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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭安妮(Annie On-Lei Kwok) | |
| dc.contributor.author | Guan-Peng Chen | en |
| dc.contributor.author | 陳冠朋 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:28:21Z | - |
| dc.date.copyright | 2022-09-26 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-23 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85903 | - |
| dc.description.abstract | 在地震工程的研究中得知影響地震動的因素相當複雜，主要分為震源效應、路徑效應和場址效應。尤其是地震波進入盆地後，會受到盆地幾何形狀及其鬆軟質沉積物的影響，會產生明顯的地震波振幅放大現象。台北地區為地質構造特殊的盆地，因此台北地區的地震動反應為本研究的目標，在這項研究中主要探討兩個地震動預測目標，第一是地震動振幅，第二是場址放大因子。 傳統的地震動放大預測模型利用迴歸方程式進行分析，常用的預測參數包括 Vs30和 PGAr。對於具有盆地地形或不規則幾何形狀的場址，可能需要額外的參數來有效量化場址效應，但是這些參數不容易透過迴歸方程式進行量化分析。 隨著電腦科技的進步，利用機器學習來開發地震動的預測模型也成為一種有效的方法。本研究在眾多機器學習演算法中主要使用 XGBOOST 演算法，利用地震資料特徵和測站資料特徵建立數據庫，來建立地震動的預測模型進行預測，並與其他演算法的預測結果進行比較。機器學習與回歸模型相比，機器學習在預測時往往具有更好的準確性，但它也失去了線性模型的可解釋性。因此機器學習的可解釋性需要通過 SHAP 值(一種廣泛適用的解釋模型方法)來改善，並針對輸入-輸出依賴關係進行說明。最後比較機器學習和傳統迴歸分析所建立地震動的預測模型的分析結果。 | zh_TW |
| dc.description.abstract | In ground motion modeling, the influences from earthquake source, path, and site are usually considered separately. For sites with special geometry, such as basin, the wave propagation process can be complex as the ray path would be affected by the basin geometry and the properties of the soft sediments within the basin. The City of Taipei is located in a basin with special geological structure, so the site effect on the ground motion is always the topic of interest. Conventionally, empirical ground motion prediction models were developed using regression method. Typical prediction parameters for capturing site effect include the average shear wave velocity in the upper 30 meters (Vs30) and peak ground acceleration expected on rock site (PGAr). For sites with irregular topography or subsurface geometry, additional parameters may be required to effectively quantify the site effect. However, these parameters may not be easily identified. With the advancement of computer technology, the use of machine learning to develop ground-motion prediction models becomes plausible. In this study, the XGBOOST technique is used to develop models for predicting the ground motion amplitudes (such as peak ground acceleration and spectral acceleration at a particular period) and the amplification factors. The definition of amplification factor adopted in this study is the ratio of an intensity measure for a particular site to the same intensity measure for the reference site. Compared to the models developed by the regression method, the XGBOOST model tends to have a better prediction accuracy. The interpretability of the models developed by the XGBOOST method is achieved by examining the SHAP value, which can be used to explain the physical and quantitative interpretation of the input-output dependencies. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:28:21Z (GMT). No. of bitstreams: 1 U0001-1909202218191400.pdf: 14732670 bytes, checksum: 75b0ce33b2dac94be639762b98ddc606 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 I 謝辭 II 摘要 III ABSTRACT IV CONTENTS VI LIST OF FIGURES X LIST OF TABLES XVI CHAPTER 1 INTRODUCTION 1 1.1 Introduction 1 1.2 Research Method 2 1.3 Thesis Organization 3 CHAPTER 2 LITERATURE REVIEW 5 2.1 Site Effect 5 2.1.1 Compilation of Data on Local Site Effect 5 2.1.2 Multiple Station Method 8 2.1.3 Single Station Method 10 2.2 Machine Learning (ML) Application 12 2.2.1 Ensemble Learning in Machine Learning Technology 12 2.2.2 Machine learning for Evaluating Seismic Liquefaction Potential 14 2.2.3 Machine learning in ground motion prediction 15 CHAPTER 3 GEOLOGY OF TAIPEI BASIN AND DATA COLLECTION 23 3.1 Geology of Taipei Basin 23 3.1.1 Quaternary Tectonic Evolution 23 3.1.2 Geology of Taipei Basin 24 3.2 Data Collection and Processing 29 3.2.1 Seismic Data Collection 29 3.2.2 Station Data Collection 30 3.2.3 Site Parameters 31 3.3 Statistical Distribution 45 CHAPTER 4 MODEL DEVELOPMENT 58 4.1 Algorithm 60 4.1.1 Decision Tree 60 4.1.2 Random forests 73 4.1.3 XGBOOST 76 4.2 Ground Motion Amplification Model Development 89 4.2.1 Development of different models 89 4.2.2 Hyperparameter tuning 92 4.2.3 Results of Model Prediction 98 4.2.4 SHAP Value 100 4.2.5 Interpretable Machine Learning 106 4.3 Ground Motion Amplification Model performance 120 4.3.1 Comparison between XGBOOST Model and Different Models 120 4.3.2 Comparison between Regression and Other ML 124 4.3.3 Model diagnostics 130 4.3.4 Estimation of model uncertainty 131 4.4 Ground Motion Amplitude Model Development 134 4.4.1 Development of different models 134 4.4.2 Hyperparameter tuning 137 4.4.3 Results of Model Prediction 137 4.4.4 Interpretable Machine Learning 138 4.5 Ground Motion Amplitude Model performance 153 4.5.1 Comparison between XGBOOST Model and Different Models 153 4.5.2 Comparison between Regression and Other ML 156 4.5.3 Model diagnostics 159 4.5.4 Estimation of model uncertainty 160 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 162 5.1 Conclusions 162 5.2 Recommendations 163 REFERENCE 164 APPENDIX 167 a. Hyperparameter tuning 167 AF 167 GM 169 b. Interpretable Machine Learning 171 AF 171 GM 193 c. Estimation of model uncertainty 213 AF 213 GM 220 | |
| dc.language.iso | en | |
| dc.title | 以XGBOOST演算法探討台北地區地震動預測研究 | zh_TW |
| dc.title | Ground Motion Prediction for Taipei Area Using XGBOOST Technique | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭俊翔(Chun-Hsiang Kuo),許尚逸(Shang-Yi Hsu) | |
| dc.subject.keyword | 機器學習,場址放大因子,台北盆地,極限梯度提升,夏普利值, | zh_TW |
| dc.subject.keyword | Machine learning,Taipei Basin,Amplification factor,XGBOOST,SHAP value, | en |
| dc.relation.page | 225 | |
| dc.identifier.doi | 10.6342/NTU202203597 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-09-24 | |
| 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 | 2022-09-26 | - |
| 顯示於系所單位： | 土木工程學系 | |
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