Soft computing methodologies for estimation of bridge girder forces with perforations under tsunami wave loading

详细信息    查看全文

• 作者：Shatirah Akib (4)
  Sadia Rahman (4)
  Shahaboddin Shamshirband (2) (3)
  Dalibor Petkovi膰 (1)
  
  4. Department of Civil Engineering ; University of Malaya ; 50603聽 ; Kuala Lumpur ; Malaysia
  2. Department of Computer System and Technology ; Faculty of Computer Science and Information Technology ; University of Malaya ; 50603 ; 聽Kuala Lumpur ; Malaysia
  3. Department of Computer Science ; Chalous Branch ; Islamic Azad University (IAU) ; 46615-397聽 ; Chalous ; Mazandaran ; Iran
  1. Department for Mechatronics and Control ; Faculty of Mechanical Engineering ; University of Ni拧 ; Aleksandra Medvedeva 14 ; 18000聽 ; Nis ; Serbia
  
• 关键词：Bridge girder force ; Perforation ; Tsunami wave ; ANFIS ; SVR
• 刊名：Bulletin of Earthquake Engineering
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：13
• 期：3
• 页码：935-952
• 全文大小：1,304 KB
• 参考文献：1. Abderrahmane K, Hakim B, Youcef M, Mounir N (2014) Vulnerability assessment of reinforced concrete bridge structures in Algiers using scenario earthquakes. Bull Earthq Eng 12:807鈥?27 CrossRef
  2. Adankon MM, Cheriet M (2011) Help-training for semi-supervised support vector machines. Pattern Recognit 44:2220鈥?230 CrossRef
  3. Akib S, Othman F, Othman I, Amini A, Marzuki MS (2009) Local scour at integral bridges with single and double row piles in a two- stage channel. In: H2009: 32nd hydrology and water resources symposium
  4. Akib S, Shirazi SM, Sholichin M, Othman F, Fayyadh MM, Primasari B (2011a) Influence of flow shallowness on scour depth at semi-integral bridge piers. Adv Mater Res 243:4478鈥?481 CrossRef
  5. Akib S, Fayyadh MM, Othman I (2011b) Structural behaviour of a skewed integral bridge affected by different parameters. J Road Bridge Eng 6(2):107鈥?14 CrossRef
  6. Akib S, Mashodi N, Rahman S (2013a) Semi-integral bridge scour countermeasure using Gabion and crushed concrete mixed with palm shell: a review. J Sci Technol 51(2B):59鈥?8
  7. Akib S, Mohammadhassani M, Jahangirzadeh A (2013b) Application of ANFIS and LR in prediction of scour depth in bridges. Comput Fluids. doi:10.1016/j.compfluid.2013.12.004
  8. Aldair AA, Wang WJ (2011) Design an intelligent controller for full vehicle nonlinear active suspension systems. Int J Smart Sens Intell Syst 4(2):224鈥?43
  9. Ananthakrishnan S, Prasad R, Stallard D, Natarajan P (2013) Batch-mode semi-supervised active learning for statistical machine translation. Comput Speech Lang 27:397鈥?06 CrossRef
  10. Areed FG, Haikal AY, Mohammed RH (2010) Adaptive neuro-fuzzy control of an induction motor. Ain Shams Eng J 1:71鈥?8 CrossRef
  11. Asakura R, Iwase RK, Ikeya T, Takao M, Kaneko T, Fujii N, Omori M (2000) An experimental study on wave force acting on on-shore structures due to overflowing tsunamis. Proc Coast Eng 47:911鈥?15 (in Japanese) CrossRef
  12. Ballantyne D (2006) Sri Lanka lifelines after the December 2004 Great Sumatra earthquake and tsunami. Earthq Spectr 22(S3):545鈥?59 CrossRef
  13. Barlas TK, van Kuik GAM (2005) Application of neural network controller for maximum power extraction of a grid-connected wind turbine system. Electron Eng 88:45鈥?3 CrossRef
  14. Dastranj MR, Ebroahimi E, Changizi N, Sameni E (2011) Control DC motorspeed with adaptive neuro-fuzzy control (ANFIS). Aust J Basic Appl Sci 5(10):1499鈥?504
  15. Fayyadh M, Akib S, Othman I, Razak HA (2011) Experimental investigation and finite element modelling of the effects of flow velocities on a skewed integral bridge. Simul Model Pract Theory 19(9):1795鈥?810 CrossRef
  16. Federico C, Carlo P, Riccardo R, Massimiliano G, Claudio M (2014) An integrated procedure for management of bridge networks in seismic areas. Bull Earthq Eng 12:807鈥?27 CrossRef
  17. Ghobarah A, Saatcioglu M, Nistor I (2006) The impact of 26 December 2004 earthquake and tsunami on structures and infrastructure. Eng Struct 28:312鈥?26 CrossRef
  18. Grigorie TL, Botez RM (2009). Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling. Proc Inst Mech Eng, Part G: J Aerosp Eng 223(6):655-668
  19. Iemura H, Pradono MH, Tada T (2007) Experiments of tsunami force acting on bridge models. J Earthq Eng 29:902鈥?11
  20. Jain P, Garibaldib JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33:216鈥?23 CrossRef
  21. Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665鈥?85 CrossRef
  22. Japan Port and Harbour Association (1999) Technical standards and commentaries of port and harbour facilities
  23. Kassem AM (February 2012) Neural control design for isolated wind generation system based on SVC and nonlinear autoregressive moving average approach. WSEAS Trans Syst 11(2):39鈥?9
  24. Kataoka S, Kusakabe T, Nagaya K (2006) Wave force acts on a bridge girder struck by tsunami. In: Proceedings of the 12th Japan earthquakeengineering symposium, pp 154鈥?57
  25. Khajeh A, Modarress H, Rezaee B (2009) Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Syst Appl 36(3):5728鈥?732
  26. Li H, Shi KL, McLaren P (2005) Capture neural network based sensorless maximum wind energy, with compensated power coefficient. IEEE Trans Ind Appl 41(6):1548鈥?556 CrossRef
  27. Lukkunaprasit P, Ruangrassamee A (2008) Buildings damage in Thailand in 2004 Indian Ocean tsunami and clues for tsunami-resistant design. Inst Eng Singap J Part A Civ Struct Eng 1(1):17鈥?0
  28. Lukkunaprasit P, Lau TL (2011) Influence of bridge deck on tsunami loading on inland bridge piers. IES J Part A Civ Struct Eng 4(2):115鈥?21 CrossRef
  29. Maheshwari BK, Sharma ML, Narayan JP (2006) Geotechnical and structural damage in Tamil Nadu, India, from the December 2004 Indian Ocean Tsunami. Earthq Spectr 22(S3):475鈥?93 CrossRef
  30. Matsutomi H (1991) The pressure distribution and the total wave force. Coast Eng Jpn 38:626鈥?30
  31. Mizutani S, Imamura F (2000) Hydraulic experimental study on wave force of a bore acting on a structure. Proc Coast Eng 47:946鈥?50 (in Japanese) CrossRef
  32. Moustakidis SP, Rovithakis GA, Theocharis JB (2008) An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems. Automatica 44:1418鈥?425 CrossRef
  33. Nistor I, Saatcioglu M, Ghobarah A (2005) The 26 December 2004 earthquake and tsunami-hydrodynamic forces on physical infrastructure in Thailand and Indonesia. In: Proceedings 2005 Canadian coastal engineering conference, Halifax, Canada, CD-ROM, pp 15
  34. Omar BAA, Haikal AYM, Areed FFG (2011) Design adaptive neuro-fuzzy speed controller for an electro-mechanical system. Ain Shams Eng J 2:99鈥?07 CrossRef
  35. Ornella L, Tapia E (2010) Supervised machine learning and heterotic classification of maize ( / Zea mays L.) using molecular marker data. Comput Electron Agric 74:250鈥?57 CrossRef
  36. Petkovi膰 D, 膯ojba拧i膰 沤 (2012) Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput Appl 21(8):2065鈥?070 CrossRef
  37. Petkovi膰 D, Issa M, Pavlovi膰 ND, Pavlovi膰 NT, Zentner L (2012a) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst Appl 39(10):9477鈥?482, ISSN 0957鈥?174
  38. Petkovi膰 D, Issa M, Pavlovi膰 ND, Zentner L, 膯ojba拧i膰 沤 (2012b) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Appl 39(18):13295鈥?3304, ISSN 0957鈥?174
  39. Petkovi膰 D, Pavlovi膰 ND (2013) Applications and adaptive neuro-fuzzy estimation of conductive silicone rubber properties. Strojarstvo: 膷asopis za teoriju i praksu u strojarstvu 54(3)
  40. Petkovi膰 D, 膯ojba拧i膰 沤, Nikoli膰 V (2013a) Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renew Sustain Energy Rev 28:191鈥?95. doi:10.1016/j.rser.2013.07.049 CrossRef
  41. Petkovi膰 D, Pavlovi膰 ND, 膯ojba拧i膰 沤, Pavlovi膰 NT (2013b) Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces. Expert Syst Appl 40(1):281鈥?86, ISSN 0957鈥?174
  42. Petkovi膰 D, 膯ojba拧i膰 沤, Luki膰 S (2013c) Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Expert Syst Appl 40(11):4490鈥?495
  43. Petkovi膰 D, Shahaboddin S, 膯ojba拧i膰 沤, Nikoli膰 V, Anuar NB, Sabri AQM, Akib S (2013e) Adaptive neuro-fuzzy estimation of building augmentation of wind turbine power. Comput Fluids 97:188鈥?94
  44. Petkovi膰 D, Issa M, Pavlovi膰 ND, Zentner L (2013d) Intelligent rotational direction control of passive robotic joint with embedded sensors. Exp Syst Appl 40(4):1265鈥?273, ISSN 0957鈥?174
  45. Peymanfar A, Khoei A, Hadidi K (2010) Design of a general proposed neuro-fuzzy controller by using modified adaptive-network-based fuzzy inference system. Int J Electron Commun 64:433鈥?42 CrossRef
  46. Rahman S, Akib S, Khan M, Triatmadja R (2014) Performance of bridge girder with perforations under tsunami wave loading. Int J Civil Arch Sci Eng 8(2):17鈥?2
  47. Rahman S, Akib S, Khan MTR, Shirazi SM (2014) Experimental study on tsunami risk reduction on coastal building fronted by sea wall. Sci World J 7. doi:10.1155/2014/729357
  48. Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578鈥?587 oceaneng.2008.08.004" target="_blank" title="It opens in new window">CrossRef
  49. Saatcioglu M, Ghobarah A, Nistor I (2006) Performance of structures in Indonesia during the 2004 Sumatra earthquake and tsunami. Earthq Spectr 22(S3):295鈥?20 CrossRef
  50. Scawthorn C, Ono T, Iemura H, Ridha M, Purwanto B (2006) Performance of lifelines in Banda Aceh, Indonesia, during the December 2004 Great Sumatra earthquake and tsunami. Earthq Spectr 22(S3):511鈥?44 CrossRef
  51. Sedighizadeh M, Rezazadeh A (2008) Adaptive PID control of wind energy conversion systems using RASP1 mother wavelet basis function networks. In: Proceedings of World Academy of Science, Engineering and Technology, volume 27, February 2008 ISSN, pp 1307鈥?884
  52. Shahaboddin S, Iqbal J, Petkovi膰 D, Mirhashemi MA (2014a) Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology. Adv Eng Softw 76:148鈥?53
  53. Shahaboddin S, Patel A, Anuar NB, Kiah MLM, Abraham A (2014b) Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks. Eng Appl Artif Intell 32:228鈥?41
  54. Shahaboddin S, Petkovi膰 D, Anuar NB, Gani A (2014c) Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models. Renew Sustain Energy Rev 36:270鈥?76
  55. Shahaboddin S, Petkovi膰 D, Anuar NB, Mat Kiah ML, Akib S, Gani A, 膯ojba拧i膰 沤, Nikoli膰 V (2014d) Sensorless estimation of wind speed by adaptive neuro-fuzzy methodology. Int J Electr Power Energy Syst 62:490鈥?95
  56. Sheth A, Sanyal S, Jaiswal A, Gandhi P (2006) Effects of the December 2004 Indian Ocean Tsunami on the Indian Mainland. Earthq Spectr 22(S3):435鈥?73 CrossRef
  57. Shoji G, Mori Y (2006) Hydraulic model experiment to simulate the damage of a bridge deck subjected to tsunamis. Annu J Coast Eng 53(2):801鈥?05 (in Japanese)
  58. Sivakumar R, Balu K (2010) ANFIS based distillation column control. IJCA special issue on evolutionary computation for optimization techniques, pp 67鈥?3
  59. Tian L, Collins C (2005) Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15(10):1305鈥?320 CrossRef
  60. Tomita T, Imamura F, Arikawa T, Yasuda T, Kawata Y (2006) Damage caused by the 2004 Indian Ocean Tsunami on the southwestern coast of Sri Lanka. Coast Eng J 48(2):99鈥?16 CrossRef
  61. Unjoh S (2005) Damage to transportation facilities. The damage induced by Sumatra earthquake and associated tsunami of December 26, 2004, A report of the reconnaissance team of Japan Society of Civil Engineers, pp 66鈥?6
  62. Unjoh S (2007) Bridge damage caused by tsunami. Bull Jpn Assoc Earthq Eng 6:26鈥?8 (in Japanese)
  63. Wahida Banu RSD, Shakila Banu A (2011) Identification and control of nonlinear systems using soft computing techniques. Int J Model Optim 1(1):24鈥?8
  64. Wei Z, Tao T, ZhuoShu D, Zio E (2013) A dynamic particle filter-support vector regressio n method for reliability prediction. Reliab Eng Syst Saf 119:109鈥?16 CrossRef
  65. Yamamoto TH, Hettiarachchi S, Samarawickrama S (2006) Verification of the destruction mechanism of structures in Sri Lanka and Thailand due to the Indian Ocean tsunami. Coast Eng J 48(2):117鈥?46 CrossRef
  66. Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72:2659鈥?669 CrossRef
  67. Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36:6527鈥?535 CrossRef
  68. Zhang LI, Zhou W-D, Chang P-C, Yang J-W, Li F-Z (2013) Iterated time series prediction with multiple support vector regression models. Neurocomputing 99:411鈥?42 CrossRef
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geotechnical Engineering
  Civil Engineering
  Geophysics and Geodesy
  Hydrogeology
  Structural Geology
  
• 出版者：Springer Netherlands
• ISSN：1573-1456

文摘

Tsunamis pose a great threat to coastal infrastructures. Bridges without adequate provisions for earthquake and tsunami loading generally are vulnerable when a tsunami occurs. During the last two disastrous tsunami events (i.e., the tsunami in the Indian Ocean and the tsunami that struck Japan), many bridges were damaged by the waves created by the tsunamis. In this paper, in order to address this crucial problem, we used soft computing techniques to design and develop a process that simulates the effects of perforations in the girders of bridges on reducing the forces applied on the bridge when a tsunami occurs. Soft computing methods have very good learning and prediction capabilities, which make it an effective tool for dealing with the uncertainties encountered when waves are generated by a tsunami. Laboratory experiments were conducted to acquire a better understanding of the effects of the factors involved and to check the data required for the soft computing methods. In order to predict the effects of perforations in the girder of a bridge on force reduction, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were investigated. In this study, the polynomial, linear, and radial basis function were used as the kernel function of the SVR to estimate the effects of perforations in a girder of a bridge. The performances of the proposed estimators were confirmed by simulation results. The SVR results were compared with the ANFIS results, and we observed that an improvement in predictive accuracy and the ability to generalize were achieved by the ANFIS approach.