利用反復式模型改良法於結構系統健康診斷

Health Diagnosis of Structures Using a Repetitive Model Refinement Approach

林正紋(Jeng-Wen Lin) ； 黃志偉(Chih-Wei Huang) ； 蔡文賢(Wen-Hsien Tsai)

《科學與工程技術期刊》 4卷2期 (2008/06) Pp. 11-17

https://doi.org/10.7117/JSET.200806.0011

摘要

本文對於結構系統承受地震力之健康診斷，提出統計信心區間模型改良(refinement)法，此法係指使用估算結構參數的95%信心區間，在最小平方迴歸中判斷其統計意義。當參數之信心區間包含「零」值時，在統計上便足以刪除該參數，因此反覆此參數篩選程序，直至所有參數之統計意義無法更進一步地被改善，剩餘的參數將爲改良之模型。另外，爲了確認結構參數的95%信心區間爲最適當的選擇，本文將其與90%及99%的信心區間做一比較測試。這項新發展的模型改良法，已執行於多變數多項展開式之級數模型（線性、泰勒級數以及冪級數模型）中，爲安全評估提供了更精確的結構辨識。

關鍵字

統計信心區間 ； 模型改良法 ； 健康診斷

並列摘要

This research proposes a statistical confidence interval-based model refinement approach for the health diagnosis of structural systems subjected to seismic excitations. The proposed approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the confidence interval of the parameters covers the ”null” value, it is statistically sustainable to truncate them. Thus, the remaining parameters repetitively undergo such a sifting process for model refinement until all statistical significance cannot be further improved. Other confidence intervals, such as the 90% and 99%, of structural parameters are also tested for comparison and validation purposes. This newly developed model-refinement approach is implemented for the developed series models of multivariable polynomial expansions: the linear, as well as the Taylor and the power series models, thereby leading to a more accurate identification of structures for safety assessment.

並列關鍵字

statistical confidence interval ； model refinement approach ； health diagnosis