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研究生:張延任
研究生(外文):Yen-Jen Chang
論文名稱:以整合式模糊群聚類神經網路架構評估土壤液化
論文名稱(外文):Integrated Fuzzy Clustering Neural Network Structure for Evaluating Liquefaction of Soil.
指導教授:陳俶季陳俶季引用關係
指導教授(外文):Shuh-Gi Chern
學位類別:碩士
校院名稱:國立海洋大學
系所名稱:河海工程學系
學門:工程學門
學類:河海工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:100
中文關鍵詞:群聚分析分工與一一克服方法論類神經模糊系統土壤液化
外文關鍵詞:clusteringdivide-and-conquer methodologyneuro-fuzzy systemsoil liquefaction
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  • 被引用被引用:7
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  • 收藏至我的研究室書目清單書目收藏:1
過去四十年來,全世界有許多國家,例如日本、智利、中國大陸、中美洲以及美國等發生大規模地震而引起土壤液化,造成人們生命與財產嚴重的損失,尤以1999年台灣中部所發生之921大地震,更是造成無可彌補之鉅大傷害。因此,地震引起的土壤液化問題一向受到學術界與工程界的重視,也發展出許多液化潛能評估的方法。綜觀目前世界著名的土壤液化評估方法,大多是累積許多地震,液化與非液化案例加上現地試驗參數,例如SPT-N與CTP-qc值所發展而成。這些簡易法,經由簡單的公式運算,即可獲得符合工程需要之評估結果,而廣為工程界普遍使用。然而檢視這些方法,多有明顯之地域特性,且無法適用在不同土壤性質之範圍,因此本研究試圖以類神經模糊系統解決此一困境。
本研究基於分工與一一克服方法論,以模糊系統為基礎,結合群聚分析概念與類神經網路架構,提出一整合式模糊群聚類神經網路架構。利用全世界已發表論文所列之案例,加上921地震的現場觀測數據,藉以評估土壤液化潛能並驗證系統之準確性及推估能力。本研究所提出之系統架構,對於現地土壤液化誤判率遠較傳統經驗法則為低,評估結果亦較倒傳遞類神經網路為優,應用於預測土壤液化現象上有很好的結果。其系統化的訓練方法不僅簡單,同時也提供了另一個輔助現有分析方法的方向,而其適應性以及推廣性更令人對其未來發展抱持樂觀的態度。
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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