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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 周家蓓 | |
dc.contributor.author | Cyuan-hong Gao | en |
dc.contributor.author | 高銓鴻 | zh_TW |
dc.date.accessioned | 2021-06-13T00:41:46Z | - |
dc.date.available | 2007-07-26 | |
dc.date.copyright | 2007-07-26 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-23 | |
dc.identifier.citation | 1. 財團法人中華工程顧問工程司,「衝擊式落重撓度資料反算及應用之研究」,研發計畫成果報告,財團法人中華顧問工程司,民國九十五年
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Determination of Stress-Dependent Material Properties with the FWD, fir Use in the Structural Analysis of Pavement Using Finite Element Analysis Techniques, proceeding of the 8th Conference on Asphalt Pavements for Southern Africa (CAPA’04), 2004 23. Gerardo W. Flintsch, Imad L. Al-Qadi, Youngjin Park, Thomas L. Bramdon and Alexander Appea, “Relationship Between Backcalculated and Laboratory-Measured Resilient Moduli of Unbound Materials”, Transportation Research Record 1849. Washington, DC: National Research Council, Transportation Research Board, 2003 24. “AASHTO Guide for Design of Pavement Structures 1993”, American Association of State Highway and Transportation Officals, Washtington DC, 1993 25. Von Quintus H. L., and Simpson A. L., “Back-Calculation fo Layer Parameters for LTPP Test Sections, Volume II: Layered Elastic Analysis for Flexible and Rigid Pavements”, Publication No. FHWA-RD-01-113, Federal Highways Administration, McLean, VA, 2002 26. Khazanovich Lev, Shiraz D. Tayabji, and Michael I. Darter , “Backcalculation of Layer Parameters for LTPP Test Sections, Volume I: Slab on Elastic Solid and Slab on Dense-Liquid Foundation Analysis of Rigid Pavements”, Publication No. FHWA-RD-00-086, Federal Highways Administration, McLean, VA, 1998 27. Khazanovich Lev, “Dynamic Analysis of FWD Test Results for Rigid Pavements”, Nondestructive Testing of Pavements and Backcalculation of Moduli: Third Volume, ASTM STP 1375, S. D. Tayabji and E.O. Lukanen, Eds., American Society for Testing and Materials, West Conshohocken, Pa, 2000 28. Samir N. Shoukry, Gergis W. William, and David R. Martinelli, “Assessment of the Performance of Rigid Pavement Backcalculation Through Finite Element modeling”, Nondestructive Evaluation of Bridges and Highways III, Steven B. Chase, Editor, 1999 29. Waheed Uddin, Sergio Garza, “In Situ Material Characterization of Pavement-Subgrade Systems Using FWD Data and Validation by 3D-FE Simulations”, Federal Aviation Administration Airport Technology Transfer Conference, 2002 30. Waheed Uddin, Sergio Garza, “3D-FE Modeling and Simulation of Airfield Pavement Subjected to FWD Impact Load Pulse and Wheel Loads”, Proceedings,2003 Airfield Pavement Specialty Conference, American Society of Civil Engineers, Las Vegas, Nevada, September 21-24, 2003, pp. 304-315 31. Rafael Foinquinos, Jose M. Roesset, and Kenneth H. Stoke II, “Response of Pavement Systems to Dynamic Loads Imposed by Nondestructive Tests”, Transportation Research Record 1504, TRB National Research Council, Washington D.C., 1995 32. Richard Y. Kim and Sungho Mun, “Determination of Subgrade Strength under Intact Portland Cement Concrete Slabs for Rubblization Projects”, Publication No. FHWA/NC/2002-010, Federal Highways Administration, McLean, VA, 2002 33. Yung-Chien Lee, Y. Richard Kim, and S. Ranji Ranjithan, “Dynamic Analysis-Based Approach To Determine Flexible Pavement Layer Moduli Using Deflection Basin Parameters”, Transportation Research Record 1639, TRB National Research Council, Washington D.C., 1998 34. Stubstad R. N., Y. J. Jiang, M. L. Clevenson, and E. O. Lukanen, “Review of the Long-Term Pavement Performance Backcalculation Results-Final Report”, Publication No. FHWA-HRT-05-150, Federal Highways Administration, McLean, VA, 2006 35. Kasthurirrangan Gopalakrishnan and Marshall R. Thompson, “Assessing Damage to Airport Pavement Structure due to Complex Gear Loads”, Journal of Transportation Engineering, ASCE, 2006 36. Yusuf Mehta and Reynaldo Roque, “Evaluation of FWD Data for Determination of Layer Moduli of Pavements”, Journal of Material in Civil Engineering, 2003 37. Waheed Uddin, Dingming Zhang, and Francisco Fernandez, “Finite Element Simulation of Pavement Discontinuities and Dynamic Load Response”, Transportation Research Record 1448, TRB National Research Council, Washington D.C., 1994 38. Joe P. Mahoney, Brian C. Winters, Newton C. Jackson and Linda M. Pierce, “Some Observation About Backcalculation and Use of a Stiff Layer Condition”, Transportation Research Record 1384, TRB National Research Council, Washington D.C., 1993 39. Der-Wen Chang, Yumin Vincent Kang, Jose Roesset and Kenneth H. Stoke II, “Effect of Depth to Bedrock on Deflection Basins Obtained with Dynaflect and Falling Weight Deflectometer Tests”, 40. Jose M. Roesset, Kenneth H. Stoke II and Chia-Ray Seng, “Determination of Depth to Bedrock from Falling Weight Deflectometer Test Data”, Transportation Research Record 1504, TRB National Research Council, Washington D.C., 1995 41. Richard Y. Kim, Yung-Chien Lee and S. Ranji Ranjithan, “Flexible Pavement Condition Evaluation Using Deflection Basin Parameters and Dynamic Finite Element Analysis Implemented by Artificial Neural Networks”, Nondestructive Testing of Pavements and Backcalculation of Moduli: Third Volume, ASTM STP 1375, S. D. Tayabji and E. O. Lukanen, Eds., 2000 42. Bredenhann S. J. and M.F.C van de ven, “Application of Artificial Neural Networks in the Back-calculation of Flexible Pavement Layer Moduli from Deflection Measurements”, proceeding of the 8th Conference on Asphalt Pavements for Southern Africa(CAPA ’04), 2004 43. Burak A. Goktepe and Hilmi A. Lav, “Role of Learning Algorithm in Neural Network-Based Backcalculation of Felxible Pavements”, Journal of Computing in Civil Engineering, ASCE, 2006 44. Mehmet Saltan and Serdal Terzi, “Backcalculation of Pavement Layer Parameters Using Artificial Neural Network”, Indian Journal of Engineering and Materials Sciences, 2004 45. Trefor P. Williams and Nenad Gucunski, “Neural Networks for Backcalculation of Moduli from SASW Test”, Journal of Computing in Civil Engineering, Vol. 9, No. 1, 1995 46. Su Wang and imad Abdallah, “Neural Network Approach to Non-Destructive Evaluation of Pavements”, Circuits and Systems, 1999. 42nd Midwest Symposium on, 1999 47. Pekcan O., E. Tutumluer and M. R. Thompson, “Nondestructive Flexible Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models”, Geotechnical Engineering in the Information Technology Age, Proceedings of GeoCongress 2006 48. Kasthurirangan Gopalakrishnan, Marshall R. Thompson and Anshu Manik, “Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks”, International Journal of Computational Intelligence Volume 3, 2006 49. Kasthurirangan Gopalakrishnan, Anshu Manik and Siddhartha K. Khaitan, “Runway Stiffness Evaluation Using an Artificial Neural Systems Approach”, International Journal of Intelligent Technology Volume 1, 2006 50. Saltan, M., Tigdemir, M., Karasahin, M., “Artificial Neural Network Application for Flexible Pavement Thickness Modeling”, Turkish Journal of Eng and Env Sci, 2002 51. Nune Rakesh and A. K. Jain, M. Amaranatha Reddy and K. Sudhakar Reddy, “Artificial Neural Networks-Genetic Algorithm Based Model for Backcalculation of Pavement Layer Moduli”, International Journal of Pavement Engineering, Volume 7, 2006 52. 鄭榮和,有限元素法導論講義,民國九十五年 53. J.N. Reddy,An Introduction to the Finite Element Method,McGraw-Hill, Inc., 1993 54. 有限元素軟體ABAQUS http://www.abaqus.com/products/products_overview.html 55. 張斐章,類神經網路講義,台灣大學生物環境系統工程學研究所,民國九十四年九月 56. 葉怡成,應用類神經網路,儒林圖書有限公司,民國九十年 57. 蔡瑞煌,類神經網路概論,三民書局,民國八十四年 58. Lynne H. Irwin,Instructional Guide for Back-Calculation and the Use of MODCOMP3,Cornell University Local Roads Program,CLRP Publication No. 94-10,March 1994 59. 「中正國際機場道面整建策略及評估計畫,道面調查/檢測成果報告」,交通部民用航空局,民國九十二年 60. 「國立台灣大學水源校區地質鑽探工程地質鑽探報告書」,巨東地質技術工程有限公司,民國九十四年 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29128 | - |
dc.description.abstract | 落重撓度儀於鋪面強度檢測之應用已逐漸成熟且為目前之趨勢,各種軟體亦不斷被發展以更加精確地反算鋪面之勁度值。在反算軟體中,參數之設定,例如初始勁度值、各層厚度與堅硬層深度等,將會影響反算結果之精確性。本研究利用有限元素軟體模擬剛性鋪面受動力荷重之反應情形,並探討在不同鋪面組成下各參數間之相互關係。利用撓度盤以及最大撓度回彈時間兩個參數,觀察其與堅硬層深度與勁度、面層厚度與勁度以及路基土壤之勁度值等因素間是否有關係性存在。結果顯示,最大撓度回彈時間、堅硬層深度以及路基土壤勁度三者間之關係最為密切,故利用此三個參數建立堅硬層深度之計算公式;而路基土壤勁度值則利用BDI-F2(Richard,1998)建立公式計算得知。以上述兩個公式進行迭代與利用靜力反算程式(MODCOMP)設定不同堅硬層深度,以及以ABAQUS假設不同層之勁度值之試誤法三者所得之路基土壤勁度值進行比較。由結果得知以迭代以及利用ABAQUS試誤法兩者之結果較為接近,並且MODCOMP未考慮堅硬層所得之反算結果與AQBAQUS所得者相差最大。因此,建議往後使用反算軟體時,不可將堅硬層參數忽略。另一方面,本研究亦將有限元素軟體之模擬結果用以建構動力類神經網路反算模式。將此一模式與其他常用之反算軟體以現地機場撓度進行反算得知,本研究所建構之類神經網路反算模式,其反算值較靠近實驗室值且數值穩定,尤以路基土壤勁度之反算結果最為明顯。因此,本研究所構建之類神經反算模式不但可確實反算鋪面勁度,其運算速度亦較一般傳統反算軟體快。 | zh_TW |
dc.description.abstract | The use of falling weight deflectometer (FWD) in pavement strength inspection is getting matured and popular. Also, different kinds of software have been developed in order to backcalculate more precisely the pavement stiffness. However, the setting of parameters, such as initial stiffness, layer thicknesses, and depth of stiff layer, would influence the accuracy of backcaluation results. Thus, the finite element software, ABAQUS, was used in this research for simulating pavement responses to dynamic loadings and for evaluating the relationships between different parameters under various types of pavement compositions.
Deflection bowls and rebounding time were analyzed to observe the relations with the depth and stiffness of stiff and surface layers as well as the stiffness of subgrade. It was found that rebounding time had close relationships with stiff layer depth and subgrade stiffness, and they were used to develop the equation for the calculation of stiff layer depth. In addition, subgrade stiffness could be obtained by the equation composed of BDI and F2. By comparing the backcalculated subgrade stiffness obtained with three different methods, i.e. by the iteration of two equations mentioned above, by setting different stiff layer depths in static backcalculation software, MODCOMP, and by using different layer stiffness in ABAQUS, it was found that the results of the first and the last methods were closest. In those of the second method, the result without considering stiff layer thickness was quite different. Therefore, it was shown that the depth of stiff layer could not be neglected while using softwares to backcalculate. Besides, the simulation results of the finite element software were also used as the training data of artificial neural network (ANN) to build dynamic backcalculation models. The backcalculation results of the dynamic ANN backcalculation models were compared with those of other common backcalculation softwares, and it was found that the former ones were more stable and closer to the laboratory values, especially for those of subgrade stiffness. Therefore, the dynamic ANN backcalculation model built in this research can backcalculate pavement stiffness more precisely and faster. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:41:46Z (GMT). No. of bitstreams: 1 ntu-96-R94521513-1.pdf: 1985456 bytes, checksum: c58a30592bbc7fd351ccb23365a50a6a (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 致謝 .............. .......................... 一
中文摘要 .............. .......................... 二 英文摘要 .............. .......................... 三 圖目錄 .............. .......................... 八 表目錄 .............. .......................... 一一 第一章 緒論.............. .......................... 1 1.1 研究背景......................................... 1 1.2 研究目的......................................... 2 1.3 研究內容與方法................................... 2 1.4 研究流程......................................... 3 第二章 文獻回顧..................................... 7 2.1 落重撓度儀簡介................................... 7 2.1.1 非破壞性檢測方式.............................. 7 2.1.2 衝擊式落重撓度儀.............................. 9 2.2 反算軟體概述..................................... 11 2.2.1 反算軟體之分類................................ 12 2.2.2 反算方式之簡述................................ 12 2.3 影響反算結果之因素............................... 14 2.3.1 材料非線性.................................... 14 2.3.2 有限元素模擬方式.............................. 17 2.3.3 堅硬層存在之影響性............................ 21 2.4 類神經網路於鋪面反算之應用....................... 24 2.5 小結............................................. 27 第三章 研究方法概述................................. 29 3.1 有限元素法簡介................................... 29 3.2 有限元素模型建構說明............................. 31 3.3 類神經網路介紹................................... 34 3.4 MODCOMP反算軟體介紹.............................. 38 第四章 有限元素模擬資料整理與分析................... 41 4.1 路基土壤模數之預測............................... 41 4.1.1 比較淺層與深層之堅硬層對於BDI-F2之影響........ 42 4.1.2 比較淺層與深層之堅硬層對於D0-F3之影響......... 45 4.1.3 多元回歸結果探討.............................. 46 4.2 靜力與動力載重於撓度值之影響..................... 47 4.3 撓度與回彈時間之分析............................. 53 4.3.1 撓度分析...................................... 54 4.3.1.1 不同堅硬層深度對於撓度之影響............... 54 4.3.1.2 不同堅硬層勁度對於撓度之影響............... 56 4.3.2 最大撓度回彈時間.............................. 58 4.3.2.1 不同堅硬層深度與勁度對於回彈時間之影響..... 58 4.3.2.2 不同路基勁度對於回彈時間之影響............. 62 4.3.2.3 不同面層厚度與勁度對於回彈時間之影響....... 64 4.4 實例驗證......................................... 67 4.4.1 公式法對於路基土壤計算之驗證.................. 67 4.4.2 堅硬層深度之驗證與路基土壤勁度................ 72 4.5 小結............................................. 75 第五章 類神經反算模式建構........................... 77 5.1 多層函數連結網路介紹............................. 77 5.2 模式構建比較..................................... 78 5.3 網路反算模式建構................................. 83 5.3.1 多層模式之反算網路建構........................ 83 5.3.2 單一模式之反算網路建構........................ 86 5.3.3 反算模式建構最佳化............................ 88 5.4 實例驗證......................................... 94 5.5 小結............................................. 97 第六章 結論與建議................................... 99 6.1 結論............................................. 99 6.2 建議 .............................................100 參考文獻..............................................103 | |
dc.language.iso | zh-TW | |
dc.title | 應用有限元素法與類神經網路建立動力撓度反算模式 | zh_TW |
dc.title | Integrate the FEM and ANN into the Dynamic Model of Rigid Pavement Backcalculation | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張德文,郭振銘 | |
dc.subject.keyword | 落重撓度儀(FWD),鋪面反算,堅硬層,有限元素法,類神經網路, | zh_TW |
dc.subject.keyword | falling weight deflectometer (FWD),artificial neural network (ANN),stiff layer,finite element method, | en |
dc.relation.page | 109 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-25 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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