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LMS-based structural health monitoring of a non-linear rocking structure

机译：基于LMS的非线性摇摆结构的结构健康监测

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摘要

A structure's health or level of damage can be monitored by identifying changes in structural or modal parameters. This research directly identifies changes in structural stiffness due to modelling error or damage for a post-tensioned pre-cast reinforced concrete frame building with rocking beam column connections and added damping and stiffness (ADAS) elements. A structural health monitoring (SHM) method based on adaptive least mean squares (LMS) filtering theory is presented that identifies changes from a simple baseline model of the structure. This method is able to track changes in the stiffness matrix, identifying when the building is (1) rocking, (2) moving in a hybrid rocking-elastic regime, or (3) responding linearly. Results are compared for two different LMS-based SHM methods using an L_2 error norm metric. In addition, two baseline models of the structure, one using tangential stiffness and the second a more accurate bi-linear stiffness model, are employed. The impact of baseline model complexity is then delineated. The LMS-based methods are able to track the non-linearity of the system to within 15% using this metric, with the error due primarily to filter convergence rates as the structural response changes regimes while undergoing the El Centro ground motion record. The use of a bi-linear baseline model for the SHM problem is shown to result in error metrics that are at least 50% lower than those for the tangential baseline model. Errors of 5-15% with this L_2 error norm are fairly stringent compared to the greater than 2 x changes in stiffness undergone by the structure, however, in practice the usefulness of the results is dependent on the resolution required by the user. The impact of sampling rate is shown to be negligible over the range of 200-1000 Hz, along with the choice of LMS-based SHM method. The choice of baseline model and its level of knowledge about the actual structure is seen to be the dominant factor in achieving good results. The methods presented require 2.8-14.0 Mcycles of computation and therefore could easily be implemented in real time.
机译：可以通过识别结构或模态参数的变化来监视结构的健康状况或损坏程度。这项研究直接确定了由于模型误差或后摇预应力钢筋混凝土框架房屋的结构误差而引起的结构刚度变化，该结构具有摇摆梁柱连接和附加的阻尼和刚度（ADAS）元素。提出了一种基于自适应最小均方（LMS）过滤理论的结构健康监测（SHM）方法，该方法可识别结构的简单基线模型中的变化。该方法能够跟踪刚度矩阵的变化，确定建筑物何时（1）摇摆，（2）在混合摇摆-弹性状态下移动或（3）线性响应。使用L_2误差范数度量比较了两种基于LMS的SHM方法的结果。另外，采用了两个结构的基线模型，一个使用切向刚度，第二个使用更精确的双线性刚度模型。然后描述基线模型复杂度的影响。基于LMS的方法能够使用该指标将系统的非线性跟踪到15％以内，其误差主要归因于在经历El Centro地面运动记录时随着结构响应改变状态，滤波器的收敛速度。对于SHM问题，使用双线性基线模型显示出的误差度量比切线基线模型的误差度量至少低50％。与该结构所经历的刚度变化大于2倍相比，使用L_2误差范数的5-15％的误差相当严格，但是，实际上，结果的实用性取决于用户所需的分辨率。在基于LMS的SHM方法的选择下，采样率的影响在200-1000 Hz范围内可忽略不计。基线模型的选择及其对实际结构的了解程度被视为获得良好结果的主要因素。提出的方法需要2.8-14.0 Mcycles的计算，因此可以轻松地实时实现。

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