精确可靠的结构损伤识别结果。首先，自适应蝗虫算法中引入了自适应Lévy飞行和精英反向学习策略，避免结构损伤识别陷入局部最优，以提高识别结果稳定性；其次，融合了稀疏正则化构造模态参数型目标函数，通过提高结构损伤识别解的稀疏度以实现识别精度和鲁棒性的提高。基准函数测试表明，自适应蝗虫算法相比标准算法具有更好的全局收敛性和识别稳定性。针对简支梁的数值与试验结果表明，所提方法在不完备测量情况下仍可保证可靠的结构损伤识别精度，并且具有良好的噪声鲁棒性。 |
| 英文摘要: |
| Structural condition assessment is crucial for ensuring the safe serviceability of structures, with structural damage detection (SDD) being a core component. In this paper, a novel SDD method is proposed based on the adaptive grasshopper algorithm and sparse regularization. It aims to tackle SDD results accuracy decline and instability involving uncertainties and incomplete measurement, thereby achieving sparse-regularization-based structural condition assessment. Firstly, adaptive Lévy flight and elite opposition-based learning strategies are incorporated into the adaptive grasshopper algorithm to avoid the SDD process from falling into local optima and to enhance the stability of SDD results. Secondly, a modal parameter-based objective function with sparse regularization is formulated to increase the sparsity of SDD results, thereby improving SDD accuracy and robustness. The optimization results of competition-based evolutionary computation benchmark functions illustrated that adaptive grasshopper algorithm exhibits better global convergence and identification stability compared to its standard version. Numerical and experimental results for simply-supported beams indicate that the proposed method can ensure reliable SDD accuracy even in the case of incomplete measurements, and it possesses good noise robustness as well. |
| 查看/发表评论 下载PDF阅读器 |
