An adaptive network-based fuzzy inference system for rock share estimation in forest road construction;Prilagodljivi mrežno-fazni sustav za procjenu udjela stijena pri izgradnji šumskih prometnica

Croatian Journal of Forest Engineering

Volumn 33, Issue 2, 2012, Pages 313-328

  Ghajar, Ismael ^a   Najafi, Akbar ^a   Torabi, Seyed Ali ^b   Khamehchiyan, Mashalah ^a   Boston, Kevin ^c  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b UNIVERSITY OF TEHRAN   (Iran)

^c OREGON STATE UNIVERSITY   (United States)

Author keywords

AHP; ANFIS; Forest road cost; Membership function; Rock proportion

Indexed keywords

ANALYTIC HIERARCHY PROCESS; COST BENEFIT ANALYSIS; FORESTRY; FUZZY INFERENCE; GEOLOGY; HIERARCHICAL SYSTEMS; ROAD CONSTRUCTION; ROADS AND STREETS; ROCKS;

ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEM; ADAPTIVE-NETWORK- BASED FUZZY INFERENCE SYSTEMS; ESTIMATION PROCEDURES; FOREST ROAD COSTS; FOREST ROADS; KEY ELEMENTS; MEMBERSHIPS FUNCTION; ROCK PROPORTIONS; SLOPE FORMATION; TERRAIN SLOPE;

MEMBERSHIP FUNCTIONS;

ANALYTICAL HIERARCHY PROCESS; COST-BENEFIT ANALYSIS; EMBANKMENT; FOREST MANAGEMENT; FUZZY MATHEMATICS; MONTANE FOREST; OPTIMIZATION; ROAD CONSTRUCTION; SLOPE; TERRAIN;

CONSTRUCTION; COSTS; ESTIMATION; FORESTRY; ROADS; ROCK;

EID: 84867440370     PISSN: 18455719     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (49)

1. Ananda, J., Herath, G., 2008: Multi-attribute preference modeling and regional land-use planning. Ecological Economics 65(2): 325-335.
2. Andrews, R., Diederich J., Tickle, A., 1995: A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Base Systems 8(6): 373-389.
3. ASCE Task Committee on Applications of Artificial Neural Networks in Hydrology 2000: Artificial neural networks in hydrology: I: preliminary concepts; II: hydrologic applications. Journal of Hydrological Engineering 5(2): 115-137.
4. Barton, N., Lien, R., Lunde, J., 1974: Engineering classification of rock masses for the design of tunnel support. Rock Mechanics 6(4): 189-236.
5. Bieniawski, Z. T., 1975: Case studies: prediction of rock mass behavior by the geomechanics classification. Proc. 2nd Australia-New Zealand Conference Geomechanics, Brisbane, 36-41 pp.
6. Bouma, J., Brouwer, R., Van Ek, R., 2000: The use of integrated assessment methods in Dutch water management: a comparison of cost-benefit and multi-criteria analysis. Third International Conference of the European Society for Ecological Economics, Vienna, 3-6 pp.
7. Farokhnia, A., Morid, S., Byun, H. R., 2010: Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques. Theoretical and Applied Climatology 10(1-2): 71-81.
8. Gail, M., Brion, T. R., Neelakantan, S. L., 2002: A neuralnetworkbased classification scheme for sorting sources and ages of fecal contamination in water. Water Research 36(15): 3765-3774.
9. Ghajar, I., Najafi, A., Ezzati, S., 2010: Skidding Machines Allocation (SMA) using fuzzy set theory. Croatian Journal of Forest Engineering 31(2): 99-110.
10. nd ed.). Wiley, 562 pp, New York.
11. Hecht-Nielsen, R., 1990: Neurocomputing, Addison-Wesely Publishing Company, Reading, MA.
12. Hoek, E., Bray, J. W., 1981: Rock Slope Engineering. (3rd ed.). Institute of Mining and Metallurgy, 358 pp, London 1981.
13. th Meeting of the Japanese Forestry Society. April 2-4, Japan.
14. Jang, J.S.R., 1992: Self-learning fuzzy controllers based on temporal backpropagation. IEEE Transactions on Neural Networks 3(5): 714-723.
15. Jang, J.S.R., 1993: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3): 665-685.
16. Kangas, J., 1992: Multiple-use planning of forest resources by using the analytic hierarchy process. Scandinavian Journal of Forest Research 7(1-4): 259-268.
17. Kangas, J., Kangas A., 2005: Multiple criteria decision support in forest management - the approach, methods applied, and experiences gained. Forest Ecology and Management 207(1-2): 133-143.
18. Kangas, A., Kangas, J., Laukkanen, S., 2006: Fuzzy multicriteria approval method and its application to two forest planning problems. Forest Science 52(3): 232-242.
19. Kung, G. T., Hsiao, E. C., Schuster, M., Juang, C. H., 2007: A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Computers and Geotechnics 34(5): 385-396.
20. Kurttila, M., Pesonen, M., Kangas, J., Kajanus, M., 2000: Utilizing the analytic hierarchy process (AHP) in SWOT analysis - a hybrid method and its applications to a forest certification case. Forest Policy and Economics 1(1): 41-52.
21. Liu, Y. C., Chen, C. S., 2007: A new approach for application of rock mass classification on rock slope stability assessment. Engineering Geology 89(1-2): 129-143
22. Mamdani, E. H., Assilian, S., 1974: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1): 1-13.
23. Mendoza, G. A., Sprouse, W., 1989: Forest planning and decision making under fuzzy environments: an overview and illustration. Forest Science 35(2): 481-502.
24. Mendoza, G. A., Bare, B. B., Zhou, Z., 1993: A fuzzy multiple objective linear programming approach to forest planning under uncertainty. Agricultural Systems 41(3): 257-274.
25. Murray, D. M., Von Gadow, K., 1991: Prioritizing mountain catchment areas. Journal of Environmental Management 32(4): 357-366.
26. Najjar, Y. M., Basheer, I. A., 1996: Neural network approach for site characterization and uncertainty prediction. ASCE Geological Special Publication 58(1): 134-148.
27. Padmini, D., Ilamparuthi K., Sudheer, K. P., 2008: Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers Geotechnics 35(1): 33-46.
28. Pettifer, G. S., Fookes, P. G., 1994: A revision of the graphical method for assessing the excavatability of rock. Journal of Engineering Geology 27(2): 145-164.
29. Polat, K., Yosunkaya, S., Güneş, S., 2008: Pairwise ANFIS Approach to Determining the Disorder Degree of Obstructive Sleep Apnea Syndrome. Journal of Medical Systems 32(5): 379-387.
30. Rankine, R., Sivakugan, N., 2005: Prediction of paste backfill performance using artificial neural networks. Proceedings of 16th international society for soil mechanics and foundation engineering, Osaka, 1107-1110 pp.
31. Roger, J. S., Sun, C., 1993: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans Neural Netw 4:156-159. doi:10.1109/72.182710
32. Saaty, T. L., 1977: A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15(3): 234-281.
33. Saaty, T. L., 1980: The Analytic Hierarchy Process. McGraw-Hill, New York.
34. Shahin, M. A., Jaksa, M. B., Maier, H. R., 2005: Neural network based stochastic design charts for settlement prediction. Canadian Geotechnical Journal 42(1): 110-120.
35. Shahin, M. A., Jaksa, M. B., Maier, H. R., 2008: State of the art of artificial neural networks in geotechical engineering. Electronic Journal of Geotechnical Engineering 8: 1-26.
36. Sreekanth, P. D., Sreedevi, P. D., Ahmed, S., Geethanjali, N., 2010: Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, DOI 10.1007/s12665-010-0617-0.
37. Stone, M., 1974: Cross-validatory choice and assessment of statistical predictions. Journal of Royal Statistical Society 36(2): 111-147.
38. Stuckelberger, J. A, Heinimann, H. R., Burlet, E. C., 2006: Modeling spatial variability in the life-cycle costs of low-volume forest roads. European Journal of Forest Research 125(4): 377-390.
39. Sugeno, M., Kang, G. T., 1988: Structure identification of fuzzy model. Fuzzy Sets and Systems 28(1): 15-33.
40. Takagi, T., Sugeno, M., 1983: Derivation of fuzzy control rules from human operator's control actions. In: Proc IFAC Symp Fuzzy Inf, 55-60 pp.
41. Tecle, A., Duckstein, L., Korhonen, P., 1994: Interactive, multiobjective programming for forest resources management. Applied Mathematics and Computations 63(1): 75-93.
42. Tekin, E., Akbas, S. O., 2011: Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts. Bulletin of Engineering Geology and the Environment 70(1): 153-161.
43. Tsukamoto, Y., 1979: An approach to fuzzy reasoning method. In: Gupta MM, Ragade RK, Yager RR (eds) Advances in fuzzy set theory and applications. Elsevier, Amsterdam, 137-149 pp.
44. Twomey, J. M., Smith, A. E., 1997: »Validation and verification «. Artificial neural networks for civil engineers: Fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., ASCE, New York, 44-64.
45. Vacik, H., Lexer, M. J., 2001: Application of a spatial decision support system in managing the protection forests of Vienna for sustained yield of water resources. Forest Ecology and Management 143(1-3): 65-76.
46. Wang, C. H., Liu, B. J., Wu, L. S. H., 2010: The Association Forecasting of 13 Variants Within Seven Asthma Susceptibility Genes on 3 Serum IgE Groups in Taiwanese Population by Integrating of Adaptive Neuro-fuzzy Inference System (ANFIS) and Classification Analysis Methods. Journal of Medical Systems 36(1): 175-185.
47. Wickham, G. E., Tiedemann, H. R., Skinner, E. H., 1972: Support determination based on geologic predictions. Proc. Conf. Rapid Excavation and Tunneling, 43-64 pp.
48. Yilmaz, I., Yuksek, G., 2009: Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. International Journal of Rock Mechanics & Mining Sciences 46(4): 803-810.
49. Zadeh, L. A., 1965: Fuzzy sets. Inf Control. 8: 338-353.