應用複合多尺度交叉取樣熵於民生建築之長期結構健康診斷

臺灣位於歐亞以及菲律賓海板塊交界，是環太平洋火山地震帶的活躍區，因此，結構建築之安全性與使用性是保障人民生命財產之關鍵。近年來，結構健康診斷技術的研究與開發，日益受到重視。本研究建立一基於複合多尺度交叉取樣熵分析的結構健康診斷系統，並透過圖形化程式設計軟體將理論轉為簡易的使用介面，對建築物所產生的進行分析。為了提高各尺度下熵分析之可靠性與準確性，採用了複合多尺度交叉取樣熵，量測相鄰樓層上環境微振訊號之間的相似度與異步性，並且增加粗粒化訊號時間序列的次數，其結果用以作為結構健康分析的指標。本研究選取一棟民用住宅大樓為長期監測對象，並以此棟建築物於20年內擷取之地震與環境震動所累積之數據，評估複合多尺度交叉取樣熵分析法之實用性與可能性。由每一筆數據分析獲得之熵曲線及相對應之破壞指數，得到基於複合多尺度交叉取樣熵分析之長期健康趨勢，證明所提出結構健康診斷系統之可靠性；此外，分析此棟結構物所經歷之大地震事件，發現於大地震後熵值會有增加，得到偏高的破壞指數，由此可知大地震後結構訊號對熵值分析具有相當程度之影響。複合多尺度交叉取樣熵對於結構物長期觀測與分析具有穩定性與實用性，可提供結構於不穩定時之初步預警，並藉以擬定相關維護策略。

關鍵字

結構健康診斷；熵分析；複合多尺度；交叉取樣熵；長期健康評估

並列摘要

This study proposed a novel entropy-based structural health monitoring (SHM) system for measuring microvibration signals generated by actual buildings. A structural health diagnosis interface was established for demonstration. To enhance the reliability and accuracy of entropy evaluation at various scales, composite multiscale cross-sample entropy (CMSCE) was adopted to increase the number of coarse-grained time series. The degree of similarity and asynchrony between ambient vibration signals measured on adjacent floors was used as an indicator for structural health assessment. A residential building that has been monitored since 1994 was selected for long-term monitoring. The accumulated database including both the earthquake and ambient vibrations in each seismic event provide the possibility to evaluate the practicability of the CMSCE-based method. Entropy curves obtained for each of the years as well as the stable trend of the corresponding damage index (DI) graphs demonstrated the reliability of the proposed SHM system. Moreover, the large earthquake events that occurred near the monitoring site were analyzed. The results revealed that entropy values may be increased after major earthquakes with positive. Positive DI values were obtained for the floors, which could provide an early warning of structural instability. Base on the result, the proposed SHM system is highly stable and practical.

並列關鍵字

structural health monitoring ； entropy analysis ； multi-scale ； composite cross-sample entropy ； long-term evaluation

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