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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂良正 | zh_TW |
| dc.contributor.advisor | Liang-Jenq Leu | en |
| dc.contributor.author | 楊晏瑜 | zh_TW |
| dc.contributor.author | Yen-Yu Yang | en |
| dc.date.accessioned | 2023-01-09T17:04:11Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-01-06 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2002-01-01 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83142 | - |
| dc.description.abstract | 台灣位屬多地震區域，由於地震頻繁，為了結構物之安全性，需於定期進行結構物健康檢測，尤其是災害發生後能確保結構物之安全。設置感測器極為耗成本且耗時，故本文所提出之最佳化配置為減少再次檢測同棟結構物時所需之感測器顆數，並且提高判斷結構物模態頻率的精度。為此目的，本文結合了四種方法進行最佳化配置，分別為隨機子空間識別法、三次樣條內插法、K-means演算法與基因演算法等方法判斷出加速度感測器配置之最佳樓層位置。提出各種不同模型，並與模態頻率解析解相互比較，驗證此法能以少數層樓之時間歷時，得到整體結構物的模態頻率，以確保結構物之安全性。為了驗證此方法之可行性，於數值模擬上，本研究建立幾個不同勁度折減之模型，再次驗證結構物災害發生前以及災害發生後，最佳配置差異；在現地實驗方面，本研究以六棟真實結構物進行實驗，分別為國立台灣大學土木研究大樓、國立台灣大學醫學院附設癌醫中心、淡水施工中20層樓建築物、板橋兩棟20與21層樓建築物及國家地震工程研究中心等建築，並以傅立葉快速轉換法來驗證此本文方法之可行性，由各種數值模擬以及實驗上證明。故本文最主要的貢獻在於提出之感測器最佳配置法，可以大幅減少第二次檢測時所使用之感測器數量與獲得較為精準之中高模態頻率，作為日後檢測之重要相關依據，當建築物受到損害時，即時檢測以瞭解建築物之受損情形。 | zh_TW |
| dc.description.abstract | 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 placements (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. In this study, created some model to proved this method that can obtained the high accuracy modal frequency, and then created some model which stiffness decrease to proved that used OSP method to obtain modal frequency before and after stiffness decrease which similar with real modal frequency. There are six in-situ experiments for the method verifying. The six in-situ building are NTU Civil Engineering Research Building, NTU Cancer Center, Tamsui building (under construction), two apartment complex in Banqiao and National Center for Research on Earthquake Engineering, respectively. The OSP method can obtained great result from these in-situ experiments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-09T17:04:11Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-01-09T17:04:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目錄 致謝 I 摘要 III ABSTRACT V 目錄 i 圖目錄 iv 表目錄 viii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 文章架構 4 第二章 系統識別方法 6 2.1 微振量測 6 2.2 快速傅立業轉換(Fast Fourier Transform) 7 2.3 狀態空間 8 2.3.1 連續時間狀態空間方程式 9 2.3.2 離散時間狀態空間方程式 10 2.3.3 隨機狀態空間方程式 11 2.4 隨機過程 12 2.5 隨機子空間識別法 13 2.5.1協方差隨機子空間識別法 14 2.6 系統模態參數 16 2.7 穩態圖 17 2.7.1穩態圖實例 17 2.8 三次樣條內插法 20 2.8.1 CSI曲線演算法原理 21 2.8.2 CSI可行性探討 22 2.9 小結 28 第三章 分群與最佳化演算法 30 3.1 K-means演算法 30 3.2 基因演算法 34 3.2.1基因演算法原理與操作 35 3.2.2適應函數 36 3.2.3基因演算法流程 37 3.3 最佳感測器配置流程 38 第四章 最佳化感測器配置-利用模擬試驗數據 42 4.1 直接積分Newmark-β 法 42 4.2 數值模擬-外力形式 43 4.2.1 自由振動 44 4.2.2 白噪音外力 49 4.3 濾波與重新取樣 52 4.3.1 濾波 52 4.3.2 重新取樣 54 4.4 不同樓高模擬 55 4.4.1 20層樓模擬 57 4.4.2 頻率分解法 59 4.4.3 不同樓高模擬 64 4.5 特殊分配法 68 4.5.1 使用與不使用CSI之差異探討 68 4.5.2 加權配置法 (Weighting Sensors Placement, WSP ) 70 4.5.3 模態振型配置法(Modal Shape Sensor Placement, MSSP) 78 4.5.4 特殊分配法小結 84 4.6 勁度折減前後分析 86 4.7 小結 90 第五章 最佳化感測器配置-利用現地實驗數據 92 5.1實驗儀器介紹 92 5.2台灣大學土木研究大樓 94 5.3台灣大學醫學院附屬癌醫中心 103 5.4淡水20層樓施工中建築物 107 5.5板橋區A棟20層與B棟21層社區大樓 111 5.6國家地震工程研究中心 117 5.7小結 122 第六章 結論與未來展望 124 6.1結論 124 6.2未來展望 127 6.2.1方法改進 127 6.2.2未來展望 127 參考文獻 129 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 系統識別 | zh_TW |
| dc.subject | 感測器最佳配置 | zh_TW |
| dc.subject | K-means分群法 | zh_TW |
| dc.subject | 三次樣條內插法 | zh_TW |
| dc.subject | 頻率域分解法 | zh_TW |
| dc.subject | 隨機子空間識別法 | zh_TW |
| dc.subject | 結構健康監測 | zh_TW |
| dc.subject | Stochastic subspace identification | en |
| dc.subject | Cubic-spline interpolation | en |
| dc.subject | Optimal sensor placements | en |
| dc.subject | K-means clustering | en |
| dc.subject | System identification | en |
| dc.subject | Structural health monitoring | en |
| dc.subject | Frequency domain decomposition | en |
| dc.title | 建築物微振量測之最佳感測器配置 | zh_TW |
| dc.title | Optimal Sensor Placements for Ambient-Vibration Monitoring of Building | en |
| dc.title.alternative | Optimal Sensor Placements for Ambient-Vibration Monitoring of Building | - |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 羅俊雄;張國鎮;韓仁毓;宋裕祺;黃仲偉 | zh_TW |
| dc.contributor.oralexamcommittee | Chin-Hsiung Loh;Kuo-Chun Chang;Jen-Yu Han;Yu-Chi Sung;Chang-Wei Huang | en |
| dc.subject.keyword | 系統識別,結構健康監測,隨機子空間識別法,頻率域分解法,三次樣條內插法,K-means分群法,感測器最佳配置, | zh_TW |
| dc.subject.keyword | System identification,Structural health monitoring,Stochastic subspace identification,Frequency domain decomposition,Cubic-spline interpolation,K-means clustering,Optimal sensor placements, | en |
| dc.relation.page | 135 | - |
| dc.identifier.doi | 10.6342/NTU202200895 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2022-10-11 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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|---|---|---|---|
| U0001-0906202211162500.pdf | 22.49 MB | Adobe PDF | 檢視/開啟 |
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