메뉴 건너뛰기




Volumn 48, Issue 3, 2011, Pages 427-433

Prediction of hard rock TBM penetration rate using particle swarm optimization

Author keywords

Particle swarm optimization; Rock mass properties; TBM penetration rate

Indexed keywords

BORING MACHINES (MACHINE TOOLS); CONSTRUCTION EQUIPMENT; FRACTURE MECHANICS; PARTICLE SWARM OPTIMIZATION (PSO); ROCK MECHANICS;

EID: 79952623576     PISSN: 13651609     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijrmms.2011.02.013     Document Type: Article
Times cited : (191)

References (41)
  • 1
    • 85154529468 scopus 로고    scopus 로고
    • Development of rock fracture & brittleness indices to quantifying the effects of rock mass features & toughness in the CSM model basic penetration for hard rock tunneling machines. PhD thesis, Colorado School of Mines, Golden, Colo
    • Yagiz S. Development of rock fracture & brittleness indices to quantifying the effects of rock mass features & toughness in the CSM model basic penetration for hard rock tunneling machines. PhD thesis, Colorado School of Mines, Golden, Colo, 2002.
    • (2002)
    • Yagiz, S.1
  • 2
    • 65749099794 scopus 로고    scopus 로고
    • Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
    • Yagiz S., Gokceoglu C., Sezer E., Iplikci S. Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 2009, 22:818-824.
    • (2009) Eng Appl Artif Intell , vol.22 , pp. 818-824
    • Yagiz, S.1    Gokceoglu, C.2    Sezer, E.3    Iplikci, S.4
  • 3
    • 39749166977 scopus 로고    scopus 로고
    • Utilizing rock mass properties for predicting TBM performance in hard rock condition
    • Yagiz S. Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunnel Underground Space Tech 2008, 23:326-339.
    • (2008) Tunnel Underground Space Tech , vol.23 , pp. 326-339
    • Yagiz, S.1
  • 4
    • 85154600230 scopus 로고    scopus 로고
    • Factors influencing performance of hard rock tunnel boring machine. In: Proceedings of EUROCK'09, Dubrovnik, Croatia
    • Yagiz S, Rostami J, Kim T, Ozdemir L, Merguerian C. Factors influencing performance of hard rock tunnel boring machine. In: Proceedings of EUROCK'09, Dubrovnik, Croatia, 2009, p. 695-700.
    • (2009) , pp. 695-700
    • Yagiz, S.1    Rostami, J.2    Kim, T.3    Ozdemir, L.4    Merguerian, C.5
  • 5
    • 85154599124 scopus 로고
    • Rock hardness index properties and geotechnical parameters for predicting tunnel boring machine performance. PhD thesis, University of Illinois Urbana-Champaign
    • Tarkoy PJ. Rock hardness index properties and geotechnical parameters for predicting tunnel boring machine performance. PhD thesis, University of Illinois Urbana-Champaign, 1975.
    • (1975)
    • Tarkoy, P.J.1
  • 6
    • 85154543436 scopus 로고
    • Development of theoretical equations for predicting tunnel borability. PhD thesis, Colorado School of Mines, Golden, Colo
    • Ozdemir L. Development of theoretical equations for predicting tunnel borability. PhD thesis, Colorado School of Mines, Golden, Colo, 1977.
    • (1977)
    • Ozdemir, L.1
  • 7
    • 85154557298 scopus 로고
    • Tunnel boring machine performance in sedimentary rocks. Report to Goldberg-Zoino Association, by School of Civil & Environmental Engineering, Cornell University
    • Nelson PP, O'Rourke TD. Tunnel boring machine performance in sedimentary rocks. Report to Goldberg-Zoino Association, by School of Civil & Environmental Engineering, Cornell University, 1983, 438 pp.
    • (1983) , pp. 438
    • Nelson, P.P.1    O'Rourke, T.D.2
  • 8
    • 0023823705 scopus 로고
    • Hard rock tunnel boring: prognosis and costs
    • Lislerud A. Hard rock tunnel boring: prognosis and costs. Tunnel Underground Space Tech 1988, 3:9-17.
    • (1988) Tunnel Underground Space Tech , vol.3 , pp. 9-17
    • Lislerud, A.1
  • 9
    • 0027229858 scopus 로고
    • A new model for performance prediction of hard rock TBM. In: Proceedings of the rapid excav tunnel conference, Boston
    • Rostami J, Ozdemir L. A new model for performance prediction of hard rock TBM. In: Proceedings of the rapid excav tunnel conference, Boston, 1993, p. 793-809.
    • (1993) , pp. 793-809
    • Rostami, J.1    Ozdemir, L.2
  • 12
    • 85154610354 scopus 로고    scopus 로고
    • A model for prediction of tunnel boring machine performance. In: Proceedings of the 10th IAEG congress, Nottingham, UK
    • Yagiz S. A model for prediction of tunnel boring machine performance. In: Proceedings of the 10th IAEG congress, Nottingham, UK, 2006.
    • (2006)
    • Yagiz, S.1
  • 13
    • 84959904862 scopus 로고    scopus 로고
    • Recommended rock testing methods for predicting TBM performance: focus on the CSM and NTNU Models. In: Proceedings of the 5th Asian rock mechanics symposium, Tehran, Iran
    • Yagiz S, Rostami J, Ozdemir L. Recommended rock testing methods for predicting TBM performance: focus on the CSM and NTNU Models. In: Proceedings of the 5th Asian rock mechanics symposium, Tehran, Iran, 2008, p. 1523-30.
    • (2008) , pp. 1523-1530
    • Yagiz, S.1    Rostami, J.2    Ozdemir, L.3
  • 14
    • 0034232581 scopus 로고    scopus 로고
    • Verhoef PNW. Modeling tunnel boring machine performance by neuro-fuzzy methods
    • Alvarez Grima M., Bruines P.A. Verhoef PNW. Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnel Underground Space Tech 2000, 15:259-269.
    • (2000) Tunnel Underground Space Tech , vol.15 , pp. 259-269
    • Alvarez Grima, M.1    Bruines, P.A.2
  • 15
    • 4544324798 scopus 로고    scopus 로고
    • Expert systems for applicability of tunnel boring machine in Japan
    • Okubo S., Kfukie K., Chen W. Expert systems for applicability of tunnel boring machine in Japan. Rock Mech Rock Eng 2003, 36:305-322.
    • (2003) Rock Mech Rock Eng , vol.36 , pp. 305-322
    • Okubo, S.1    Kfukie, K.2    Chen, W.3
  • 17
    • 0032768798 scopus 로고    scopus 로고
    • Fuzzy model for the prediction of unconfined compressive strength of rock samples
    • Alvarez Grima M., Babuska R. Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 1999, 36:339-349.
    • (1999) Int J Rock Mech Min Sci , vol.36 , pp. 339-349
    • Alvarez Grima, M.1    Babuska, R.2
  • 18
    • 1242264861 scopus 로고    scopus 로고
    • A fuzzy model to predict the uniaxial compressive strength and modulus of elasticity of a problematic rock
    • Gokceoglu C., Zorlu K. A fuzzy model to predict the uniaxial compressive strength and modulus of elasticity of a problematic rock. Eng Appl Artific Intell 2004, 17:61-72.
    • (2004) Eng Appl Artific Intell , vol.17 , pp. 61-72
    • Gokceoglu, C.1    Zorlu, K.2
  • 19
    • 30344474020 scopus 로고    scopus 로고
    • Estimation of rock modules: for intact rock with an artificial neural network and for rock masses with a new empirical equation
    • Sonmez H., Gokceoglu C., Nefeslioglu H.A., Kayabasi A. Estimation of rock modules: for intact rock with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 2006, 43:224-235.
    • (2006) Int J Rock Mech Min Sci , vol.43 , pp. 224-235
    • Sonmez, H.1    Gokceoglu, C.2    Nefeslioglu, H.A.3    Kayabasi, A.4
  • 20
    • 85154538575 scopus 로고    scopus 로고
    • Development of a fuzzy logic based utilization predictor model for hard rock tunnel boring machines. PhD thesis Colorado School of Mines, Golden, Colo
    • Kim T. Development of a fuzzy logic based utilization predictor model for hard rock tunnel boring machines. PhD thesis Colorado School of Mines, Golden, Colo, 2004.
    • (2004)
    • Kim, T.1
  • 21
    • 33847019533 scopus 로고    scopus 로고
    • Predicting rainfall-intensity using genetic algorithm approach
    • Karahan H., Ceylan H., Ayvaz M.T. Predicting rainfall-intensity using genetic algorithm approach. Hydrol Proc 2007, 21:470-475.
    • (2007) Hydrol Proc , vol.21 , pp. 470-475
    • Karahan, H.1    Ceylan, H.2    Ayvaz, M.T.3
  • 22
    • 33845488712 scopus 로고    scopus 로고
    • Tunneling performance prediction using an integrated GIS and neural network
    • Yoo C., Kim J. Tunneling performance prediction using an integrated GIS and neural network. Comp Geotech 2007, 34:19-30.
    • (2007) Comp Geotech , vol.34 , pp. 19-30
    • Yoo, C.1    Kim, J.2
  • 23
    • 70449523078 scopus 로고    scopus 로고
    • Application of fuzzy inference and non-linear regression methods for predicting rock brittleness
    • Yagiz S., Gokceoglu C. Application of fuzzy inference and non-linear regression methods for predicting rock brittleness. Expert Syst Appl 2010, 37:2265-2272.
    • (2010) Expert Syst Appl , vol.37 , pp. 2265-2272
    • Yagiz, S.1    Gokceoglu, C.2
  • 24
    • 79952625131 scopus 로고
    • Standard practice for preparing rock core specimens and determining dimension and shape tolerances
    • ASTM
    • ASTM Standard practice for preparing rock core specimens and determining dimension and shape tolerances. Amer Soc Test Mater 1995, D4543.
    • (1995) Amer Soc Test Mater
  • 25
    • 85154562139 scopus 로고
    • Standard test method for unconfined compressive strength of intact rock core specimens
    • ASTM
    • ASTM Standard test method for unconfined compressive strength of intact rock core specimens. Am Soc Test Mater 1995, D2938.
    • (1995) Am Soc Test Mater
  • 26
    • 33845516569 scopus 로고
    • Standard test method for splitting tensile strength of intact rock core specimens
    • ASTM
    • ASTM Standard test method for splitting tensile strength of intact rock core specimens. Am Soc Test Mater 1995, D3967.
    • (1995) Am Soc Test Mater
  • 27
    • 53349163498 scopus 로고    scopus 로고
    • Assessment of brittleness using rock strength and density with punch penetration test
    • Yagiz S. Assessment of brittleness using rock strength and density with punch penetration test. Tunnel Underground Space Tech 2009, 24:66-74.
    • (2009) Tunnel Underground Space Tech , vol.24 , pp. 66-74
    • Yagiz, S.1
  • 28
    • 42749085778 scopus 로고    scopus 로고
    • A fuzzy logic model to predict specific energy requirement for TBM performance prediction
    • Acaroglu O., Ozdemir L., Asbury B. A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnel Underground Space Tech 2008, 23:600-608.
    • (2008) Tunnel Underground Space Tech , vol.23 , pp. 600-608
    • Acaroglu, O.1    Ozdemir, L.2    Asbury, B.3
  • 29
    • 0029535737 scopus 로고
    • Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia
    • Kennedy J, Eberthart RC. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, 1995, p. 1942-48.
    • (1995) , pp. 1942-1948
    • Kennedy, J.1    Eberthart, R.C.2
  • 30
    • 33645963421 scopus 로고    scopus 로고
    • Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm
    • Feng X., Chen B., Yang C., Zhou H., Ding X. Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm. Int J Rock Mech Min Sci 2006, 43:789-801.
    • (2006) Int J Rock Mech Min Sci , vol.43 , pp. 789-801
    • Feng, X.1    Chen, B.2    Yang, C.3    Zhou, H.4    Ding, X.5
  • 31
    • 0031700696 scopus 로고    scopus 로고
    • A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computers, Anchorage, Alaska
    • Shi Y, Eberhart R. A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computers, Anchorage, Alaska, 1998, p. 69-73.
    • (1998) , pp. 69-73
    • Shi, Y.1    Eberhart, R.2
  • 32
    • 84901421400 scopus 로고    scopus 로고
    • The swarm and the queen: towards a deterministic and adaptive particle swarm optimization In: Proceedings of ICEC, Washington, DC
    • Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization In: Proceedings of ICEC, Washington, DC, 1999, p. 1951-57.
    • (1999) , pp. 1951-1957
    • Clerc, M.1
  • 33
    • 35349010023 scopus 로고    scopus 로고
    • A split-step particle swarm optimization algorithm in river stage forecasting
    • Chau KW. A split-step particle swarm optimization algorithm in river stage forecasting. J Hydrol 2007, 34:131-135.
    • (2007) J Hydrol , vol.34 , pp. 131-135
    • Chau, K.W.1
  • 34
    • 85154545982 scopus 로고
    • A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro-machine & human science, Nagoya, Japan
    • Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro-machine & human science, Nagoya, Japan, 1995, p. 1942-48.
    • (1995) , pp. 1942-1948
    • Eberhart, R.C.1    Kennedy, J.2
  • 35
    • 0034430526 scopus 로고    scopus 로고
    • A particle swarm optimization for reactive power and voltage control considering voltage security assessment. In: Proceedings of the IEEE conference on transportation power systems
    • Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. In: Proceedings of the IEEE conference on transportation power systems, 2000, p. 1232-39.
    • (2000) , pp. 1232-1239
    • Yoshida, H.1    Kawata, K.2    Fukuyama, Y.3    Takayama, S.4    Nakanishi, Y.5
  • 36
    • 85154618335 scopus 로고    scopus 로고
    • Optimal design of power system stabilizers using particle swarm optimization. In: Proceedings of IEEE
    • Abido MA. Optimal design of power system stabilizers using particle swarm optimization. In: Proceedings of IEEE 2002, vol. 17, p. 723-29.
    • (2002) , vol.17 , pp. 723-729
    • Abido, M.A.1
  • 37
    • 34247475194 scopus 로고    scopus 로고
    • Short-term hydro-thermal scheduling using particle swarm optimization method
    • Yu B., Yuan X., Wang J. Short-term hydro-thermal scheduling using particle swarm optimization method. Energy Conversion Manag 2007, 48:1902-1908.
    • (2007) Energy Conversion Manag , vol.48 , pp. 1902-1908
    • Yu, B.1    Yuan, X.2    Wang, J.3
  • 38
    • 38949186067 scopus 로고    scopus 로고
    • Nonlinear parameter estimation through particle swarm optimization
    • Schwaab M., Biscaia E.C., Monteiro J.L., Pinto J.C. Nonlinear parameter estimation through particle swarm optimization. Chem Eng Sci 2008, 63:1542-1552.
    • (2008) Chem Eng Sci , vol.63 , pp. 1542-1552
    • Schwaab, M.1    Biscaia, E.C.2    Monteiro, J.L.3    Pinto, J.C.4
  • 39
    • 33748929857 scopus 로고    scopus 로고
    • Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River
    • Chau K.W. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J Hydrol 2006, 329:363-367.
    • (2006) J Hydrol , vol.329 , pp. 363-367
    • Chau, K.W.1
  • 40
    • 45049085799 scopus 로고    scopus 로고
    • Particle swarm optimization applied to the design of water supply systems
    • Montalvo I., Izquirerdo J., Perez R., Tung M.M. Particle swarm optimization applied to the design of water supply systems. Comps Math Appl 2008, 56:769-776.
    • (2008) Comps Math Appl , vol.56 , pp. 769-776
    • Montalvo, I.1    Izquirerdo, J.2    Perez, R.3    Tung, M.M.4
  • 41
    • 45049083954 scopus 로고    scopus 로고
    • Design optimization of wastewater collection networks by PSO
    • Izquierdo J., Montalvo I., Perez R., Fuertes V.S. Design optimization of wastewater collection networks by PSO. Comps Math Appl 2008, 56:777-784.
    • (2008) Comps Math Appl , vol.56 , pp. 777-784
    • Izquierdo, J.1    Montalvo, I.2    Perez, R.3    Fuertes, V.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.