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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8250完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖文正(Wen-Cheng Liao) | |
| dc.contributor.author | Ho-Cheng Huang | en |
| dc.contributor.author | 黃禾程 | zh_TW |
| dc.date.accessioned | 2021-05-20T00:50:46Z | - |
| dc.date.available | 2023-08-11 | |
| dc.date.available | 2021-05-20T00:50:46Z | - |
| dc.date.copyright | 2020-09-22 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8250 | - |
| dc.description.abstract | 進行混凝土結構物設計、興建與維護管理時，需考量強度、變形及耐久性以達到長期安全及服務性，然而實務上多未能準確考量收縮和潛變所造成的影響。國內外對於混凝土收縮與潛變已有長期的試驗與研究發展，且也提出多種收縮及潛變預測模型。台灣於2017年建立了「台灣混凝土潛變收縮資料庫」，並選用Model B4為基礎提出了本土化收縮潛變預測模型Model B4TW (2017)。 Model B4TW (2017) 雖然已針對台灣多項混凝土特性進行修正，包含高膠結材含量、低彈性模數與低粒料勁度等，但仍未考慮骨材含量與對預測模型中各項水泥相關參數進行本土化修正。為對前述進行修正並使TW資料庫更加完善，本研究在「台灣混凝土潛變收縮資料庫」中新增了多項欄位，包含細粒料量、粗粒料量、細粒料比重、粗粒料比重、爐石細度與飛灰種類等，期望提出更準確且更符合物理意義的預測模型Model B4TW (2020)。 本研究使用Python 建立分析方法並選用 Microsoft SQL (MSSQL) 做為資料庫管理系統，而非傳統之 Excel 或者 Access，其原因在於透過 SQL 語法之可攜性可輕易進行資料篩選與分析，且較適用於處理大量數據，效率遠勝傳統資料庫分析方法。近年來資料科學發展興盛，本研究透過使用多種機器學習演算法對台灣收縮與潛變數據進行回歸與預測，期望提出更準確的預測模型。為方便工程師以及各國學者在不需進行任何複雜操作以及程式碼處理的情況下使用本研究所提出的各項成果，本研究透過將資料庫雲端化，並使用ASP.net建置線上即時分析網頁S.C.D.T (Shrinkage and Creep Database in Taiwan)，使用者僅須在網頁上輸入所需參數便可以迅速得到各模型收縮與潛變預測成果。 造成預力混凝土橋梁長期變形的原因，主要可歸因於混凝土收縮與潛變所引致的預力損失，目前各國學者與規範所提出之收縮與潛變模型在預測台灣混凝土時皆呈現低估的現象。為探討並比較國內外各模型應用於橋梁長期變位分析之差異，本研究選用多種混凝土收縮潛變預測模型，包含ACI 209R-92、AASHTO LRFD 2014、CEM MC90、CEB MC10、GL2000、Model B3以及Model B4TW (2020)等，使用MIDAS Civil建立預力箱型橋梁模型進行分析。比較分析結果後發現，若直接套用國外預測模型，將嚴重低估橋梁長期變位與預力損失，導致使用年限高估。本研究期望發展出一套適合台灣混凝土預力橋梁長期變形的分析模式，使工程界能更準確掌握台灣混凝土橋梁長期變形量以及評估使用年限，利於安全監測及適時進行維護，以避免災害發生並延長混凝土結構物的生命週期。 | zh_TW |
| dc.description.abstract | Under design, construction and maintenance of concrete structures, strength, deformation and durability must be taken into consideration in order to reach long-term safety and serviceability. However, the effect of shrinkage and creep is rarely considered in practice. Experiments and researches on shrinkage and creep of concrete have been developed over decades all around the world. Numerous shrinkage and creep prediction models have been proposed. In 2017, Shrinkage and Creep Database of Concrete in Taiwan (TW Database) was established. A localized prediction formula, Model B4TW (2017) was proposed to predict shrinkage and creep of concrete in Taiwan. Model B4TW (2017) takes Model B4 as its base model and modifies with local characteristics of concrete in Taiwan, including high cementitious material content, low concrete elastic modulus and low aggregate stiffness. Even though Model B4TW (2017) already has considerable high prediction accuracy, modification on cement related coefficient and the effect of aggregate content were not yet considered. To modify on aforementioned and integrate TW database, this study adds several parameters, including coarse aggregate content, fine aggregate content, specific gravity of aggregate, slag type and fly ash type into TW database; looking forward to proposing the Model B4TW (2020) a prediction model with higher accuracy and satisfied physical principles.. This study uses Microsoft SQL as database management system and Python to establish all processing procedures. The reason why Microsoft SQL was selected rather than traditional software such as Excel and Access is that SQL is highly portable and more suitable when handling large datasets. By combining Microsoft SQL and Python, the efficiency of analysis can be greatly enhanced. In recent years, data science has gained popularity all over the globe. This research selected several machine learning algorithms to predict shrinkage and creep of concrete; expecting to propose a prediction model with even higher accuracy. In order to enable engineers and scholars to make use of the result proposed by this research without the need of complicated calculation and ability of coding, an online analysis webpage S.C.D.T (Shrinkage and Creep Database in Taiwan) was established using ASP.net. By inputting parameters required, users can easily and quickly obtain prediction results of shrinkage and creep calculated by each model. The cause of long-term deflection of prestressed concrete bridges are mainly attributed by tendon loss induced by shrinkage and creep of concrete. All prediction models available at present underestimate shrinkage and creep strain of concrete in Taiwan. In order to investigate and compare long-term behavior of prestressed concrete bridge, this research applies multiple prediction models, including ACI 209R-92、AASHTO LRFD、CEM MC90、CEB MC10、Model B3 and Model B4TW (2020) to prestressed concrete box girder bridge model established by MIDAS Civil. Analysis results show that if foreign prediction model is directly applied in designing prestressed concrete bridge in Taiwan, maximum deflection of the bridge will be underestimated and service life will be overestimated. This research developed a long-term deformation analysis which enables engineers to accurately calculate long-term deflection, tendon loss and service life of prestressed concrete bridges in Taiwan. The Analysis results also enable engineers to monitor and provide retrofit when needed, extend the service life of structure during the design and maintenance phase to achieve sustainability. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T00:50:46Z (GMT). No. of bitstreams: 1 U0001-1108202022501900.pdf: 27290440 bytes, checksum: 895af13434fab75bb1c261a1282136df (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 摘要 i Abstract iii 目錄 v 表目錄 xii 圖目錄 xviii 第一章、 緒論 1 1.1 研究動機與目的 1 1.2 研究內容 3 1.3 研究流程圖 7 第二章、 文獻回顧 8 2.1 卜作嵐摻料 8 2.1.1 水淬高爐爐碴粉 9 2.1.2 飛灰 9 2.2 混凝土之收縮變形 10 2.2.1 混凝土收縮變形機制 10 2.2.2 影響混凝土自體收縮之因素 14 2.2.3 影響混凝土乾燥收縮之因素 17 2.3 混凝土之潛變變形 26 2.3.1 混凝土潛變變形機制 26 2.3.2 影響混凝土潛變之因素 29 2.4 預力混凝土介紹 36 2.4.1 預力混凝土之結構原理 36 2.4.2 預力損失簡介 38 2.4.3 預力損失種類介紹 39 2.5 機器學習介紹 42 2.5.1 機器學習種類 42 2.5.2 監督式學習介紹與流程 43 2.5.3 機器學習演算法簡介 45 2.5.4 各演算法常用之可調整參數說明 50 2.5.5 預測成果判定標準 50 第三章、 國內外混凝土收縮與潛變規範與統計指標 53 3.1 國外混凝土自體收縮變形預測模型 53 3.1.1 FIB2000 53 3.1.2 JCI 53 3.1.3 Model B4 54 3.2 國內混凝土自體收縮變形預測模型 55 3.2.1 Chiang 55 3.2.2 Model B4TW (2017) 56 3.3 國外混凝土總收縮變形預測模型 58 3.3.1 ACI 209R-92 58 3.3.2 AASHTO LRFD 2014 59 3.3.3 GL2000 59 3.3.4 CEB MC90 60 3.3.5 CEB MC90-99 61 3.3.6 CEB MC10 62 3.3.7 JSCE 1996 63 3.3.8 JSCE 2002 64 3.3.9 Model B3 65 3.3.10 Model B4 66 3.4 國內混凝土總收縮變形預測模型 69 3.4.1 Model CCL 69 3.4.2 Wu 70 3.4.3 Model B4TW (2017) 71 3.5 國外混凝土潛變變形預測模型 73 3.5.1 ACI 209R-92 73 3.5.2 AASHTO LRFD 2014 75 3.5.3 GL2000 75 3.5.4 CEB MC90 76 3.5.5 CEB MC90-99 78 3.5.6 CEB MC10 79 3.5.7 JSCE 1996 80 3.5.8 JSCE 2002 81 3.5.9 Model B3 81 3.5.10 Model B4 84 3.6 國內混凝土潛變變形預測模型 88 3.6.1 Model CCL 88 3.6.2 Model B4TW (2017) 89 3.7 國內各項統計指標介紹 93 3.7.1 決定係數R2 93 3.7.2 均方誤差MSE 93 3.7.3 BP coefficient of variation method 94 3.7.4 CEB statistical indicators 95 3.7.5 Gardner coefficient of variation 97 第四章、 國內外收縮潛變資料庫與分析方法介紹 99 4.1 台灣混凝土收縮潛變研究沿革 99 4.2 國內混凝土收縮潛變資料庫簡介 100 4.3 國內混凝土收縮潛變資料庫建立與分析方法 108 4.3.1 Microsoft SQL介紹 109 4.3.2 Python介紹 110 4.3.3 資料庫架構與分析方法 112 4.4 TW資料庫中各項參數說明與介面介紹 114 4.4.1 TW資料庫參數說明 114 4.4.2 TW資料庫介面介紹 118 4.5 國內外收縮潛變資料庫之比較 119 4.5.1 國內外資料庫各項重要參數分佈比較 119 4.5.2 台灣混凝土配比特性探討 122 4.6 TW資料用應用於各預測模型之預測結果 125 4.6.1 分析資料之剔除 125 4.6.2 分析結果表示方法 126 4.6.3 摻料混凝土之自體收縮分析結果 127 4.6.4 普通與摻料混凝土總收縮分析成果 128 4.6.5 普通與摻料混凝土基本潛變分析結果 132 4.6.6 普通與摻料混凝土總潛變分析結果 135 4.7 線上即時分析方法S.C.D.T之建立與使用簡介 139 4.7.1 ASP.net簡介 140 4.7.2 網頁架構簡介 141 4.7.3 網頁各項功能介紹 141 4.7.4 台灣混凝土收縮潛變網頁S.C.D.T優點 148 第五章、 Model B4TW (2020)收縮潛變預測模型發展 149 5.1 Model B4TW (2020)無因次項修正係數探討 149 5.2 普通混凝土收縮公式發展 151 5.2.1 普通混凝土收縮公式修正項目探討 151 5.2.2 普通混凝土自體收縮修正 152 5.2.3 普通混凝土總收縮修正 152 5.3 摻料混凝土收縮公式發展 164 5.3.1 摻料混凝土收縮修正項目探討 164 5.3.2 爐石混凝土收縮修正式發展 165 5.3.3 飛灰混凝土收縮修正式發展 170 5.3.4 爐灰混凝土收縮修正式發展 174 5.3.5 爐矽灰混凝土收縮修正式發展 178 5.4 普通混凝土潛變公式發展 181 5.4.1 普通混凝土潛變公式修正項目探討 181 5.4.2 瞬時彈性應變修正 182 5.4.3 普通混凝土基本潛變修正 184 5.4.4 普通混凝土總潛變修正 189 5.5 摻料混凝土潛變公式發展 195 5.5.1 摻料混凝土潛變修正項目探討 195 5.5.2 爐石混凝土潛變修正式發展 196 5.5.3 飛灰混凝土潛變修正式發展 200 5.5.4 爐灰混凝土潛變修正式發展 204 5.6 台灣混凝土收縮與潛變預測建議公式與分析結果 208 5.6.1 Model B4TW (2020) 總收縮建議公式 208 5.6.2 Model B4TW (2020) 自體收縮預測模型分析結果比較 212 5.6.3 Model B4TW (2020) 總收縮預測模型分析結果比較 214 5.6.4 Model B4TW (2020) 潛變建議公式 216 5.6.5 Model B4TW (2020) 基本潛變預測模型分析結果比較 220 5.6.6 Model B4TW (2020) 總潛變預測模型分析結果比較 222 第六章、 以機器學習預測混凝土收縮與潛變變形 225 6.1 機器學習演算法適用性分析 225 6.2 以機器學習預測收縮變形 228 6.2.1 以Decision Tree預測TW資料庫總收縮 229 6.2.2 以Extra Tree預測TW資料庫總收縮 232 6.2.3 以Random Forest預測TW資料庫總收縮 234 6.2.4 以Extra Trees預測TW資料庫總收縮 237 6.2.5 以Gradient Boosting Regressor預測TW資料庫總收縮 240 6.2.6 以XGBoost預測TW資料庫總收縮 242 6.2.7 應用機器學習預測總收縮之結果比較 244 6.3 以機器學習預測潛變變形 245 6.3.1 以Decision Tree預測TW資料庫潛變 247 6.3.2 以Extra Tree預測TW資料庫潛變 249 6.3.3 以Random Forest預測TW資料庫潛變 251 6.3.4 以Extra Trees預測TW資料庫潛變 253 6.3.5 以Gradient Boosting Regressor預測TW資料庫潛變 255 6.3.6 以XGBoost預測TW資料庫潛變 258 6.3.7 應用機器學習預測潛變之結果比較 260 6.4 機器學習之分析結論與應用於實務上之問題討論 261 第七章、 MIDAS Civil預力橋梁模型建立與分析結果 267 7.1 MIDAS Civil 分析研究背景 267 7.2 MIDAS Civil介紹 267 7.3 案例介紹 268 7.4 模型建立與參數設定 273 7.4.1 定義材料與斷面 273 7.4.2 建立結構模型 274 7.4.3 自定義收縮與潛變數值 277 7.4.4 定義邊界條件與載重 279 7.4.5 定義施工階段 280 7.4.6 分析方法之設定 284 7.5 MIDAS Civil分析結果 284 7.5.1 混凝土材料潛變預測差異 284 7.5.2 混凝土材料收縮預測差異 287 7.5.3 分析結果與討論 290 7.5.4 應用不同強度混凝土之分析結果 300 7.5.5 簡化分析之可行性探討 310 第八章、 結論與建議 325 8.1 結論 325 8.2 建議 328 參考文獻 330 附錄 A 340 | |
| dc.language.iso | zh-TW | |
| dc.title | 以資料庫回歸台灣混凝土收縮與潛變預測模型並應用於預力橋梁長期變位分析 | zh_TW |
| dc.title | Regression of Shrinkage and Creep Prediction Model of Concrete in Taiwan Based on Database Analysis and Application to Long-term Deflection Analysis of Prestressed Concrete Bridge | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 詹穎雯 (Yin-Wen Chan),王勇智(Yung-Chih Wang),歐昱辰(Yu-Chen Ou) | |
| dc.subject.keyword | 混凝土,卜作嵐材料,收縮,潛變,資料庫,機器學習,預力混凝土橋,長期變形,預力損失, | zh_TW |
| dc.subject.keyword | Concrete,Pozzolanic Material,Shrinkage,Creep,Database,Machine Learning,Prestressed Concrete Bridge,Long-term Deflection,Tendon Loss, | en |
| dc.relation.page | 345 | |
| dc.identifier.doi | 10.6342/NTU202003020 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2020-08-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-11 | - |
| 顯示於系所單位： | 土木工程學系 | |
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