Hybrid computational model for predicting bridge scour depth near piers and abutments

Automation in Construction

Volumn 48, Issue , 2014, Pages 88-96

  Chou, Jui Sheng ^a   Pham, Anh Duc ^a,b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b THE UNIVERSITY OF DANANG   (Viet Nam)

Author keywords

Artificial intelligence; Bridge foundation; Data mining; Genetic algorithm; Optimization; Scour depth; Support vector regression

Indexed keywords

ABUTMENTS (BRIDGE); ARTIFICIAL INTELLIGENCE; BRIDGE PIERS; COMPUTATION THEORY; COMPUTATIONAL METHODS; COST EFFECTIVENESS; COST REDUCTION; DATA MINING; FAILURE (MECHANICAL); FORECASTING; GENETIC ALGORITHMS; OPTIMIZATION; PIERS; SCOUR; SUPPORT VECTOR REGRESSION;

AUTOMATIC INTERACTIONS; BRIDGE FOUNDATION; CLASSIFICATION AND REGRESSION TREE; GENERALIZATION PERFORMANCE; HYBRID COMPUTATIONAL MODEL; MULTIPLE REGRESSIONS; SCOUR DEPTH; SUPPORT VECTOR REGRESSION (SVR);

PREDICTIVE ANALYTICS;

EID: 84924061041     PISSN: 09265805     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.autcon.2014.08.006     Document Type: Article

References (39)

1. H. Azamathulla, A. Ghani, N. Zakaria, and A. Guven Genetic Programming to Predict Bridge Pier Scour J. Hydraul. Eng. 136 3 2010 165 169
2. H.M. Azamathulla Gene expression programming for prediction of scour depth downstream of sills J. Hydrol. 460-461 2012 156 159
3. D. Biggs, B. De Ville, and E. Suen A method of choosing multiway partitions for classification and decision trees J. Appl. Stat. 18 1 1991 49 62
4. L. Breiman, J. Friedman, R. Olshen, and C. Stone Classification and Regression Trees 1984 Chapman & Hall/CRC New York
5. J.-S. Chou, M.-Y. Cheng, and Y.-W. Wu Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models Expert Syst. Appl. 40 6 2013 2263 2274
6. J.-S. Chou, M.-Y. Cheng, Y.-W. Wu, and C.-C. Wu Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model Int. J. Proj. Manag. 30 8 2012 967 977
7. J.-S. Chou, C. Chiu, M. Farfoura, and I. Al-Taharwa Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques J. Comput. Civ. Eng. 25 3 2011 242 253
8. J.-S. Chou, and C. Lin Predicting Disputes in Public-Private Partnership Projects: Classification and Ensemble Models J. Comput. Civ. Eng. 27 1 2013 51 60
9. J.-S. Chou, and A.-D. Pham Applying Smart Meter and Data Mining Techniques to Predict Refrigeration System Performance P. Meesad, H. Unger, S. Boonkrong, The 9th International Conference on Computing and InformationTechnology (IC2IT2013) vol. 209 2013 Springer Berlin Heidelberg 249 257
10. J.-S. Chou, and C.-F. Tsai Preliminary cost estimates for thin-film transistor liquid-crystal display inspection and repair equipment: A hybrid hierarchical approach Comput. Ind. Eng. 62 2 2012 661 669
11. S. Dey, and A. Barbhuiya Time Variation of Scour at Abutments J. Hydraul. Eng. 131 1 2005 11 23
12. R. Ettema, G. Constantinescu, and B. Melville Final Report for NCHRP Project Web-Only Document 175: Pier Scour Processes and Predictions Evaluation of Bridge Scour Research 2012 Transportation Research Board Washington DC
13. C.-W. Feng, S.-H. Ju, and H.-Y. Huang Hybridizing Finite Element Method and Genetic Algorithms to Estimate Bridge Scour Depths M.H.H.S. Al Zahir, J. Cheng, The 23rd IASTED International Conference on Modelling and Simulation 2012 ACTA Press Canada
14. D.E. Goldberg Genetic Algorithms in Search 1989 Optimization and Machine Learning Addison-Wesley, Boston, MA, USA
15. W. Haifeng, and H. Dejin Comparison of SVM and LS-SVM for Regression International Conference on Neural Networks and Brain, 2005 ICNN&B '05 vol. 1 2005 279 283
16. J.-H. Hong, M.K. Goyal, Y.-M. Chiew, and L.H.C. Chua Predicting time-dependent pier scour depth with support vector regression J. Hydrol. 468-469 2012 241 248
17. C.-W. Hsu, and C.-J. Lin A Simple Decomposition Method for Support Vector Machines Mach. Learn. 46 1-3 2002 291 314
18. J. Huang, X. Hu, and F. Yang Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker Measurement 44 6 2011 1018 1027
19. W.-K. Kao, H.-M. Chen, and J.-S. Chou Aseismic ability estimation of school building using predictive data mining models Expert Syst. Appl. 38 8 2011 10252 10263
20. G.V. Kass An Exploratory Technique for Investigating Large Quantities of Categorical Data J. R. Stat. Soc.: Ser. C: Appl. Stat. 29 2 1980 119 127
21. M. Khan, and P. Coulibaly Application of Support Vector Machine in Lake Water Level Prediction J. Hydrol. Eng. 11 3 2006 199 205
22. R. Kohavi A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection Paper presented at the International Joint Conference on Artificial Intelligence 1995
23. I. Kucuk, and N. Derebasi Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm Measurement 39 7 2006 605 611
24. C. Lauchlan, and B. Melville Riprap Protection at Bridge Piers J. Hydraul. Eng. 127 5 2001 412 418
25. S.-H. Liao, P.-H. Chu, and P.-Y. Hsiao Data mining techniques and applications - A decade review from 2000 to 2011 Expert Syst. Appl. 39 12 2012 11303 11311
26. H.-B. Lui, and Y.-B. Jiao Application of Genetic Algorithm-Support Vector Machine (GA-SVM) for Damage Identification of Bridge Int. J. Comput. Intell. Appl. 10 04 2011 383 397
27. S. Martino, F. Ferrucci, C. Gravino, and F. Sarro A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components D. Caivano, M. Oivo, M. Baldassarre, G. Visaggio, Product-Focused Software Process Improvement vol. 6759 2011 Springer Berlin Heidelberg 247 261
28. B. Melville, and Y. Chiew Time Scale for Local Scour at Bridge Piers J. Hydraul. Eng. 125 1 1999 59 65
29. M. Mia, and H. Nago Design Method of Time-Dependent Local Scour at Circular Bridge Pier J. Hydraul. Eng. 129 6 2003 420 427
30. J. Neter, M.H. Kutner, C.J. Nachtsheim, and W. Wasserman Applied Linear Statistical Models Fourth ed. 1996 McGraw-Hill/Irwin
31. M. Pal, N.K. Singh, and N.K. Tiwari Support vector regression based modeling of pier scour using field data Eng. Appl. Artif. Intell. 24 5 2011 911 916
32. L. Ren, X. Xiang, and J. Ni Forecast Modeling of Monthly Runoff with Adaptive Neural Fuzzy Inference System and Wavelet Analysis J. Hydrol. Eng. 18 9 2013 1133 1139
33. SPSS Clementine 12.0 Algorithms Guide 2007 Integral Solutions Limited Chicago, USA
34. T.W. Sturm, R. Ettema, and B.W. Melville Final Report for NCHRP Web-Only Document 181: Abutment and Contraction Scour Processes and Prediction Evaluation of Bridge-Scour Research 2012 Transportation Research Board Washington DC
35. X.-h. Tan, W.-h. Bi, X.-l. Hou, and W. Wang Reliability analysis using radial basis function networks and support vector machines Comput. Geotech. 38 2 2011 178 186
36. V. Vapnik Universal learning technology: Support Vector Machines NEC J. Adv. Technol. 2 2 2005 137 144
37. P.-S. Yu, S.-T. Chen, and I.F. Chang Support vector regression for real-time flood stage forecasting J. Hydrol. 328 3-4 2006 704 716
38. W.G. Zhang, and A.T.C. Goh Multivariate adaptive regression splines for analysis of geotechnical engineering systems Comput. Geotech. 48 2013 82 95
39. H.-b. Zhao Slope reliability analysis using a support vector machine Comput. Geotech. 35 3 2008 459 467