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研究生:嚴世裕
研究生(外文):Shi-Yu Yan
論文名稱:利用基因演算法評估地層下陷區沉積層壓密參數之初步研究
論文名稱(外文):Preliminary Study on Estimating Consolidation Parameters of Alluvial Deposits in Ground Subsidence Area Based on Genetic Algorithms
指導教授:張睦雄
指導教授(外文):Muhsiung Chang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:營建工程系碩士班
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:94
中文關鍵詞:壓密 係數(Cv)濁水溪沖積扇地層下陷地下水位壓縮指數(Cc)基因演算法
外文關鍵詞:genetic algorithmground subsidenceChouswei River Alluvial Fangroundwater levelconsolidation coefficient (Cv)compression index (Cc)
相關次數:
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台灣位處歐亞大陸板塊和菲律賓海板塊交界帶、形成地勢起伏變化極大之地
形,造成河川短促、在短時間內即奔流入海。另一方面,台灣降雨時間分布不均,
既有之水庫容量不足無法蓄存足夠水資源,以因應民生及產業快速成長需求。因
此,台灣西南部沿海平原隨處可見抽取地下水以填補水資源不足之情況,同時也
造成全台各平原沖積扇與盆地地層下陷現象。隨著人口與經濟快速成長,公路及
大眾交通運輸大量興建;由於可利用之土地資源限制,導致新闢建設多以路堤及
高架方式構築。上述大量抽取地下水補充水資源不足,及運輸設施興建引致地表
負載增加,加速土層壓密現象之產生。
數十年來,台灣西南部沿海地層下陷問題持續發生。其結果不僅危及行經此
區域之高鐵行車安全,同時也影響到濁水溪沖積扇各農田排水系統以及沿海地區
海堤工程之功能。由此可知,地層下陷量之評估與預測對於工程結構之使用與維
護的重要性。然而現階段工程設計對地層沉陷參數常使用現地鑽探取樣後進行室
內試驗求得,再使用此參數代入經驗公式或沉陷理論預測沉陷量。但對於深層土
壤沉陷參數現地鑽探取樣取得,除費時且耗費金錢外,可資參考的數據甚為缺乏。
因此,近年來逐漸朝現地觀測資料之迴歸、分析等手段,以改善經驗公式、推估
土壤壓縮參數,進而預測地盤長期沉陷之趨勢與發展。
本研究擬針對位於濁水溪沖積扇之土庫國中沉陷監測站近年既有的地層下陷
現地監測數據及地下水位監測資料,配合基因演算法與Terzaghi 單向度壓密理論公
式,進行壓密沉陷分析模式之建立。並藉由壓密沉陷分析模式估計出一組最佳土
壤壓縮性代表參數;接著利用此組參數作為推測地盤長期沉陷之趨勢與發展,並
以此分析模式提供地層下陷評估之參考。
根據監測資料與水位資料顯示,每年1 月至4 月進入旱季時期,地下水位將
大幅度下降,同時引致地層壓密沉陷量明顯增加。然而當進入濕季時期地下水位
則上升,此時沉陷監測亦發生些許回脹現象。如此反覆旱季與溼季循環造成地層
下陷監測結果有階梯形式情況產生。本研究以基因演算法建立之壓密沉陷分析模
式,針對土庫國中地層下陷監測分析結果發現,以前3 年監測數據推估全期(6 年)
沉陷量之效果最佳、以前2 年推估次之,以前1 年推估之誤差率最大。本研究亦
發現分析之沉陷趨勢亦有階梯形式情形產生。
Taiwan is located at the conjunction area between Eurasian Plate and Philippine
Sea Plate. As a result, the terrain of the island is greatly undulating and causing
generally short and fast-running rivers into the sea. In addition, uneven distribution
of rainfall has also caused shortages in reservoir capacity, which is unable to cope with
the fast-growing demands for general public and industries in Taiwan. Consequently,
water pumping is often seen on the coastal alluvial plains of the island due to shortfalls
in the agriculture supply, a situation that would in part contribute to the land
subsidence phenomenon observed in these areas. With rapid growth in population
and economy, the constructions in highways and other mass transit systems are
therefore booming. Due to availability of lands, embankments or elevated structures
are more preferred in the construction of transportation projects. The extraction of
groundwater and the surcharging of transportation projects as indicated above would
appear to accelerate the ground subsidence phenomenon observed in the alluvial
plains of this island.
The land subsidence problem in the southwestern coastal plains of Taiwan has
been occurring for decades. The problem deteriorates the functions of irrigation and
drainage systems on the alluvial plains and the seawall along the coast, as well as the
safety of the high-speed rail that passes through these regions. The evaluation and
prediction on the amounts and rates of land subsidence in the alluvial plains would
become critical. For most engineering design, the prediction of ground subsidence is
based upon theoretical or empirical approaches using material parameters obtained
from in-situ and laboratory testing. However, soil sampling in deep ground is usually
difficult and costly, and the consolidation properties of the materials are often scarce.
In accordance, the regression analysis of monitoring data has become a favor way for
obtaining the related parameters and then applying them in the theoretical or
empirical approaches for prediction the long-term trend of ground subsidence.
In this study, we examined the in-situ monitoring data of ground subsidence and
groundwater fluctuation at Tuku Junior High School (TJHS) Monitoring Station (to the
mid of Chouswei River Alluvial Fan in southwestern portion of the island), associated
with genetic algorithms and 1D Terzaghi Consolidation Theory, and established a
ground settlement analysis model. This analysis model was adopted to optimize a set
of representative consolidation parameters for soils in TJHS monitoring station, and
then used to predict the future development of ground subsidence in this area.
Previous monitoring data has shown the groundwater level drops substantially in this area during dry seasons of the year (JAN-APR). However, the groundwater level
rebounds in wet seasons, so as to the measurements in the ground subsidence. The
dry and wet cycles have resulted in a step-wise settlement curve of the ground.
Based on genetic algorithms, results of the settlement analysis model in this study
indicate ground subsidence prediction would be the most accurate with optimized
material parameters from the previous 3 years monitoring data, less accurate with
parameters from the previous 2 years data, and least accurate with only one year data.
The study also computes a step-wise settlement curve of the ground, which compares
well with the observations.
摘要 ..................................... I
ABSTRACT ........................... II
目錄 ................................... IV
表目錄 .............................. VII
圖目錄............................. VIII
一、緒論 .................................................. 1
1.1、 前言 .................................................. 1
1.2、 研究動機與目的 ............................... 1
1.3、 研究內容及論文架構........................ 1
1.4、 研究流程 ........................................... 3
二、文獻回顧 ..................... 4
2.1、 地層沉陷問題 ................................... 4
2.2、 地層下陷研究方法.............................. 6
2.3、 沉陷監測方法................................... 7
2.4、 Terzaghi 單向度壓密理論 .................. 9
2.4.1、 理論推導 ................................... 9
2.4.2、 Terzaghi 單向度壓密方程式解 12
2.4.3、 壓密理論之應用 ........................................... 14
2.5、 演化式計算法 .................................................... 14
2.5.1、 基因演算基本概念 .................................... 16
2.5.2、 基因演算流程 ................................................... 16
2.5.3、 基因演算名詞介紹 ...................................... 17
2.6、 濁水溪沖積扇地層下陷相關研究 ............................... 19
2.7、 土庫國中地陷監測井 ....................................... 22
2.7.1、 地理位置 ........................................................... 22
三、研究區域概述 ....................................... 23
3.1、 濁水溪沖積扇 ................................................... 23
3.1.1、 地理概況 ....................................................... 23
3.1.2、 地形地貌 ...................................................... 23
3.1.3、 區域地質調查 .......................................... 24
3.1.4、 水文地質分析 .......................................... 27
3.1.5、 地質分析 .................................................... 31
3.1.6、 沉陷監測深度 .......................................... 32
四、研究分析方法 ................................ 34
4.1、 研究規劃與分析方法 ...................................... 34
4.1.1、 分析流程 ................................................. 34
4.1.2、 研究分析方法驗證 ................................... 37
4.2、 資料收集與整理 ............................................ 38
4.2.1、 地下水監測 .............................................. 38
4.2.2、 磁環分層地陷井監測 ............................ 39
4.3、 資料分析及篩選 .............................................. 43
4.4、 地層物性參數 ................................................... 44
五、遺傳基因演算法分析與討論 ..................................... 45
5.1、 分析情況 .................................................... 45
5.1.1、 Case-1 .................................................... 45
5.1.2、 Case-2 ................................................. 48
5.1.3、 Case-3 ................................................. 50
5.1.4、 Case-4 ..................................................... 52
5.2、 綜合討論 ................................................... 54
六、結論與建議 .............................................. 56
6.1、 結論 .............................................................. 56
6.2、 建議 ........................................................... 56
參考文獻 ............................................................... 57
附錄1:土庫國中下陷監測井各環深度監測結果分析彙整 ............................. 59
附錄2:基因演算法沉陷計算過程總表........................................... 74
附錄3:基因演算法沉陷量計算外掛程式碼 ........................................ 91
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