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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭安妮(Annie On-Lei Kwok) | |
| dc.contributor.author | Wei-En Hsu | en |
| dc.contributor.author | 徐爲恩 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:02:05Z | - |
| dc.date.available | 2021-11-06 | |
| dc.date.available | 2022-11-23T09:02:05Z | - |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-06 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79504 | - |
| dc.description.abstract | 隨機森林(random forest)模型是由Breiman和Culter在2001年提出之機器學習演算法。通過在森林中建構一棵棵各自獨立之決策樹(分類樹或迴歸樹)，並以投票方式(眾數或平均數)決定最終結果，以此提高模型之預測精度。該建模方式之優點包含運轉速度快、處理大量數據時表現優異、可提供特徵參數之重要性以及便於計算特徵參數之非線性作用，並提供特徵參數間之交互作用。 綜觀歷年地盤放大係數之研究，多數研究者於不同地區提供之迴歸方程式中所使用之參數不乏用以表示線性區間之VS30以及非線性區間之PGAr。由於難以有效將其餘對地盤放大係數產生影響之因子進行量化，致使該作法對於特殊地形或不規則地下岩盤之特定區域無法順利奏效。此外，由於研究區域乃半地塹之構造盆地，在特定入射方向上之震波可能會引致盆地中彈性波之建設性干涉而產生表面波，使得盆地內部分地點之主頻與震幅發生變化。有鑑於此，本研究將基岩深度與地表地形納入考量，透過自由場測站之加速度資料求得譜加速度，並使用TAP071測站作為參考測站以計算臺北盆地之地盤放大係數。接續彙整各項場址參數及地盤放大係數，分別作為數據庫之特徵參數與預測值，並使用Python作為建模環境以建立隨機森林模型。利用傅立葉分析觀察不同地震下之各測站頻域，比較傳統迴歸分析與隨機森林演算法之地盤放大係數預估值。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:02:05Z (GMT). No. of bitstreams: 1 U0001-0210202117344700.pdf: 24842433 bytes, checksum: ef843f26f4627a41f3f8a2c5f598ce33 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | CONTENTS 誌謝 I 摘要 II ABSTRACT III CONTENTS V LIST OF FIGURES VII LIST OF TABLES XIX Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Method 3 1.3 Thesis Organization 4 Chapter 2 Literature Review 5 2.1 Local Site effect 6 2.1.1 Phenomenon 6 2.1.2 Empirical Methods 14 2.1.3 One-Dimensional Ground Response Analysis 18 2.2 Taipei Basin 21 2.2.1 Geology 21 2.2.2 Formations 23 2.2.3 Faults 28 2.2.4 Quaternary Tectonic Evolution 30 2.2.5 Ground Motion 31 2.3 Machine Learning (ML) Application for Geotechnical Engineering 36 2.3.1 Liquefaction 36 2.3.2 Landslide Susceptibility 37 2.3.3 Hydraulic Conductivity 38 2.3.4 Summary 38 Chapter 3 Random Forest 40 3.1 Bagging 40 3.2 CART 41 3.2.1 Structure 42 3.2.2 Splitting Decision 42 3.2.3 Example 44 3.3 Algorithm 51 3.4 Model Explainability 53 3.4.1 Variable Importance 54 3.4.2 Partial Dependence Plot 55 3.4.3 Shapley Value 55 Chapter 4 Data Collection and Analysis Methods 58 4.1 Data Collection and Processing 58 4.2 Statistical Distribution 69 4.3 Model Development 82 4.3.1 Preliminary Model 82 4.3.2 Observation of Surface Wave 90 4.3.3 Revised Model 165 4.3.4 Summary 177 4.4 Model Prediction 178 Chapter 5 Discussion on The Explainability of Amp Model Based on RF 184 5.1 Discussion on Variable Importance 185 5.2 Discussion on Partial Dependence Plots 191 5.3 Discussion on SHAP Summary Plots 204 Chapter 6 Conclusions and Recommendation 211 6.1 Conclusions 211 6.2 Recommendation 213 References 215 | |
| dc.language.iso | en | |
| dc.title | 以隨機森林方式建立臺北盆地之地盤放大係數模型 | zh_TW |
| dc.title | Development of Ground Motion Amplification Model for Taipei Basin Using Random Forest Technique | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭俊翔(Hsin-Tsai Liu),許尚逸(Chih-Yang Tseng) | |
| dc.subject.keyword | 場址效應,臺北盆地,反應譜,地盤放大係數,隨機森林, | zh_TW |
| dc.subject.keyword | Basin effect,Taipei Basin,amplification factor,random forest, | en |
| dc.relation.page | 219 | |
| dc.identifier.doi | 10.6342/NTU202103510 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-08 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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