臺灣博碩士論文加值系統

自0206美濃地震在台南造成嚴重土壤液化災害，提升國內土壤液化風險圖資的精確度日趨重要，更精準的圖資就需要比過去密集的地質調查，但傳統的液化評估法需鑽探因此常會受到時間、空間與經費的限制，因此本研究使用地表微震動儀透過Nakamura(1989)單站頻譜法經驗式獲得地表的場址特徵，試圖由非破壞性、方便快速的地表微震動量測在台南都會區建立其與地質、土壤液化間的關係。
研究分為兩部分，第一部份是使用等值線法，從鑽探報告、過去在台南都會區的地質研究以及0206美濃地震實際液化地點，以微震動量測所得之卓越頻率的空間分布探討微震動與地質、土壤液化間的關係；第二部分則是透過先前在台南都會區之鑽孔報告，經HBF法獲得各孔位之液化風險潛勢，以及微震動量測所得之特徵，由類神經網路建立兩者間的關係。
類神經網路訓練結果發現，地表微震動在預測液化、非液化整體有74%的成功率，在初步的分類上是有幫助的，不過若要進一步預測低、中、高風險區就稍嫌不足。等值線法研究成果得出，地表微震動資料確實能夠反映台南都會區的地質狀況，且若聚焦於0206美濃地震各個液化地點，的確這些地方在卓越頻率分布圖上普遍偏低約在1.35Hz以下，是相對軟弱的區域。綜合以上結果得出，地表微震動的確是能夠反映一地方的地質特徵，並且利用這些特徵對於預測可能發生土壤液化的地點有很大的幫助。

In this study we used microtremor which is convenient and non-destructive to acquire the site characteristics and analyzed it by Nakamura method(1989) which also called HVSR. Finally, establishing the empirical relation between microtremor and soil liquefaction in Tainan urban area.
There are two parts in this study. In first part, we will use GIS software to plot the distribution of predominant frequencies and compare it with borehole data, past geologic research and soil liquefaction regions of 0206 Meinong earthquake in Tainan urban area. In the second part, we will use ANN tool to help us connect site characteristics which got from HVSR with liquefied potential which got from SPT borehole data by HBF method (2005). We used the site characteristics as inputs, on the other hand, liquefied potential as targets. With different model assumptions, finally picked up the model that has highest success rate.
From the result of first part, microtremor exactly can reflect the geology of Tainan urban area. And focus on these liquefied regions in 0206 earthquake, most of these regions has predominant frequency that below 1.35Hz, which are relative weak places. From the result of second part, the best ANN model has success rate round 74% in predicting one place is liquefied or non-liquefied, it’s helpful for preliminary classification. However, if we want to predict one place is low, medium or high liquefied potential, the success rate will drop.
Finally we can conclude that microtremor will really show the characteristics of one place. And it’s helpful for predicting soil liquefaction in one place.

摘要 I
Abstract II
致謝 X
表目錄 XV
圖目錄 XVII
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 研究流程 3
1.4 論文架構 4
第二章 文獻回顧 5
2.1微震動(Microtremor) 5
2.1.1微震動定義 5
2.1.2 微震動分析方法 6
2.1.3 單站頻譜比法(Horizontal to Vertical Spectral Ratios) 9
2.1.4 Kg值 (Vulnerability Index) 13
2.1.5 單站頻譜法(HVSR)在大地工程的應用 16
2.2 土壤液化 21
2.2.1 定義 21
2.2.2 土壤液化現象與影響因素 23
2.2.3 標準貫入試驗(SPT)液化評估法 24
2.2.4 雙曲線函數評估法(Hyperbolic Function, HBF法，2005) 25
2.2.5 Iwasaki液化潛能指數 27
2.2.6 水平加速度係數 27
2.3 類神經網路 32
2.3.1 定義 32
2.3.2 類神經網路的問題類型與架構 33
2.3.3 類神經網路的應用及限制 36
2.3.4 類神經網路於地震工程上的應用 38
2.3.5 類神經網路於大地工程上的應用 38
第三章 場址介紹 41
3.1 研究範圍 41
3.2地質概況 41
3.3 地工特性分區 46
第四章 研究方法 49
4.1 微震動測量 49
4.1.1 微震動儀器介紹 50
4.1.2 量測地點 51
4.1.3 現地量測 54
4.1.4 微震動數據處理 56
4.1.5 微震動數據判讀 58
4.2 SPT簡易液化評估法計算 61
4.2.1 標準貫入試驗之參數 61
4.2.2 地下水位 61
4.3.3 最大水平加速度 61
4.3 類神經網路訓練 64
4.3.1 倒傳遞神經網路 64
4.3.2 類神經網路輸入層參數 68
4.3.3 類神經網路學習目標 71
4.3.4 資料預處理 72
4.4 等值線法 72
第五章 等值線法研究結果與討論 73
5.1 卓越頻率分布 73
5.2 地工分區與卓越頻率探討 76
5.3 卓越頻譜圖與實際液化位置比較 88
5.4 地震與微震動資料H/V頻譜比較 96
5.5 地下水對H/V頻譜的影響 98
5.6 放大係數分布圖以及Kg值分布圖 100
5.7 小結 102
第六章 類神經網路研究結果與討論 103
6.1 參數設定 103
6.1.1 訓練資料比例 103
6.1.2 訓練資料之輸入變數 103
6.1.3 剔除無效資料 104
6.1.4 輸入參數正規化 106
6.1.5 相關性測試結果 106
6.1.6 訓練目標LPI值 108
6.1.7 隱藏層神經數測試 109
6.1.8 模型間的差異以及設計原因 110
6.2 高、中、低液化風險類神經網路訓練結果 111
6.2.1 訓練模型一 111
6.2.2 訓練模型二 117
6.3 液化、非液化類神經網路訓練結果 121
6.3.1 訓練模型三 121
6.3.2 訓練模型四 125
6.3.3 訓練模型五 131
6.3.4 訓練模型六 135
6.4 美濃地震實際液化地區及非液化地區資料測試 140
6.5 小結 141
第七章 結論與建議 145
7.1 結論 145
7.2 建議 146
參考文獻 148
附錄A 155
附錄B 182
附錄C 186

[1]Chang, Mark. (2015)，類神經網路 – Backward Propagation 詳細推導過程。「http://cpmarkchang.logdown.com/」
[2]三木拓人, 清野純史, 奥村与志弘, 土肥裕史, 呉建宏, ＆ 李徳河. (2017). 2016 年台湾高雄美濃地震と台南市の地盤震動特性. 地域安全学会論文集, 31, 319-327.
[3]內政部(2017)，「建築物耐震設計規範與解說」，詹氏書局，台北，台灣
[4]台灣世曦工程顧問股份有限公司(2018)，「台南市中級土壤液化潛勢地圖第一期建置暨地質改善委託技術服務」，台南市政府工務局，台南，台灣
[5]李德河、許琦(1988)。台南都會區地質概況。地工技術雜誌，第22期，第40~55頁。
[6]李德河、鍾廣吉、古志生、黃健政、劉憲德、林文哲、曾俊傑、廖志中、胡賢能、莊長賢、林炳森、蔡百祥、張振成(2005)，都會區地下地質與工程環境調查研究第二期－新竹、苗栗與台南都會區地下地質與與工程環境調查研究(台南都會區)，經濟部中央地質調查所委託研究計畫報告，新北，台灣
[7]沈哲平、林主潔、黃謝恭、林沛暘與王冠又（2010）。類神經網路應用於強震即時警報系統之建物受震反應分析。第十屆中華民國結構工程研討會，台灣。
[8]周小文、付暉(2005)，「Kriging法在大區域場地砂土液化範圍判别中的應用研究」，長江科學院院報，第22卷，第4期，第48-51頁
[9]林朝棨(1971)，「台南地方的第四紀地質」，經濟部聯合礦業研究所，新竹，台灣。
[10]洪勝利，利用類神經網路建立微震量測法評估土壤液化潛勢之研究，國立成功大學土木工程研究所碩士論文，2018。
[11]夏啟明(1992)，「細料塑性程度對台北盆地粉泥質砂液化潛能之影響」，國立台灣大學土木工程研究所，碩士論文
[12]國家災害防救科技中心(2016)，0206地震災情彙整與實地調查報告，「https://www.ncdr.nat.gov.tw/」。
[13]國家地震工程研究中心(2013)，「https://www.ncree.org/DesignSpectra.aspx」
[14]國家地震工程研究中心(2017)，「https://www.ncree.org/HBF.aspx」
[15]陳嘉裕(1999)，「細粒料含量對砂土液化潛能之影響研究」，國立成功大學土木工程研究所，碩士論文
[16]黃于庭，台南地區土壤液化評估方法適用性之研究。國立成功大學土木工程學系碩士論文，台南市，2018。
[17]黃有志，蘭陽平原場址效應及淺層S 波速度構造，國立中央大學地球物理所碩士論文，2003。
[18]黃俊鴻、楊志文、陳正興，2005，「本土化液化評估方法之建議－雙曲線液化強度曲線」，地工技術，第103期，第53-64頁。
[19]黃俊鴻、 陳正興、莊長賢，2012，「本土 HBF土壤液化評估法之不確定性」， 地工技術雜誌，第 133期，第 77-86頁
[20]黃雋彥，利用微地動量測探討台灣地區之場址效應，國立中央大學地球物理所碩士論文，2009。
[21]葉怡成(2001)，類神經網路，台北 : 儒林圖書有限公司。
[22]廖元憶(2005)，「台灣西南沿海高細粒料含量砂土的探討」，國立成功大學土木工程研究所，碩士論文
[23]鄭文隆、吳偉康(1985)，「土壤液化之災害型態與現地研判」，地工技術雜誌，第90期，第90-103頁
[24]盧志杰、許尚逸、黃郁惟、黃俊鴻(2016)，「美濃地震液化災損調查及簡易評估」，中華民國第十三屆結構工程研討會暨第三屆地震工程研討會，桃園。
[25]Almendros, J., Luzón, F., ＆ Posadas, A. (2004). Microtremor analyses at Teide Volcano (Canary Islands, Spain): assessment of natural frequencies of vibration using time-dependent horizontal-to-vertical spectral ratios. pure and applied geophysics, 161(7), 1579-1596.
[26]Beroya, et al.(2009)/ Beroya, M. A. A., Aydin, A., Tiglao, R., ＆ Lasala, M. (2009). Use of microtremor in liquefaction hazard mapping. Engineering Geology, 107(3-4), 140-153.
[27]Bolton Seed, H., Tokimatsu, K., Harder, L. F., ＆ Chung, R. M. (1985). Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of Geotechnical Engineering, 111(12), 1425-1445.
[28]Borcherdt, R. D. (1970). Effects of local geology on ground motion near San Francisco Bay. Bulletin of the Seismological Society of America, 60(1), 29-61.
[29]Chang, S. K., Lee, D. H., Wu, J. H., ＆ Juang, C. H. (2011). Rainfall-based criteria for assessing slump rate of mountainous highway slopes: a case study of slopes along Highway 18 in Alishan, Taiwan. Engineering geology, 118(3-4), 63-74.
[30]Chien, L. K., Oh, Y. N., ＆ Chang, C. H. (2002). Effects of fines content on liquefaction strength and dynamic settlement of reclaimed soil. Canadian Geotechnical Journal, 39(1), 254-265.
[31]Dongare, A. D., Kharde, R. R., ＆ Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.
[32]Goh, A. T. C. (1994). Seismic liquefaction potential assessed by neural network, Journal of Geotechnical ＆ Geoenvironmental Engineering, ASCE, 120(9), page 1467-1480.
[33]Hazen, A. (1920). Hydraulic-fill dams. Transactions of the American Society of Civil Engineers, 83(1), 1713-1745.
[34]Holzer, T. L., Bennett, M. J., Noce, T. E., Padovani, A. C., ＆ Tinsley III, J. C. (2006). Liquefaction hazard mapping with LPI in the greater Oakland, California, area. Earthquake Spectra, 22(3), 693-708.
[35]Huang, H. C. (2002). Characteristics of earthquake ground motions and the H/V of microtremors in the southwestern part of Taiwan. Earthquake engineering ＆ structural dynamics, 31(10), 1815-1829.
[36]Huang, H. C., ＆ Teng, T. L. (1999). An evaluation on H/V ratio vs. spectral ratio for site-response estimation using the 1994 Northridge earthquake sequences. pure and applied geophysics, 156(4), 631-649.
[37]Huang, H-C., and Tseng, Y-S., 2002, Characteristics of Soil Liquefaction using H/V of Microtremors in Yuan-Lin area, Taiwan, Terrestrial, Atmospheric and Oceanic (TAO), Vol. 13, No.3, September 2002, page 325 - 338
[38]Idriss, I.M., and R.W. Boulanger, 2008, Soil Liquefaction during Earthquakes, Earthquake Engineering Research Institute MNO-12, Oakland, California.
[39]Ishihara, K. (1985). Stability of natural deposits during earthquakes. Proc. of 11th ICSMFE, 1985, 1, 321-376.
[40]Iwasaki, T., Arakawa, T., ＆ Tokida, K. I. (1984). Simplified procedures for assessing soil liquefaction during earthquakes. International Journal of Soil Dynamics and Earthquake Engineering, 3(1), 49-58.
[41]Juang, C. H., ＆ Chen, C. J. (1999). Cpt‐based liquefaction evaluation using artificial neural networks. Computer‐Aided Civil and Infrastructure Engineering, 14(3), 221-229.
[42]Kanai, K. (1954). TANAKA, T. andOSADA, K MeasurementoftheMicrotremor. 1. Bull. Earthq. Res. Inst, 32, 199-209.
[43]Kanai, K., Tanaka, T., ＆ Osada, K. (1962). Measurement of the microtremor VII. Bull. Earth. Res. Inst, 35, 191-200.
[44]Khaze, S. R., Masdari, M., ＆ Hojjatkhah, S. (2013). Application of Artificial Neural Networks in estimating participation in elections. arXiv preprint arXiv:1309.2183.
[45]Kiyono, J., Ono, Y., Sato, A., Noguchi, T., ＆ Putra, R. R. (2011). Estimation of subsurface structure based on microtremor observations at Padang, Indonesia. ASEAN Engineering Journal, Part C, 1(3), 66-81.
[46]Kyaw, Z. L., Pramumijoyo, S., Husein, S., Fathani, T. F., ＆ Kiyono, J. (2014). Investigation to the local site effects during earthquake induced ground deformation using microtremor observation in Yogyakarta, Central Java-Indonesia. In Landslide Science for a Safer Geoenvironment (pp. 241-249). Springer, Cham.
[47]Lee, K.L. and Seed, H.B., 1967, Cyclic Stress Conditions causing Liquefaction of Sand: Am. Soc. Civil Engineers Proc. Jour. Soil Mechanics and Found. Div. Vol.93, no SM1, page 47 – 70
[48]Lermo, J., ＆ Chávez-García, F. J. (1994). Are microtremors useful in site response evaluation?. Bulletin of the seismological society of America, 84(5), 1350-1364.
[49]Lermo, J., ＆ Chávez-García, F. J. (1993). Site effect evaluation using spectral ratios with only one station. Bulletin of the seismological society of America, 83(5), 1574-1594.
[50]MINISTRY OF BUSINESS, INNOVATION ＆ EMPLOYMENT [New Zealand] (2012), “http://www.dbh.govt.nz/guidance-on-repairs-after-earthquake.
[51]Mucciarelli, M., Gallipoli, M. R., Di Giacomo, D., Di Nota, F., ＆ Nino, E. (2005). The influence of wind on measurements of seismic noise. Geophysical Journal International, 161(2), 303-308.
[52]Nakamura,Y., 1989, A Method for Dynamic Characteristics Estimation of Surface Layers using Microtremor on the Surface, Quarterly Report of RTRI Vol. 30 No.1, page 18–27
[53]Nakamura, Y. (1996). Real-time information systems for seismic hazards mitigation UrEDAS, HERAS and PIC. QUARTERLY REPORT-RTRI, 37(3), 112-127.
[54]Nakamura, Y. (2008, October). ON THE H/V SPRECTRUM. The 14th World Conference on Earthquake Engineering, Beijing, China.
[55]Rumelhart, D. E., Hinton, G. E., ＆ Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive modeling, 5(3), 1.
[56]Seed, H. B. (1976). Evaluation of soil liquefaction effects on level ground during earthquakes. Liquefaction problems in geotechnical engineering, 2752, 1-104.
[57]Seed, H.B. and Idriss I.M. (1971). Simplified Procedure for Evaluating Soil Liquefaction Potential, Am. Soc. Civil Engineers Proc. Jour. Soil Mechanics and Found. Div. Vol. 92, No. SM6, page 105 - 134
[58]Shahin, M. A., Jaksa, M. B., ＆ Maier, H. R. (2001). Artificial neural network applications in geotechnical engineering. Australian geomechanics, 36(1), 49-62.
[59]Sharma, V., S. Rai, A. Dev, 2012, A comprehensive Study of Artificial Neural Networks, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Vol. 2, Issue 10, October 2012, page 278 – 284
[60]Terzaghi, Karl, and Peck, R.B. (1948). Soil Mechanics in Engineering Practice, New York: John Wiley and Sons, 566 p.
[61]Tokeshi, J.K., Sugimura, Y., and Sasaki, T. (1996). Assessment of Natural Frequency from Microtremor Measurement using Phase Spectrum, 11th World Conference on Earthquake Engineering, paper no.309
[62]Tokimatsu, K., ＆ Yoshimi, Y. (1983). Empirical correlation of soil liquefaction based on SPT N-value and fines content. Soils and Foundations, 23(4), 56-74.
[63]Youd, T. L., ＆ Idriss, I. M. (2001). Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. Journal of geotechnical and geoenvironmental engineering, 127(4), 297-313.
[64]Zhang, H., Jeng, D. S., Cha, D., ＆ Blumenstein, M. (2007). Parametric study on the prediction of wave-induced liquefaction using an artificial neural network model. Journal of Coastal Research, 374-378.