系統識別號 U0026-0309201414461900
論文名稱(中文) 應用間接量測方法於橋樑沖刷預測與安全評估
論文名稱(英文) Proxy-Based Models for Bridge Scour Prediction and Safety Evaluation
校院名稱 成功大學
系所名稱(中) 土木工程學系
系所名稱(英) Department of Civil Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 黃薰毅
研究生(英文) Hsun-Yi Huang
學號 n68961157
學位類別 博士
語文別 英文
論文頁數 109頁
口試委員 指導教授-馮重偉
中文關鍵字 橋梁沖刷  間接測量  基因演算法  類神經網路  蒙地卡羅模擬  自然震動頻率  有限元素法 
英文關鍵字 Bridge Scour  Genetic algorithms  Artificial neural networks  Monte Carlo method  Bridge natural frequency  Finite element method 
中文摘要   河水沖刷致使橋基裸露已業經調查為橋梁致災最重要原因之一,橋梁基礎沖刷行為是一個長期的過程,由於平時沖刷的累積,在汛期來臨,尤其遭受颱風侵襲時,致使橋梁基礎無法承受瞬間過大的沖刷而傾倒斷裂,進而造成重大的災害。由於橋梁基礎沖刷通常位於水面之下,不若其他橋梁構件可以目視檢測法初步判別橋損狀況,在基礎沖刷量測不易下,沖刷橋梁造成無預警的損壞倒塌,對用路人生命財產產生相當大的風險。
英文摘要   In this thesis, two models were developed for the bridge scour issue: a bridge scour prediction model and a safety evaluation model. Bridge scour is a long-term process that occurs under water and cannot be observed directly. This results in sudden bridge failures and disasters.
  A reasonable model for predicting bridge scour and preventing bridge failures is important. However, because measuring scour is a difficult task, there is a shortage of scour records. Therefore, to establish a bridge scour prediction model, this thesis uses rainfall events and variance in riverbed elevations extracted from the literature as proxy indexes, and integrates genetic algorithms, artificial neural networks, and the Monte Carlo method. A real case is illustrated, and can provide a reference for bridge management authorities.
  Scouring around bridge piers is an important safety issue of bridge management since it can lead to bridge slanting and collapse. Since the water flow mechanism around a pier structure is complicated, it is very difficult to develop a generic model to determine the scour depth. Many researchers have tried to estimate the scour depth around bridge piers by simulating a bridge model with a consideration of various factors such as the depth of the water, the average flow velocity, and the diameter of the sand. However, most models require predefined conditions and can only be applied to certain types of bridges.
  In addition, the bridge natural frequency as another proxy index is introduced to evaluate the bridge scour conditions and reduce the difficulty in measuring the bridge scour. A complicated finite element model and a simple soil spring model, which is an improved form of the complicated finite element model, are implemented to simulate the bridge structure and compute the bridge natural frequency along the scour depth when considering various environmental variables. The complex relationship among factors is modeled using support vector machine and the level of safety can be efficiently determined.
  The results of this study will provide bridge management authorities with efficient and effective methods to predict bridge scour and evaluate the bridge status using proxy indexes to reduce the rate of disaster occurrence from bridge failures.
論文目次 中文摘要 i
Abstract ii
誌謝 iv
Chapter 1 Introduction 1
1.1 Research background 1
1.2 Research objectives 3
1.3 Research scope 4
1.4 Research flowchart 5
1.5 Thesis organization 6
Chapter 2 Problem Statement and Literature Review 8
2.1 Problem statement 8
2.2 Mechanics of bridge scour 10
2.2.1 Introduction to bridge scour behavior, types, and factors 10
2.2.2 Literature review of bridge scour mechanics 13
2.2.3 Literature review of bridge scour depth analyses 14
2.2.4 Literature review of bridge safety prediction 16
2.3 Bridge scour field measurements 18
2.4 Evaluation of bridge conditions in Taiwan 21
2.5 Summary 23
Chapter 3 Methodology 25
3.1 Genetic Algorithms 25
3.1.1 Introduction 25
3.1.2 Chromosome Representation, Fitness Computation and Termination Procedure 27
3.1.3 Crossover, Mutation and Selection Schemes 28
3.1.4 GA features 32
3.2 Artificial Neural Networks 32
3.2.1 Overview of ANNs 33
3.2.2 Feed-forward back-propagation neural networks 35
3.2.3 Activation function 38
3.2.4 ANN features 40
3.3 Monte Carlo Method 41
3.4 Bridge Natural Frequency 43
3.4.1 Calculation of natural frequency 43
3.4.2 Effective mass and parallel subspace iteration methods 45
3.4.3 Complicated finite element model and simple soil spring model 46
3.5 Support vector machine 49
3.5.1 Introduction of support vector machine 50
3.5.2 Optimal parameter selection technique 53
3.6 Summary 54
Chapter 4 Proxy-based evaluation models for bridge scour 56
4.1 Model I: proxy-based scour bridge safety prediction 56
4.1.1 Crucial factors for extraction 57
4.1.2 Level determination for proxy index 61
4.1.3 Application of artificial neural networks 63
4.1.4 Monte Carlo simulation for rainfall events 65
4.2 Model II: proxy-based bridge scour condition evaluation 67
4.2.1 Programs for the bridge modeling 67
4.2.2 Environmental factors of bridge structure 69
4.2.3 Application of support vector machine 71
4.3 Summary 72
Chapter 5 Case study 74
5.1 Predicting level of bridge scour according to rainfall events 74
5.1.1 Case bridge information and data setting 74
5.1.2 Level determination of rainfall events using genetic algorithms 76
5.1.3 Simulation results 80
5.2 Scour condition estimation using bridge natural frequency 82
5.2.1 Case bridge information 82
5.2.2 Examination of the proposed approach 83
5.2.3 Analysis of bridge natural frequency and bridge foundation scour 85
5.2.4 Bridge scour safety level judgment using a support vector machine 94
5.3 Summary 98
Chapter 6 Conclusions and future research 100
6.1 Conclusions 100
6.2 Future research 102
Reference 104
參考文獻 Anderson, D., & McNeil, G. (1992). Artifical Neural networks Technology: Data & Analysis Center for Software.
Bateni, S. M., Borghei, S. M., & Jeng, D.-S. (2007). Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 20(3), 401-414. doi: 10.1016/j.engappai.2006.06.012
Bathe, K. J., & Wilson, E. L. (1972). Large eigenvalue problems in dynamic analysis. Journal of Engineering Mechanics, 98(6), 1471-1485.
Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Avoiding model selection bias in small-sample genomic datasets. Bioinformatics, 22(10), 1245-1250. doi: 10.1093/bioinformatics/btl066
Bolduc, L. C., Gardoni, P., & Briaud, J. L. (2008). Probability of Exceedance Estimates for Scour Depth around Bridge Piers. Journal of Geotechnical and Geoenvironmental Engineering, 134(2), 175-184.
Bozkus, Z., & Cesme, M. (2010). Reduction of Scouring Depth by Using Inclined Piers. Canadian Journal of Civil Engineering, 37(12), 1621-1630.
Buffon, G. C. D. (1777). Essai d'arithmetique morale. Supplement a l'Histoire Naturelle, 4.
Cheng, M.-Y., Chen, S.-J., Leu, S.-S., Jeng, V., Huang, C.-T., Yeh, C.-H., Lin, Y.-B. (2006). Computer-aided Decision Support System for Hazard Prevention of Traffic / Transportation Engineering Infrastructure (1/2) Taiwan (R.O.C.): Harbor & Marine Technology Center.
Chung, W.-S., Tsai, W.-H., & Kung, C.-S. (2010). White Book on Water Infrastructure Strategies to Climate Change. Taiwan: Water Resources Agency, Ministry of Economic Affairs, Taiwan (R.O.C.).
Coleman, E. (2005). Clearwater Local Scour at Complex Piers. Journal of Hydraulic Engineering, 131(4), 330-334.
Dargahi, B. (1990). Controlling Mechanism of Local Scouring. Journal of Hydraulic Engineering, 116(10), 1197-1214. doi: 10.1061/(ASCE)0733-9429(1990)116:10(1197)
Davis, L. (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
DeFalco, F., & Mele, R. (2002). The Monitoring of Bridges for Scour by Sonar and Sedimetri. NDT & E International, 35(2), 117-123.
Deng, L., & Cai, C. S. (2010). Bridge Scour: Prediction, Modeling, Monitoring, and Countermeasures—Review. Practice periodical on structural design and construction, 15(2), 125-134.
Falco, F. D., & Mele, R. (2002). The monitoring of bridges for scour by sonar and sedimetri. NDT & E International, 35(2), 117-123.
Firat, M., & Gungor, M. (2009). Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 40(8), 731-737.
Forde, M. C., McCann, D. M., Clark, M. R., Broughton, K. J., Fenning, P. J., & Brown, A. (1999). Radar measurement of bridge scour. NDT & E International, 32(8), 481-492.
Gen, M., & Cheng, R. (1997). Genetic Algorithms and Engineering Design. New York: Wiley.
Gen, M., & Cheng, R. (1999). Genetic Algorithms and Engineering Optimization. New York: Wiley.
Goldberg, D. E. (1989). Genetic algorithms in search: Addison-Wesley.
He, J., Chen, L., Xu, W. S., & Wang, Z. G. (2006). The evolution analysis of Fengcheng reach under the changing conditions. Paper presented at the International Conference on Fluvial Hydraulics, Lisbon, Portugal.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, Mich: University of Michigan Press.
Hong, J.-H., Chiew, Y.-M., Lu, J.-Y., Lai, J.-S., & Lin, Y.-B. (2012). Houfeng Bridge Failure in Taiwan. Journal Of Hydraulic Engineering, 138(2), 186-198. doi: 10.1061/(ASCE)HY.1943-7900.0000430.
Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2004). A Practical Guide to Support Vector Classification, Technical Report: Department of Computer Science and Information Engineering, National Taiwan University.
Huang, M. Y.-F., & Montgomery, D. R. (2012). Fluvial response to rapid episodic erosion by earthquake and typhoons, Tachia River, central Taiwan. Geomorphology, 175-176(15), 126-138. doi: 10.1016/j.geomorph.2012.07.004
Hydrological Year Book of Taiwan Republic of China 2001 Part I-Rainfall. (2003). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2002 Part I-Rainfall. (2003). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2003 Part I-Rainfall. (2004). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2004 Part I-Rainfall. (2005). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2005 Part I-Rainfall. (2006). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2006 Part I-Rainfall. (2007). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2007 Part I-Rainfall. (2008). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2008 Part I-Rainfall. (2009). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2009 Part I-Rainfall. (2010). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Hydrological Year Book of Taiwan Republic of China 2010 Part I-Rainfall. (2011). Taipei: MINISTRY OF ECONOMIC AFFAIRS
Ibrahim, S. R. (1977). Random Decrement Technique for Modal Identification of Structures. Journal of Spacecraft and Rockets, 14(11), 696-700. doi: 10.2514/3.57251
Japan specifications for highway bridges IV Substructures. (1996). Japan Road Association.
Johnson, P. A. (1992). Reliability-Based Pier Scour Engineering. Journal of Hydraulic Engineering, 118(10), 1344-1358.
Johnson, P. A., & Ayyub, B. M. (1992). Assessing Time-Variant Bridge Reliability Due to Pier Scour. Journal of Hydraulic Engineering, 118(6), 887-903.
Johnson, P. A., & Niezgoda, S. L. (2004). Risk-Based Method for Selecting Bridge Scour Countermeasures. Journal of Hydraulic Engineering,, 130(2), 121-128.
Johnson, P. A. &. Dock, D.A. (1998). Probabilistic Bridge Scour Estimates. Journal of Hydraulic Engineering, 124(7), 750-754.
Ju, S.-H. (1997). Development a nonlinear finite element program with rigid link and contact element, Technical Report: Report of National Science Council, Taiwan(R.O.C.).
Ju, S.-H. (2013). Determination of scoured bridge natural frequencies with soil–structure interaction. Soil Dynamics and Earthquake Engineering, 55, 247-254. doi: 10.1016/j.soildyn.2013.09.015
Kalos, M. H., & Wbitlock, P. A. (1986). Monte Carlo Methods (Vol. I:Basics). Canada: John Wiley & Sons.
Kothyari, U. C., Garde, R. C. J., & Raju, K. G. R. (1992). Temporal Variation of Scour around Circular Bridge Piers. Journal of Hydraulic Engineering, 118(8), 1091-1106.
Kothyari, U. C., Hager, W. H., & Oliveto, G. (2007). Generalized Approach for Clear-Water Scour at Bridge Foundation Elements. Journal of Hydraulic Engineering, 133(11), 1229-1240.
Kumar, A., Kothyari, U. C., & Raju, K. G. R. (2012). Flow Structure and Scour Around Circular Compound Bridge Piers - A Review. Journal of Hydro-environment Research, 6(4), 251-265.
Lai, J. S., Chang, W. Y., & Yen, C. L. (2009). Maximum Local Scour Depth at Bridge Piers under Unsteady Flow. Journal of Hydraulic Engineering, 135(7), 609-614.
Lanczos, C. (1950). An iteration method for the solution of the eigenvalue problem of linear dierential and integral operators. Journal Of Research Of The National Bureau Of Standards Section B-Mathematics And Mathematical, 45(4), 255-282.
Lebeau, K. H., & Wadia-Fascetti, S. J. (2007). Fault Tree Analysis of Constructed Facilities. Journal of Performance of Constructed Facilities, 21(4), 320-326.
Lien, H.-P. (2008). Analysis of Local Scour at Bridge Piers under Sediment-laden Flow Paper presented at the 橋梁耐洪構造及保護工程研討會, Taichung, Taiwan.
Lin, S.-S., Liao, J.-C., Chang, C.-H., Lin, J.-G., & Hsu, S.-Y. (2011). A Study on the Safety Evalution of Cross-river Bridges by Ultimate analysis Method. Paper presented at the 跨河橋梁安全預警系統暨橋梁沖刷實驗室, Taipei, Taiwan.
Lin, T. K., Wang, Y. P., Huang, M. C., & Tsai, C. A. (2013). Implementation of a Vibration-Based Bridge Health Monitoring System on Scour Issue. Applied Mechanics and Materials, 284 - 287(1351-1357). doi: 10.4028/www.scientific.net/AMM.284-287.1351
Liu, J. T., & Lin, H.-l. (2004). Sediment dynamics in a submarine canyon: a case of river–sea interaction. Marine Geology, 207(1-4), 55-81. doi: 10.1016/j.jmarsys.2007.11.0.13.
Lu, J. Y., Hong, J. H., & Su, C. C. (2008). Field Measurements and Simulation of Bridge Scour Variations during Floods. Journal of Hydraulic Engineering, 134(6), 810-821.
Martin-Vide. (1998). Local Scour at Piled Bridge Foundations. Journal of Hydraulic Engineering, 124(4), 439-444.
Melville, B. W. (1997). Pier and Abutment Scour: Integrated Approach. Journal of Hydraulic Engineering, 123(2), 125-136. doi: 10.1061/(ASCE)0733-9429(1997)123:2(125)
Melville, B. W., & Coleman, S. E. (2000). Bridge Scour. New Zealand: the University of Auckland.
Melville, B. W., & Raudkivi, A. J. (1996). Effects of Foundation Geometry on Bridge Pier Scour. Journal of Hydraulic Engineering, 122(4), 203-209.
Michalewicz, Z. (1996). Genetic Algorithm + Data Structure = Evolution Programs. New York: Springer-Verlag.
Millard, S. G., Bungey, J. H., Thomas, C., Soutsos, M. N., Shaw, M. R., & Patterson, A. (1998). Assessing bridge pier scour by radar. NDT & E International, 31(4), 251-258.
Mooney, C. Z. (1997). Mote Carlo Simulation. California: Sara Miller McCune, Sage Publications, Inc.
Mueller, D. S., & Wagner, C. R. (2005). Field observations and evaluations of streambed scour at bridges (pp. 134): Office of Engineering Research and Development, Federal Highway Administration.
Parker, D. B. (1987). Optimal Algorithms for Adaptive Networks: Second Order Back Propagation, Second order Dircct Propagation and Second Order Hebbian Learning Paper presented at the 1st ICNN.
Quinlin, J. R. (1986). Induction of Decision Trees. Machine learning, 1, 81-106.
Raikar, R. V., & Dey, S. (2005). Scour of Gravel Beds at Bridge Piers and Abutments. Proceedings of the Institution of Civil Engineers-Water Management, 158, 157-162.
Raikar, R. V., & Dey, S. (2009). Maximum Scour Depth at Piers in Armor-Beds. Journal of Civil Engineering, 13(2), 137-142.
Richardson, E. V., & Davis, S. R. (2012). Evaluating Scour at Bridges. Fourth Edition. Report No. FHWA-IP-90-017. Hydraulic Engineering Circular 18 (HEC 18), Federal Highway Administration, Washington, DC.
Richardson, J. E., & Panchang, V. G. (1998). Three-Dimensional Simulation of Scour-Inducing Flow at Bridge Piers. Journal of Hydraulic Engineering, 124(5), 530-540. doi: 10.1061/(ASCE)0733-9429(1998)124:5(530)
Richart, F. E., Hall, J. R., & Woods, R. D. (1970). Vibration of soils and foundations. Englewood Cliffs, N.J.: Prentice-Hall.
Schalkoff, R. J. (1997). Artifical Neural Networks. New York: McGraw-Hill.
Sheehan, J. R., & Moorhouse, D. C. (1968). Predicting Safe Capacity of Pile Groups. Civil Engineering, 38(10), 44-48.
Shirole, A. M., & Holt, R. C. (1991). Planning for a Comprehensive Bridge Safety Assurance Program. Transp. Res. Rec.(1290), 137-142.
Taylor, B. J. (2006). Methods and Procedures for the Verificiation and Validation of Artificial Neural Networks. New York: Institute for Scientific Research, INC.
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.
Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
Wardhana, K., & Hadipriono, F. C. (2003). Analysis of Recent Bridge failures in the United States. Journal of Performance of Constructed Facilities, 17(3), 144-150. doi: 10.1061/(ASCE)0887-3828(2003)17:3(144)
Yanmaz, A. M., & Apaydin, M. (2012). Bridge Scour Risk Assessment and Countermeasure Design. American Society of Civil Engineers, 26(4), 499-506. .
Yanmaz, A. M., & Kose, O. (2009). A Semi-Empirical Model for Clear-Water Scour Evolution at Bridge Abutments. Journal of Hydraulic Research, 135, 140-145.
Yao, J., Xia, H., & Zhan, J. (2010). Analysis on the influence of the bridge foundation under scouring on the natural vibration characteristics of piers. China Railway Science, 2010(1), 44-48.
公路養護手冊. (2012). Taipei: 中華民國交通部公路總局.
林呈, 林蔚榮, 何宗浚, 高明哲, & 何鴻文. (2007). 跨河橋梁基礎洪水沖刷之安全檢測與因應作為. Taiwan Highway Engineering, 33(8), 2-48.
昭淩工程顧問股份有限公司. (1995). 公路橋梁一般目視檢測手冊. 中華民國交通部台灣區國道高速公路局
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