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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66554完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 范正成(Jen-Chen Fan) | |
| dc.contributor.author | Chien-Lin Huang | en |
| dc.contributor.author | 黃建霖 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:42:34Z | - |
| dc.date.available | 2012-02-16 | |
| dc.date.copyright | 2012-02-16 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-01-13 | |
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(1992), “Microfine Cement/Sodium Silicate Grout”, Grouting, Soil Improvement and Geosynthetics ASCE, Geotechnical Special Publication No. 30, pp. 676-687. 41. Liao, K.W., Fan, J.C., and Huang, C.L. (2011) “An Artificial Neural Network for Groutability Prediction of Permeation Grouting with Microfine Cement Grouts”, Computers and Geotechnics, Vol. 38: pp. 978-986. 42. Maag, E. (1938), “Ueber die Verfestigung und Dictung des Bangrundes (injektionen)”, Course on soil mech., Zurich Tech. School. 43. Mollamahmutoglu, M., and Yilmaz, Y. (2011), “Engineering Properties of Medium-to-Fine Sands Injected with Microfine Cement Grout”, Marine Georesources and Geotechnology, Vol. 29, pp.95-109. 44. Naudts, A. and Landry, E. (2003), “New On-site Wet Milling Technology for the Preparation of Ultrafine Cement-based Grouts”, American Society of Civil Engineering, Vol. 127, pp. 1200-1207. 45. Ozgurel, H. G. and Vipulanandan, C. (2005), “Effect of Grain Size and Distribution on Permeability and Mechanical Behavior of Acrylamide Grouted Sand”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, Vol. 131, No.12, pp.1457-1465. 46. Paoli, D., Bosco, B., Granata, R. and Bruce, D.A. (1992), “Fundamental Observations on Cement Based Grouts (2): Microfine Cements and the Cemill Process”, Grouting, Soil Improvement and Geosynthetics ASCE, Geotechnical Special Publication. No. 30, pp. 486-499. 47. Pearson, K. (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space”, Philosophical Magazine, Vol. 2, No. 6, pp. 559–572. 48. Peng, G., Tisch, U., Adams, O., Hakim, M., Shehada, N., Broza, Y.Y., Billan, S., Abdah-Bortnyak, R., Kuten, A. and Haick, H. (2009) “Diagnosing Lung Cancer in Exhaled Breath Using Gold Nanoparticles”, Nature Nanotechnology, Vol. 4, pp. 669-673. 49. Perret, S., Palardy, D. and Ballivy, G. 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(2006), “Hydrocarbon Residuals and Containment in Microfine Cement Grouted Sand”, Journal of Materials in Civil Engineering, Vol. 18, No.2, pp. 214-228. 55. Schwarz, L.G. and Chirumalla, M. (2007), “Effect of Injection Pressure on Permeability and Strength of Microfine Cement Grouted Sand”, Grouting for Ground Improvement: GSP 168 Geo-Denver 2007: New Peaks in Geotechniques. 56. Soga, K., Au, S.K.A., Jafari, M.R., Bolton, M.D. (2004), “Laboratory Investigation of Multiple Grout Injections into Clay” Geotechnique, Vol. 54, No. 2, pp. 81–90. 57. Stephanie, P., Danielle, P. and Gerard, B. (2000), “Rheological Behavior and Setting Time of Microfine Cement-based Grouts”, ACI Materials Journal, Vol. 97, pp. 472-478. 58. Tekin, E. and Akbas, S.O. (2010), “Artificial Neural Networks Approach for Estimating the Groutability of Granular Soils with Cement-Basis Grouts” Bull Eng Geol Environ. Vol. 70: pp. 153-161. 59. Terzaghi, K. and Peck, R.B. (1948), “Soil Mechanics in Engineering Practice”, 1st edition, John Wiley ＆ Sons, Inc, N.Y. 60. Thiessen Team (2000), “The Development of Colloidal Mixer Based CRF Systems”, Reschke, A. E. The Development of Colloidal Mixer Based CRF Systems (present at MINEFILL’98, Brisbane, Australia, 1988) Rev. Aug. 2000. 61. Tung, C.P., and Chou, C.A. (2002), “Application of Tabu Search to Groundwater Parameter Zonation”, Journal of American Water Resources Association, Vol. 38, No. 4, pp.1115-1125. 62. Zebovitz, S., Krizek, R.J. and Atmatzidis, D.K. (1989), “Injection of Fine Sands with Very Fine Cement Grout”, Journal of Geotechnical Engineering, No. 12, pp. 1717-1733. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66554 | - |
| dc.description.abstract | 本研究的目的是要建構一個粉土質砂層超微粒水泥滲透灌漿可灌性的預測公式。因為本研究之粉土質砂層富含較高的細粒料含量以及所使用的超微粒水泥粒徑遠小於傳統的卜特蘭水泥，遂傳統可灌性相對粒徑比經驗公式無法有效的預測。雖然應用倒傳遞與幅狀基底函數類神經網路亦是一個能夠預測可灌性的方法，然因其無法提供一個明確的預測公式，所以於實際工程應用上有其限制。因此，本研究藉由所蒐集之240筆現地資料以啟發式演算法(禁忌演算法)，建構一可灌性之預測公式。除了參考傳統相對粒徑比可灌性經驗公式的格式外，土壤有效粒徑(d10)、土壤粒徑(d15)、細粒料含量(FC)與水灰比(W/C) 4個影響可灌性的因子亦納入考慮，其預測準確率可高達94.58%。再者，由本研究可灌性預測公式之良好預測結果顯示，應用禁忌演算法所建立之可灌性預測公式為相當可行的方法。此外，本研究所建構之預測公式與傳統相對粒徑比可灌性經驗公式有相似的格式，將更方便於工程師之使用也更易於實際工程上的普遍應用。 | zh_TW |
| dc.description.abstract | The goal of this study is to provide an accurate formula to predict the groutability (N) of silty sand soils using microfine cement grouts in a permeation grouting. Because the fines content (FC) of the silty sand soils studied is relatively high, and the size of the grouts used is significantly smaller than the Portland cement, the existing empirical formulas cannot deliver a promising prediction of N. Artificial neural networks such as BPNN or RBFNN are alternative tools used to predict N. However, ANNs do not provide an explicit formula, which creates an obstacle for practical engineers. Thus, a heuristic algorithm (the Tabu search, TS) was used to build the prediction formula. A total of 240 in-situ data samples were analyzed to ensure the accuracy of the proposed formula. The format of the existing empirical formula, which is commonly used in practice, was adopted in the proposed TS-based formula. Four parameters were considered in our TS models: the effective soil particle size (d10), the soil particle size (d15), the water-to-cement ratio (W/C) and the FC. The prediction accuracy of the TS-based formula was approximately 94.58%. With such a promising result, it is evident that the proposed formula is a suitable tool for predicting N. Because the proposed formula has a similar format to that of formulas that are typically used, the proposed approach can be implemented readily in practical engineering settings. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:42:34Z (GMT). No. of bitstreams: 1 ntu-101-D95622008-1.pdf: 13268760 bytes, checksum: de0e38e94268a3b98e39ec12b9f05c91 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 目 錄
謝誌 I 摘要 II ABSTRACT III 目 錄 IV 圖 目 錄 VI 表 目 錄 VIII 符號表 X 第一章 研究動機與目的 1 1.1 研究動機 1 1.2 研究目的 3 第二章 文獻回顧 5 2.1超微粒水泥研磨及攪拌技術發展 5 2.1.1超微粒水泥研磨技術發展 5 2.1.2超微粒水泥攪拌技術發展 6 2.2水泥相關材料之應用與成分配比的影響 8 2.1.1水泥相關材料之應用 8 2.1.2水泥成分配比的影響 10 2.3 滲透灌漿工法 16 2.3.1 灌漿材料的選擇 19 2.3.2 室內滲透灌漿試驗 22 2.4 既有的滲透灌漿可灌性評估方法 26 2.5 主成分分析 36 2.6 禁忌演算法 36 第三章 研究方法 40 3.1 現地灌漿資料 40 3.1.1資料樣本 40 3.1.2灌漿材料 44 3.1.3灌漿方式 49 3.2 可灌性預測分析 49 3.2.1敘述性統計及主成分分析 51 3.2.2 傳統可灌性經驗公式 54 3.2.3類神經網路可灌性的預測模式 56 3.2.4超微粒水泥滲透灌漿可灌性預測公式 57 3.2.4禁忌演算法 (Tabu Search) 58 3.3推論性統計 67 第四章 結果與討論 71 4.1 敘述性統計與可灌性因子的檢定 71 4.2 主成分分析 75 4.3 傳統相對粒徑比之可灌性經驗公式 87 4.4 超微粒水泥滲透灌漿可灌性預測公式 90 4.4.1 以禁忌演算法建立預測公式 94 4.4.2 不同資料組合的敏感度分析及推論統計檢定 110 4.5 類神經網路可灌性的預測模式 124 4.6 現地資料分析結果 127 4.7 綜合比較 133 第五章 結論與建議 134 5.1 結論 134 5.2 後續建議 135 附錄 143 附錄一：現地資料 144 附錄二：SAS 9.2 PROC UNIVARIATE輸出結果 148 附錄三：SAS 9.2 PROC MANOVA輸出的整理結果(可灌與不可灌資料) 162 附錄四：SAS 9.2 PROC NPAR1WAY輸出的整理結果(可灌與不可灌資料) 164 | |
| dc.language.iso | zh-TW | |
| dc.subject | 可灌性 | zh_TW |
| dc.subject | 禁忌演算法 | zh_TW |
| dc.subject | 超微粒水泥 | zh_TW |
| dc.subject | 滲透灌漿 | zh_TW |
| dc.subject | permeation grouting | en |
| dc.subject | groutability | en |
| dc.subject | tabu search | en |
| dc.subject | microfine cement | en |
| dc.title | 利用啟發式演算法建立可灌性預測公式 | zh_TW |
| dc.title | Using A Heuristic Algorithm to Build A Groutability Formula | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳榮河(Rong-Her Chen),林炳森(Ping-Sen Lin),林宏達(Horn-Da Lin),王藝峰(Yi-Fung Wang) | |
| dc.subject.keyword | 禁忌演算法,可灌性,超微粒水泥,滲透灌漿, | zh_TW |
| dc.subject.keyword | tabu search,groutability,microfine cement,permeation grouting, | en |
| dc.relation.page | 166 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-01-16 | |
| 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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