中高層結構物微振量測之最佳感測器配置

Optimal Sensors Placement for Micro-Vibration Monitoring of Mid-High Rise Building

10.6849/SE.202112_36(4).0005

楊晏瑜(Yen-Yu Yang)；呂良正(Leng-Jenq Leu)

最佳化感測器配置 ； 結構健康監測或檢測 ； 隨機子空間識別 ； 系統識別 ； 三次樣條內插法 ； Optimal Sensors Placement ； Structural Health Monitoring ； Stochastic Subspace Identification ； Cubic Spline Interpolation

結構工程

36卷4期（2021 / 12 / 01）

116 - 137

繁體中文

台灣位屬多地震區域，由於地震頻繁，為了結構物之安全性，需於災害發生前與災害發生後進行結構物健康識別。本研究所提出之最佳化配置為減少往後檢測量測同棟中高層結構物所設置之感測器顆數，及提高判斷結構物模態頻率的精度。為此目的，需先求得結構物之參考模態頻率；首先在結構物上設置參考感測器獲得真實時間歷時，並利用Cubic spline內插法取得模擬時間歷時，得到結構物各樓層真實與模擬時間歷時後，運用隨機子空間識別（SSI），做出穩態圖，使用機器學習中的K-means演算法判斷穩態圖上之各參考模態頻率，最後基因演算法判斷出加速度感測器配置之最佳樓層位置。最後做了四棟真實結構物之實驗以驗證此方法的可行性。

Taiwan is located in the seismic zone with high frequency earthquake occurrences. In order to increase structure safety, it needs to monitor the structural health before and after disaster occurs. This study proposes a method to obtain the optimal sensors placement(OSP), which could reduce the number of sensors for building monitoring. In additions, the method could find out the higher modal frequencies for structures. First, collect the real time-histories and use Cubic spline interpolation method to obtain simulated time-histories for each floor. Second, use Stochastic Subspace Identification to generate stabilization diagrams. Third, K-means clustering method is used to obtain modal frequencies. Finally, use Genetic Algorithm method to find OSP. There are four in-situ experiments for the method verifying, one is in National Taiwan University Cancer Center, one in Tamsui(a new building) and others are 5 years buildings in Banqiao.

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