Superiority of artificial neural networks over statistical methods in prediction of the optimal length of rock bolts

Journal of Civil Engineering and Management

Volumn 18, Issue 5, 2012, Pages 655-661

  Hasanzadehshooiili, Hadi ^a   Lakirouhani, Ali ^a   Medzvieckas, Jurgis ^b  

^a UNIVERSITY OF ZANJAN   (Iran)

^b VILNIUS GEDIMINAS TECHNICAL UNIVERSITY   (Lithuania)

Author keywords

artificial neural networks; optimal length of rock bolts; sensitivity analysis; statistical methods

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; COEFFICIENT OF DETERMINATION; DESIGN METHOD; DESIGN PHASE; DESIGN PROCEDURE; DIMENSIONLESS MODELS; FRICTION ANGLES; GEOLOGICAL CONDITIONS; MEAN ABSOLUTE ERROR; MINIMUM COST; MULTIPLE LINEAR REGRESSION MODELS; NEURAL NETWORK MODELING; NEURAL NETWORK MODELLING; OPTIMAL MODEL; OPTIMISATIONS; POISSON'S RATIO; POLYNOMIAL MODELS; PREDICTION TOOLS; ROCK BOLTS; ROCK STRUCTURES; ROOT MEAN SQUARED ERRORS; STATISTICAL MODELS; SUPPORT SYSTEMS; TRAINING AND TESTING; UNDERGROUND EXCAVATION;

BOLTS; DESIGN; FORECASTING; FOUNDATIONS; LINEAR REGRESSION; NEURAL NETWORKS; ROCK BOLTING; ROCK MECHANICS; SENSITIVITY ANALYSIS; STATISTICAL METHODS;

OPTIMIZATION;

EID: 84867437192     PISSN: 13923730     EISSN: 18223605     Source Type: Journal    
DOI: 10.3846/13923730.2012.724029     Document Type: Article

References (46)

1. Abu Kiefa, M. A. 1998. General regression neural networks for driven piles in cohesionless soils, Journal of Geotechnical and Geoenvironmental Engineering ASCE 124 (12): 1177-1185. http://dx.doi.org/10.1061/(ASCE)1090-0241 (1998)124:12(1177) (Pubitemid 29103310)
2. Amsiejus, J.; Dirgéliené, N.; Norkus, A.; Zilioniené, D. 2009. Evaluation of soil shear strength parameters via triaxial testing by height versus diameter ratio of sample, The Baltic Journal of Road and Bridge Engineering 4(2): 55-60.
3. Baalousha, Y.; Celik, T. 2011. An integrated web-based data warehouse and artificial neural networks system for unit price analysis with inflation adjustment, Journal of Civil Engineering and Management 17(2): 157-167.
4. Barton, N.; Lien, R.; Lunde, J. 1974. Engineering classification of rock masses for the design of tunnel support, Journal of Rock Rock Mechanics Felsmechanik Mécanique des Roches [Mechanics and Rock Engineering] 6(4): 189- 236.
5. Belevicius, R.; Sesok, D. 2008. Global optimization of grillages using genetic algorithms, Mechanika 6: 38-14.
6. Belevicius, R.; Valentinavicius, S.; Michnevic, E. 2002. Multilevel optimization of grillages, Journal of Civil Engineering and Management 8(2): 98-103. http://dx.doi.org/10.1080/13923730.2002.10531259
7. Cai, Y.; Esaki, T.; Jiang, Y. 2004a. A rock bolt and rock mass interaction model, International Journal of Rock Mechanics and Mining Sciences 41(7): 1055-1067. http://dx.doi.org/10.1016/j.ijrmms.2004.04.005 (Pubitemid 39122666)
8. Cai, Y.; Esaki, T.; Jiang, Y. 2004b. An analytical model to predict axial load in grouted rock bolt for soft rock tunnelling, Tunnelling and Underground Space Technology 19(6): 607-618. http://dx.doi.org/10.1016/j.tust.2004.02.129 (Pubitemid 39239108)
9. Chua, K. M.; Aimone, C.; Majtabai, N. 1992. A numerical study of the effectiveness of mechanical rock bolts in an underground opening excavated by blasting, in Proc. of the 33th U.S. Symposium on Rock Mechanics (USRMS), 3-5 June, 1992, Santa Fe, NM, American Rock Mechanics Association, Paper 92-0285. 10 p.
10. Demuth, H.; Beai, M.; Hagan, M. 1996. Neural network toolbox 5 user's guide. The Math Work, Inc., Natick. 852 p.
11. Dikmen, S. U.; Sonmez, M. 2011. An artificial neural networks model for the estimation of formwork labour, Journal of Civil Engineering and Management 17(3): 340-347. http://dx.doi.org/10.3846/13923730.2011.594154
12. Funahashi, K.-I. 1989. On the approximate realization of continuous mappings by neural networks, Neural Networks 2(3): 183-92. http://dx.doi.org/10. 1016/0893-6080(89)90003-8
13. Goh, A. T. C.; Wong, K. S.; Broms, B. B. 1995. Estimation of lateral wall movements in braced excavation using neural networks, Canadian Geotechnical Journal 32(6): 1059- 1064. http://dx.doi.org/10.1139/t95-103 (Pubitemid 26473454)
14. Hasanzadehshooiili, H.; Lakirouhani, A.; Medzvieckas, J. 2012. Evaluating elastic-plastic behaviour of rock materials using Hoek-Brown failure criterion, Journal of Civil Engineering and Management 18(3): 402-407. http://dx.doi.org/10.3846/13923730.2012.693535
15. Heshmati, A. A.; Alavi, A. H.; Keramati, M.; Gandomi, A. H. 2009. A radial basis function neural network approach for compressive strength prediction of stabilized soil, in Road Pavement Material Characterization and Rehabilitation: Selected Papers from the 2009 GeoHunan International Conference ASCE 191: 147-153.
16. Jankovski, V.; Atkociünas, J. 2010. SAOSYS toolbox as MATLAB implementation in the elastic-plastic analysis and optimal design of steel frame structures, Journal of Civil Engineering and Management 16(1): 103-121. http://dx.doi.org/10.3846/jcem.2010.10
17. Kelevisius, K.; Amsiejus, J.; Skuodis, S. 2011. The influence of changing shaft friction of the pile to wave propagation, Engineering Structures and Technologies 3(2): 64-71. (in Lithuanian)
18. Khandelwal, M.; Singh, T. N. 2006. Evaluation of blast-induced ground vibration predictors, Soil Dynamics and Earthquake Engineering 27(2): 116-125. http://dx.doi.org/10.1016/j.soildyn.2006.06.004
19. Khosrowshahi, F. 2011. Innovation in artificial neural network learning: Learn-On-Demand methodology, Automation in Construction 20(8): 1204-1210.
20. Kim, Y.; Gotoh, K.; Kim, K.; Toyosada, M. 2005. Optimum grillage structure design under a worst point load using real-coded micro-genetic algorithm, in Proc. of the 15th International Offshore and Polar Engineering Conference, 19-24 June, 2005, Seoul, Korea, 730-735.
21. 1999. A new rock bolt design criterion and knowledge-based expert system for stratified roof. PhD Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. 189 p.
22. Maity, D.; Saha, A. 2004. Damage assessment in structure from changes in static parameters using neural networks, Sad-hana 29, Part 3: 315-327. http://dx.doi.org/10.1007/BF02703781
23. Malinowski, P.; Ziembicki, P. 2006. Analysis of district heating network monitoring by neural networks classification, Journal of Civil Engineering and Management 12(1): 21-28. (Pubitemid 43333282)
24. Malinowski, P.; Polarczyk, I.; Piotrowski, J. 2006. Neural model of residential building air infiltration process, Journal of Civil Engineering and Management 12(1): 83-88. (Pubitemid 43333291)
25. Mang, H. A.; Jia, X.; Hoefmger, G. 2009. Hilltop buckling as the A and O in sensitivity analysis of the initial postbuck-ling behavior of elastic structures, Journal of Civil Engineering and Management 15(1): 35-16. http://dx.doi.org/10.3846/1392-3730.2009.15.35-46
26. Maulenkamp, F.; Grima, M. A. 1999. Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness, International Journal of Rock Mechanics and Mining Sciences 36(1): 29-39. http://dx.doi.org/10.1016/S0148-9062(98)00173-9
27. McCulloch, W. S.; Pitts, W. 1988. A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5: 115-133. http://dx.doi.org/10.1007/BF02478259
28. Monjezi, M.; Bahrami, A.; Varjani, A. Y. 2010. Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks, International Journal of Rock Mechanics and Mining Sciences 47(3): 476-480. http://dx.doi.org/10.1016/j.ijrmms.2009.09.008
29. Neaupane, K. M.; Adhikari, N. R. 2006. Prediction of tunnel-ing-induced ground movement with the multi-layer per-ceptron, Tunnelling and Underground Space Technology 21(2): 151-159. http://dx.doi.org/10.1016/j.tust.2005.07.001 (Pubitemid 43045948)
30. Peng, S. S.; Tang, D. H. Y. 1984. Roof bolting in underground mining: a state-of-the-art review, Geotechnical and Geological Engineering 2(1): 1-42.
31. Rosenblatt, F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review 65: 386-408. http://dx.doi.org/10.1037/h0042519
32. Sarle, W. S. 1994. Neural networks and statistical models, in Proc. of the 19th Annual SAS Users Group International Conference, 10-13 April, 1994, Dallas, Texas. 13 p.
33. Schabowicz, K.; Hola, J. 2008. Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios, Archives of Civil and Mechanical Engineering 8(4): 73-84. http://dx.doi.org/10.1016/S1644-9665(12) 60123-X
34. Sesok, D.; Mockus, J.; Belevicius, R.; Kaceniauskas, A. 2010. Global optimization of grillages using simulated annealing and high performance computing, Journal of Civil Engineering and Management 16(1): 95-101. http://dx.doi.org/10.3846/jcem.2010.09
35. 1990. Artificial neural system: foundation, paradigms, applications and implementations. New York: Sipson, Perga on ress. 209
36. Sivilevicius, H.; Daniünas, A.; Zavadskas, E. K.; Turskis, Z.; Susinskas, S. 2012. Experimental study on technological indicators of pile-columns at a construction site, Journal of Civil Engineering and Management 18(4): 512-518. http://dx.doi.org/10.3846/13923730.2012.709958
37. Stankus, J.; Guo, S. 1997. New Design Criteria for Roof Bolt System. in Proc. of the 16th Conference on Ground Control in Mining, 3-5 August, 1997, Morgantown, WV, 158-166.
38. Susinskas, S.; Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria assessment of pile-columns alternatives, The Baltic Journal of Road and Bridge Engineering 6(3): 77-83.
39. Unal, E. 1983. Development of design guidelines and roof-control standards for coal-mine roofs. PhD Dissertation, Pennsylvania State University, University Park, PA. 380 p.
40. Weigel, W. 1943. Channel irons for roof control, Engineering and Mining Journal 144(5): 70-72.
41. Yang, Y.; Rosenbaum, M. S. 2002. The artificial neural network as a tool for assessing geotechnical properties, Geotech-nical and Geological Engineering 20(2): 149-168. http://dx.doi.org/10.1023/A:1015066903985 (Pubitemid 34523482)
42. Yang, Y.; Zhang, Q. 1997. A hierarchical analysis for rock engineering using artificial neural networks, Rock Mechanics and Rock Engineering 30(4): 207-222. http://dx.doi.org/10.1007/BF01045717
43. Yasrebi, S. S. H.; Emami, M. 2008. Application of Artificial Neural Networks (ANNs) in prediction and interpretation of pressuremeter test results, in Proc. of the 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), 1-6 October, 2008, Goa, India, 1634-1638.
44. Young-Su, K.; Byung-Tak, K. 2006. Use of artificial neural networks in the prediction of liquefaction resistance of sands, Journal of Geotechnical and Geoenvironmental Engineering ASCE 132(11): 1502-1504. http://dx.doi.org/10.1061/ (ASCE)1090-0241(2006)132:11(1502) (Pubitemid 44781790)
45. Zarzojus, G.; Dundulis, K. 2010. Problems of correlation between Dynamic Probing Test (DPSH) and Cone Penetration Test (CPT) for cohesive soils of Lithuania, The Baltic Journal of Road and Bridge Engineering 5(2): 69-75. http://dx.doi.org/10.3846/bjrbe.2010.10
46. Zavadskas, E. K.; Turskis, Z.; Valutiené, T. 2010. Multiple criteria analysis of foundation instalment alternatives by applying Additive Ratio Assessment (ARAS) method, Archives of Civil and Mechanical Engineering 10(3): 123- 141. http://dx.doi.org/10.1016/S1644-9665(12)60141-1