Local measurement for structural health monitoring

Localized nature of damage in structures requires local measurements for structural health monitoring. The local measurement means to measure the local, usually higher modes of the vibration in a structure. Three fundamental issues about the local measurement for structural health monitoring including (1) the necessity of making local measurement, (2) the difficulty of making local measurement and (3) how to make local measurement are addressed in this paper. The results from both the analysis and the tests show that the local measurement can successfully monitor the structural health status as longas the local mod es are excited. Unfortunately, the results also illustrate that it is difficult to excite local modes in a structure. Therefore, in order to carry structural health monitoring into effect, we must ( 1 ) ensure that the local modes are excited, and (2) deploy enough sensors in a structure so that the local modes can be monitored.     

Algorithms for time synchronization of wireless structural monitoring sensors

Dense networks of wireless structural health monitoring systems can effectively remove the disadvantages associated with current wire‐based sparse sensing systems. However, recorded data sets may have relative time‐delays due to interference in radio transmission or inherent internal sensor clock errors. For structural system identification and damage detection purposes, sensor data require that they are time synchronized. The need for time synchronization of sensor data is illustrated through a series of tests on asynchronous data sets. Results from the identification of structural modal parameters show that frequencies and damping ratios are not influenced by the asynchronous data; however, the error in identifying structural mode shapes can be significant. The results from these tests are summarized in Appendix A. The objective of this paper is to present algorithms for measurement data synchronization. Two algorithms are proposed for this purpose. The first algorithm is applicable when the input signal to a structure can be measured. The time‐delay between an output measurement and the input is identified based on an ARX (auto‐regressive model with exogenous input) model for the input–output pair recordings. The second algorithm can be used for a structure subject to ambient excitation, where the excitation cannot be measured. An ARMAV (auto‐regressive moving average vector) model is constructed from two output signals and the time‐delay between them is evaluated. The proposed algorithms are verified with simulation data and recorded seismic response data from multi‐story buildings. The influence of noise on the time‐delay estimates is also assessed. Copyright © 2004 John Wiley & Sons, Ltd.     

基于随机森林算法的多维情境特征活动识别

利用智能手机传感器可感知时间、空间、时空和用户等多维情境的特征,可识别用户活动,但原框架模型中仅利用了单一分类器中的朴素贝叶斯算法,存在分类精度效果受限的问题。本文利用集成分类器中的随机森林算法对原有框架中的单一分类器进行了改进。在获取的3个数据集上的十倍交叉验证结果表明,加权平均F1量测值均有较大提高,表明利用随机森林算法在分类精度效果上有所提升;但由于集成算法结构相对复杂,其学习效率相对较低。此外,随机森林算法的分类混淆矩阵表明,导致识别误差的因素主要为活动的定义与室内定位精度。     

滑坡实时监测预警系统的实验室实现

用传感器对滑坡实施监测是一种有效的监测预警手段,但由于受一些因素的限制,在实验室进行滑坡监测技术有效性的研究方面仍有一定的难度。本文以振弦式传感器为例,提出了传感器辅助机械工装以完成传感器在实验室实现滑坡监测数据模拟采集的设计思路,并通过对模拟数据进行多次测量演算了传感器及其辅助装置的可靠性。同时,为便于对监测数据进行及时有效的管理和分析,还采用VC++语言所编制的管理程序对获取的监测数据实施有效的管理和分析。结果表明,对给定信号的监测效果好,响应快,误差小,能较好地模拟滑坡现场监测。     

An Introductory Overview of Earthquake Early Warning

Earthquake early warning (EEW) is discriminated from earthquake prediction by using initial seismic waves to predict the severity of ground motion and issue the warning information to potential affected area. The warning information is useful to mitigate the disaster and decrease the losses of life and economy. We reviewed the development history of EEW worldwide and summarized the methodologies using in different systems. Some new sensors came and are coming into EEW giving more developing potential to future implementation. The success of earthquake disaster mitigation relies on the cooperation of the whole society.