Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Engineering with Computers
Erschienen in: Engineering with Computers 1/2018

23.06.2017 | Original Article

Performance prediction of tunnel boring machine through developing a gene expression programming equation

verfasst von: Danial Jahed Armaghani, Roohollah Shirani Faradonbeh, Ehsan Momeni, Ahmad Fahimifar, M. M. Tahir

Erschienen in: Engineering with Computers | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang–Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R 2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.
Vorheriger Artikel Enriched FR conjugate search directions for robust and efficient structural reliability analysis
Nächster Artikel Dependency of modeling parameters for the construction of influence surfaces by the finite element method

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM Model basic penetration for hard rock tunneling machines. Ph.D. Thesis, Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA, p 289 (unpublished)
2.
Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22(4):808–814CrossRef
3.
Roxborough FF, Phillips HR (1975) Rock excavation by disc cutter. Int J Rock Mech Mining Sci Geomech Abstr 12:361–366CrossRef
4.
Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunn Tunn Int 12(6):22–25
5.
Snowdon RA, Ryley MD, Temporal J (1982) A study of disc cutting in selected British rocks. Int J Rock Mech Min Sci 19:107–121CrossRef
6.
Rostami J, Ozdemir L (1993) A new model for performance prediction of hard rock TBM. In: Bowerman LD et al. (eds) Proceedings of RETC, Boston, MA, pp 793–809
7.
Rostami J (1997) Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure. Ph.D. Thesis, Colorado School of Mines, Golden, Colorado, USA
8.
Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock conditions. Tunn Undergr Space Technol 23(3):326–339CrossRef
9.
Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Mining Sci 46(1):8–18CrossRef
10.
Bruines P (1998) Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate. Memoirs of the Centre of Engineering Geology in The Netherlands, Delft, no. 173, p 202. ISSN: 1386-5072
11.
Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23(3):249–262CrossRef
12.
Verma AK, Singh TN (2011) Intelligent systems for ground vibration measurement: a comparative study. Eng Comput 27(3):225–233CrossRef
13.
Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci. doi:10.​1007/​s12517-015-1952-y
14.
Faradonbeh RS, Armaghani DJ, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Environ 75(3):993–1006CrossRef
15.
Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45CrossRef
16.
Singh R, Umrao RK, Ahmad M, Ansari MK, Sharma LK, Singh TN (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119CrossRef
17.
Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of p-wave velocity and anisotropic property of rock using artificial neural network technique. J Sci Ind Res 63(1):32–38
18.
Singh TN, Verma AK, Singh V, Sahu A (2005) Slake durability study of shaly rock and its predictions. Environ Geol 47(2):246–253CrossRef
19.
Armaghani DJ, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci 8(11):9647–9665CrossRef
20.
Armaghani DJ, Mohamad ET, Hajihassani M, Abad SANK, Marto A, Moghaddam MR (2016) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput 32(1):109–121CrossRef
21.
22.
Sharma LK, Vishal V, Singh TN (2017) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Measurement 102:158–169CrossRef
23.
Sharma LK, Vishal V, Singh TN (2017) Predicting CO2 permeability of bituminous coal using statistical and adaptive neurofuzzy analysis. J Nat Gas Sci Eng 42:216–225CrossRef
24.
Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Space Technol 15(3):259–269CrossRef
25.
Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19(6):597–605CrossRef
26.
Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48(3):427–433CrossRef
27.
Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229
28.
Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Env 73(1):23–35CrossRef
29.
Yagiz S, Karahan H (2015) Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. Int J Rock Mech Min Sci 80:308–315
30.
Gholamnejad J, Tayarani N (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Technol (China). 20(5):727–733CrossRef
31.
Simoes MG, Kim T (2006) Fuzzy modeling approaches for the prediction of machine utilization in hard rock tunnel boring machines. In: Industry applications conference, 2006. 41st IAS Annual Meeting. Conference record of the 2006 IEEE. IEEE, pp 947–954
32.
Mikaeil R, Naghadehi MZ, Sereshki F (2009) Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions. Tunn Undergr Space Technol 24(5):500–505CrossRef
33.
Eftekhari M, Baghbanan A, Bayati M (2010) Predicting penetration rate of a tunnel boring machine using artificial neural network. In: ISRM international symposium-6th asian rock mechanics symposium. International Society for Rock Mechanics, pp 1–7
34.
Gholami M, Shahriar K, Sharifzadeh M, Hamidi JK (2012) A comparison of artificial neural network and multiple regression analysis in TBM performance prediction. In: ISRM regional symposium-7th Asian rock mechanics symposium. International Society for Rock Mechanics
35.
Salimi A, Esmaeili M (2013) Utilizing of linear and non-linear prediction tools for evaluation of penetration rate of tunnel boring machine in hard rock condition. Int J Min Min Eng 4(3):249–264CrossRef
36.
Torabi SR, Shirazi H, Hajali H, Monjezi M (2013) Study of the influence of geotechnical parameters on the TBM performance in Tehran-Shomal highway project using ANN and SPSS. Arab J Geosci 6(4):1215–1227CrossRef
37.
Shao C, Li X, Su H (2013) Performance prediction of hard rock TBM based on extreme learning machine. Intelligent robotics and applications. Springer, Berlin, pp 409–416CrossRef
38.
Yavari M, Mahdavi S (2005) Prediction of penetration rate of TBM using ANN. In: National mining conference, 1–3 Feb 2005, Iran, pp 1–10
39.
Oraee K, Khorami MT, Hosseini N (2012) Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (ANFIS). In: Proceeding of SME annual meeting and exhibit, from the mine to the market, now it’s global, Seattle, WA, USA, pp 297–302
40.
Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N, Yagiz S (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43CrossRef
41.
Shirani Faradonbeh R, Jahed Armaghani D, Monjezi M (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull Eng Geol Env. doi:10.​1007/​s10064-016-0872-8
42.
Baykasoglu A, Gullu H, Canakci H, Ozbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123CrossRef
43.
Çanakcı H, Baykasoğlu A, Güllü H (2009) Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput Appl 18(8):1031–1041CrossRef
44.
Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mech Geotech Eng 5(4):325–329CrossRef
45.
Dindarloo SR, Siami-Irdemoosa E (2015) Estimating the unconfined compressive strength of carbonate rocks using gene expression programming. Eur J Sci Res 135(3):309–316
46.
Ahangari K, Moeinossadat SR, Behnia D (2015) Estimation of tunnelling-induced settlement by modern intelligent methods. Soils Found 55(4):737–748CrossRef
47.
Khandelwal M, Armaghani DJ, Faradonbeh RS, Ranjith PG, Ghoraba S (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci 75(9):1–13CrossRef
48.
Dindarloo SR (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. J South Afr Inst Min Metall 115(7):637–643CrossRef
49.
Shirani Faradonbeh R, Jahed Armaghani D, Monjezi M, Mohamad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264
50.
Ferreira C (2001) Algorithm for solving gene expression programming: a new adaptive problems. Complex Syst 13:87–129MATH
51.
Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38:4080–4087CrossRef
52.
Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141:92–113CrossRef
53.
Monjezi M, Baghestani M, Faradonbeh RS, Saghand MP, Armaghani DJ (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Eng Comput 32(4):717–728CrossRef
54.
Shirani Faradonbeh R, Armaghani DJ, Amnieh HB, Mohamad ET (2016) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl. doi:10.​1007/​s00521-016-2537-8
55.
Steeb W-HH (2000) The nonlinear workbook: chaos, fractals, cellular automata. Neural networks, genetic algorithms, fuzzy logic with C++ and reduce programs. World Scientific Press, Singapore
56.
Jahed Armaghani D, Faradonbeh RS, Rezaei H, Rashid ASA, Amnieh HB (2016) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Comput Appl. doi:10.​1007/​s00521-016-2618-8
57.
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning ‘addison-wesley, 1989, Reading
58.
Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer, New YorkMATH
59.
Khandelwal M, Faradonbeh RS, Monjezi M, Armaghani DJ, Majid MZBA, Yagiz S (2016) Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Eng Comput. doi:10.​1007/​s00366-016-0452-3
60.
Mogana SN (2007) The effects of ground conditions on TBM performance in tunnel excavation—a case history. In: Proceedings of the 10th Australia New Zealand conference on Geomechanics 2007, pp 442–447
61.
Mogana SN, Rafek AG, Komoo I (1998) The influence of rock mass properties in the assessment of TBM performance. In: Moore D, Hungr O (eds) 8th IAEG Congr. Vancouver, Balkema, Rotterdam, pp 3553–3559
62.
Wu S, Qian B, Gong Z (2006) The time and cost prediction of tunnel boring machine in tunneling. Wuhan Univ J Nat Sci 11(2):385–398CrossRef
63.
Sapigni M, Berti M, Behtaz E, Busillo A, Cardone G (2002) TBM performance estimation using rock mass classification. Int J Rock Mech Min Sci Geomech Abstr 39:771–788CrossRef
64.
Farrokh E, Rostami J, Laughton C (2012) Study of various models for estimation of penetration rate of hard rock TBMs. Tunn Undergr Space Technol 30:110–123CrossRef
65.
ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods, International Society for Rock Mechanics. ISRM Turkish National Group, Ankara
66.
Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Env. doi:10.​1007/​s10064-014-0687-4
67.
Swingler K (1996) Applying neural networks: a practical guide. Academic, New York
68.
Tonnizam Mohamad E, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2016) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl. doi:10.​1007/​s00521-016-2359-8
69.
Inc SPSS (2007) SPSS for windows (version 160). SPSS Inc, Chicago
Metadaten
Titel
Performance prediction of tunnel boring machine through developing a gene expression programming equation
verfasst von
Danial Jahed Armaghani
Roohollah Shirani Faradonbeh
Ehsan Momeni
Ahmad Fahimifar
M. M. Tahir
Publikationsdatum
23.06.2017
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 1/2018
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-017-0526-x

Weitere Artikel der Ausgabe 1/2018

Engineering with Computers 1/2018 Zur Ausgabe

Original Article

Self-adaptive conjugate method for a robust and efficient performance measure approach for reliability-based design optimization

Original Article

The simplex subdivision of a complex region: a positive and negative finite element superposition principle

Original Article

A not-a-knot meshless method with radial basis functions for numerical solutions of Gilson–Pickering equation

Original Article

Direct local boundary integral equation method for numerical solution of extended Fisher–Kolmogorov equation

Original Article

Development of overbreak prediction models in drill and blast tunneling using soft computing methods

Original Article

Enhanced IGMM optimization algorithm based on vibration for numerical and engineering problems

Neuer Inhalt

    Bildnachweise