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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 范正成 | |
| dc.contributor.author | Po-Chou Lai | en |
| dc.contributor.author | 賴柏舟 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:53:44Z | - |
| dc.date.available | 2014-09-01 | |
| dc.date.copyright | 2012-08-19 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-12 | |
| dc.identifier.citation | 1. 米倉亮三、廖洪鈞、林英堂 (2002)，“恆久性灌漿材料與其灌漿砂土之動態行為”，地工技術，第93期5-12頁。
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64546 | - |
| dc.description.abstract | 本研究之目的為建立粉土質砂層超微粒水泥漿體滲透灌漿可灌性之預測模式。因為本研究區域為富含較高細粒料之粉土質砂層以及所使用之超微粒水泥粒徑遠小於傳統卜特蘭水泥，遂傳統相對粒徑比可灌性經驗公式無法有效預測。因此，本研究藉由蒐集台灣地區（台北及高雄）240筆超微粒水泥漿體現地灌漿資料以支持向量機配合禁忌演算法及羅吉斯迴歸分別建立可灌性預測模式及公式。選擇可能影響可灌性之因子，除了參考傳統相對粒徑比可灌性經驗公式所使用之土壤通過百分比為10%所對應之粒徑大小( )、土壤通過百分比為15%所對應之粒徑大小( )外，亦將細粒料含量(FC)與水灰比(W/C)納入考慮。透過支持向量機配合禁忌演算法建立之模式，以十種不同資料組合數進行驗證，其預測準確率之平均值可達97.75%。再者，由本研究可灌性預測模式良好之預測結果顯示，應用支持向量機配合禁忌演算法搜尋參數建立可灌性預測模式進行預測，為相當可行之方法，亦說明支持向量機在處理複雜且非線性問題上有相當良好之表現。此外，應用羅吉斯迴歸所建立之預測公式與傳統相對粒徑比可灌性經驗公式一樣具有簡單之方程式，方便於工程師使用，也期待能易於廣泛應用在實際工程上。 | zh_TW |
| dc.description.abstract | The purpose of this research is to establish the prediction model of the groutability of the silty sand soils using microfine cement grouts in a permeation grouting. Due to the fact that the region covered in this paper consists of the silty sand soils with relatively higher proportion of the fines content(FC) and the particle size of microfine cement used is considerably smaller than the conventional Portland cement, the existing empirical formula with relative particle size ratio is unable to provide effective predictions. Thus, this research derives the prediction model and formula from 240 data in Taiwan (Taipei and Kaohsiung) using Support Vector Machine(SVM) with Tabu Search(TS) and Logistic Regression(LR), respectively. In terms of selecting factors for the groutability, apart from the relative size for particles passing through soil with 10% and 15% permeability that are used in the conventional empirical formula with relative particle size ratio, this research also takes the fines content(FC) and the water-to-cement ratio(W/C) into account. By using SVM with TS, the model established can reach 97.75% precision of prediction. Moreover, the fine results of groutability prediction, not only indicate the feasibility of applying SVM with TS, but also explain the advantages of SVM in dealing with complicated and non-linear scenarios. In addition, the prediction formula derived from LR shares the same simplicity as in the conventional empirical formula with relative particle size ratio. It is hoped that, since engineers can use this formula with ease, it can also be widely used in applications and real-life constructions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:53:44Z (GMT). No. of bitstreams: 1 ntu-101-R99622012-1.pdf: 6406333 bytes, checksum: e8e85382a610dbe03a8ae1fee00aaf95 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 目 錄
摘 要 i Abstract ii 目 錄 iii 圖 目 錄 v 表 目 錄 vii 第一章 前言 1 1.1研究動機 1 1.2研究目的 2 1.3論文架構 3 第二章 文獻回顧 6 2.1超微粒水泥漿體滲透灌漿可灌性評估方法 6 2.1.1選取可灌性因子-主成分分析 6 2.1.2傳統可灌性經驗公式 8 2.1.3禁忌演算法可灌性預測公式 12 2.1.4類神經網路可灌性預測模式 14 2.2分析及預測方法 17 2.2.1支持向量機（Support Vector Machine, SVM） 17 2.2.2禁忌演算法（Tabu Search, TS） 19 2.2.3羅吉斯迴歸（Logistic Regression, LR） 20 第三章 研究方法 23 3.1現地灌漿資料 23 3.1.1資料樣本 23 3.1.2灌漿材料 26 3.1.3灌漿方式 29 3.2選取可灌性因子 32 3.3設定資料分組－訓練與測試 34 3.4模式之建立 37 3.4.1支持向量機（Support Vector Machine, SVM） 37 3.4.2禁忌演算法（Tabu Search, TS） 47 3.4.3羅吉斯迴歸（Logistic Regression, LR） 51 3.5評估方法之適用性 56 3.6模式準確度之評估 57 第四章 結果與討論 60 4.1支持向量機(SVM)配合禁忌演算法(TS)預測模式 60 4.2羅吉斯迴歸(LR)預測公式 68 4.3各預測模式準確率綜合比較 76 第五章 結論與建議 79 5.1結論 79 5.2建議 80 文獻回顧 81 附錄一：現地資料 90 附錄二：符號表 94 附錄三：程式 95 圖 目 錄 圖1.1 研究流程圖……………………………………………………….……...………5 圖2.1 主成分分析判別肺癌及健康呼氣之結果(摘自 Peng et al., 2009)……….……7 圖2.2 灌漿前後孔隙填充示意圖(摘自 Helal and Krizek, 1992).…………….………7 圖2.3 細顆粒含量與灌漿壓力(摘自 Ozgurel and Vipulanandan, 2005).……………11 圖2.4 水灰比及N值對可灌性關係圖(摘自 Axelsson et al., 2009).…………………11 圖2.5 雙曲型函數門檻值(s)的敏感度分析(摘自 Huang et al., 2010).…...…………16 圖2.6 雙曲型函數門檻值(s)的敏感度分析(摘自 Liao et al., 2011).…………...…...18 圖2.7 水力傳導度的設計分區情形(摘自 Tung and Chou, 2002).……………..……21 圖3.1 預定施灌區域-捷運新莊線CK570H區域(摘自 勤岩工程，2006).……..……24 圖3.2 灌漿改良範圍(摘自 勤岩工程，2006)…………………………………………24 圖3.3 預定施灌區域(摘自 勤岩工程，2005).……………………………….………25 圖3.4 部分現地照片(摘自 勤岩工程，2005).………………………….……………25 圖3.5 超微粒水泥與一般卜特蘭水泥粒徑分布曲線圖………………………..……31 圖3.6 一般卜特蘭水泥(a)與超微粒水泥(b)電子顯微圖………………….…………31 圖3.7 馬歇爾管低壓灌漿示意圖(摘自 勤岩工程，2005).…………………..………33 圖3.8 二重管低壓灌漿圖(摘自 勤岩工程，2006).……………………...………..…33 圖3.9 支持向量機資料分類示意圖(摘自 李文通，2008)….……………………..…39 圖3.10 最大分離間隔與最優分離超平面(摘自 Yonas B. Dibike1 et al., 2000).…...39 圖3.11 支持向量（SV）示意圖(摘自 李文通，2008).…………………………………42 圖3.12 線性不可分與鬆弛變量示意圖(摘自 Yonas B. Dibike1 et al., 2000).……...42 圖3.13 可容錯線性SVR 模式(摘自 Russell, J.S. et al., 1996).……………..………44 圖3.14 禁忌演算法演算流程圖………………………………………………………50 圖3.15 八個鄰近解的取點示意圖……………………………………………………50 圖3.16 羅吉斯函數圖形(摘自 林柏維，2010).……………………………….………54 圖4.1 支持向量機(SVM)中之參數γ與C值初始解為(0, 0)搜尋路徑……………..…61 圖4.2 支持向量機(SVM)中之參數γ與C值初始解為(10, 0)搜尋路徑………………62 圖4.3 支持向量機(SVM)中之參數γ與C值初始解為(10, 13)搜尋路徑…………..…62 圖4.4 支持向量機(SVM)中之參數γ與C值初始解為(5, 6)搜尋路徑………..………63 圖4.5 支持向量機(SVM)中之參數γ與C值初始解為(1, 1*10^6)搜尋路徑…………65 圖4.6 支持向量機(SVM)中之參數γ與C值初始解為(2, 2.1*10^8)搜尋路徑…..…...65 表 目 錄 表2.1禁忌演算法預測公式(1)十種不同資料組合數之準確率(摘自 黃建霖，2012).13 表2.2禁忌演算法預測公式(2)十種不同資料組合數之準確率(摘自 黃建霖，2012).13 表2.3禁忌演算法預測公式(3)十種不同資料組合數之準確率(摘自 黃建霖，2012).13 表2.4 不同因子之倒傳遞類神經網路預測模式之結果(摘自 Huang et al., 2010)....16 表2.5 四種影響因子之倒傳遞類神經網路預測模式之結果(摘自 黃建霖，2012)...16 表2.6 七個因子之幅狀基底函數類神經網路模式結果(摘自 Liao et al., 2011)……18 表2.7 四種影響因子之幅狀基底函數類神經網路模式結果(摘自 黃建霖，2012)..18 表3.1 孔位資料組數與水灰比………………………………………………………..27 表3.2 超微粒水泥基本定義…………………………………………………………..28 表3.3 超微粒水泥、爐石基本性質表………………………………………………...28 表3.4 超微粒水泥灌漿材料與一般卜特蘭水泥之基本性質比較表………………..30 表3.5 訓練與測試資料組數統計表…………………………………………………..35 表3.6 訓練與測試之各孔位資料組數表…………………………………..…………36 表3.7 分類誤差矩陣…………………………………………………………………..59 表4.1 禁忌演算法搜尋不同初始解之最佳解搜尋次數與準確率…………………..61 表4.2 強化搜尋精度與準確率………………………………………………………..63 表4.3 支持向量機(SVM)預測模式訓練195筆資料之結果………………………….67 表4.4 支持向量機(SVM)預測模式測試45筆資料之結果……………………..…….67 表4.5 支持向量機(SVM)預測模式全部240筆資料之結果………………………….67 表4.6 支持向量機(SVM)預測模式十種不同資料組合數之準確率………………...69 表4.7 LR_d15c十種不同資料組合數其可能影響因子與常數項之係數…………..71 表4.8 LR_d15十種不同資料組合數其可能影響因子與常數項之係數……………71 表4.9 LR_d10c十種不同資料組合數其可能影響因子與常數項之係數…………..72 表4.10 LR_d10十種不同資料組合數其可能影響因子與常數項之係數…………..72 表4.11 LR_d15c十種不同資料組合數之準確率…………..…………..……………73 表4.12 LR_d15十種不同資料組合數之準確率………………………...…..…...…..73 表4.13 LR_d10c十種不同資料組合數之準確率………………………...…..…...…74 表4.14 LR_d10十種不同資料組合數之準確率………………………...…..…...…..74 表4.15 四種羅吉斯迴歸(LR)預測公式十種不同資料組合數之全部準確率…....…75 表4.16 各種預測模式之準確率綜合比較………………………….…………...……77 | |
| dc.language.iso | zh-TW | |
| dc.subject | 超微粒水泥 | zh_TW |
| dc.subject | 滲透灌漿 | zh_TW |
| dc.subject | 可灌性 | zh_TW |
| dc.subject | 羅吉斯迴歸 | zh_TW |
| dc.subject | 禁忌演算法 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | Logistic Regression(LR) | en |
| dc.subject | groutability | en |
| dc.subject | Support Vector Machine(SVM) | en |
| dc.subject | microfine cement | en |
| dc.subject | permeation grouting | en |
| dc.subject | Tabu Search algorithm (TS) | en |
| dc.title | 應用支持向量機及羅吉斯迴歸法建立超微粒水泥漿體滲透灌漿可灌性預測模式 | zh_TW |
| dc.title | Using Support Vector Machine and Logistic Regression Methods to Build Groutability Models for Permeation Grouting with Microfine Cement Grout | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳榮河,童慶斌,廖國偉,黃建霖 | |
| dc.subject.keyword | 支持向量機,禁忌演算法,羅吉斯迴歸,超微粒水泥,滲透灌漿,可灌性, | zh_TW |
| dc.subject.keyword | Support Vector Machine(SVM),Tabu Search algorithm (TS),Logistic Regression(LR),microfine cement,permeation grouting,groutability, | en |
| dc.relation.page | 98 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-13 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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