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研究生(外文):Yen-Jen Chang
論文名稱(外文):Integrated Fuzzy Clustering Neural Network Structure for Evaluating Liquefaction of Soil.
指導教授(外文):Shuh-Gi Chern
外文關鍵詞:clusteringdivide-and-conquer methodologyneuro-fuzzy systemsoil liquefaction
  • 被引用被引用:7
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There are hundred of cases of ground failure and damage to structures due to liquefaction during earthquakes in Japan, Chile, China, Central America, and the United State. Recently in 1999, considerable amount of damage had been caused in central part of Taiwan by Chi-Chi earthquake induced liquefaction. Numerous studies have been conducted to understand the behavior of saturated cohesionless soil under earthquake loading. A lot of theoretical and empirical methods have also been developed for evaluation of liquefaction potential. Many of the existing assessment methods are developed from observations of the performance of sites during earthquake. Using field record like STP-N or CPT-qc, simplified empirical correlations are established between the soil and the seismic properties and the occurrence or nonoccurrence of liquefaction at the site. However, current simplified methods seem not so suitable and may fail for evaluating some sites of Chi-Chi earthquake induced liquefaction. This paper presents a neuro-fuzzy system to solve these complex problems.
In this study, a neuro-fuzzy system is developed, based on divide-and-conquer methodology, integrated fuzzy system, clustering algorithm, and neural network method. Consequently, the Chi-Chi earthquake data as well as other reported data are used to evaluate earthquake induced liquefaction potentials. With the help of case studies, it is shown that the proposed model has less error rate than conventional empirical methods, and is able to predict liquefaction potentials much more satisfactory than other neural network methods.
摘要 I
Abstract III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1-1 前言 1
1-2 研究動機 1
1-3 研究方法 2
1-4 研究內容 3
第二章 文獻回顧 6
2-1 群聚分析 6
2-1-1 群聚分析演算法 6
2-1-2 模糊 C-means群聚分析 7
2-1-3 機率 C-means群聚分析 9
2-2 類神經網路 11
2-2-1 類神經網路的發展背景 11
2-2-2 生物神經元與人工神經元 13
2-2-3 類神經網路在大地工程領域之應用 14
2-3 模糊理論 15
2-3-1 模糊理論的發展背景 15
2-3-2 模糊集合與歸屬函數 16
2-3-3 模糊系統與模糊規則 17
2-3-4 模糊理論在大地工程領域之應用 18
第三章 土壤液化潛能評估 22
3-1 前言 22
3-2 土壤液化之定義 22
3-3 土壤液化之機制 23
3-4 影響土壤液化潛能之因素 24
3-4-1 現地狀況 24
3-4-2 土壤性質 25
3-4-3 地震特性 27
3-5 土壤液化評估方法 27
3-5-1 Seed簡易經驗法 28
3-5-2 Tokimatsu & Yoshimi液化評估法 30
3-5-3 日本道路橋液化評估法 31
3-5-4 新日本道路橋液化評估法 33
3-5-5 NCEER修正的Seed簡易經驗法 36
第四章 整合式模糊群聚類神經網路架構 47
4-1 前言 47
4-2 系統模式 47
4-3 模糊規則萃取 48
4-3-1 修正型PCM群聚分析 49
4-3-2 分類適切性指標 52
4-3-3 規則補集 52
4-4 學習法則 53
4-4-1 架構學習 54
4-4-2 參數學習 55
第五章 案例分析 61
5-1 前言 61
5-2 資料處理 61
5-2-1 資料蒐集 61
5-2-2 輸入參數選擇 62
5-2-3 輸入參數正規化 64
5-3 系統參數設定 64
5-4 分析結果 67
5-4-1 類神經模糊系統與傳統液化評估法分析結果比較 67
5-4-2 類神經模糊系統與倒傳遞類神經網路分析結果比較 68
5-5 參數相對重要性 69
第六章 結論與建議 91
6-1 結論 91
6-2 建議 91
參考文獻 93
附錄 99
Garson方法 99
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