Top

Geotechnical and Geological Engineering

Published in:

08-09-2021 | Original Paper

Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm

Authors: Alireza Afradi, Arash Ebrahimabadi, Tahereh Hallajian

Published in: Geotechnical and Geological Engineering | Issue 3/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Tunnel Boring Machine (TBM) penetration rate prediction is one of the most important problem in tunneling projects. Estimating of Tunnel Boring Machine (TBM) penetration rate can considerably reduce the costs of tunneling projects. In this study, Datasets including Uniaxial Compressive Strength, Brazilian Tensile Strength, Density and Joint Angle as input parameters and Rate of Penetration as an output parameter. The aim of this study is estimating the penetration rate of tunnel boring machines using fuzzy logic method, Harmony search algorithm (HSA) and Particle Swarm Optimization (PSO) in the Nosoud water conveyance Tunnel. The modeling results showed that the fuzzy model has a significant advantage over the PSO and HSA.

previous article Frack Evolution and Destruction Characteristics of Mining Bottom Plate on Pressurized Water Based on Similar Simulation

next article Development of a New Empirical Relation to Assess P-wave Velocity Anisotropy of Rocks

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

Adoko AC, Gokceoglu C, Yagiz S (2017) Bayesian prediction of TBM penetration rate in rock mass. Eng Geol 226:245–256. https://doi.org/10.1016/j.enggeo.2017.06.014CrossRef

Afradi A, Ebrahimabadi A, Hallajian T (2019) Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)-Case Study: beheshtabad water conveyance tunnel in Iran. Asian J Water Environ Pollut 16(1):49–57. https://doi.org/10.3233/AJW190006

Afradi A, Ebrahimabadi A, Hallajian T (2020) Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel. Min Miner Depos 14(2):75–84. https://doi.org/10.33271/mining14.02.075CrossRef

Afradi A, Ebrahimabadi A (2021) Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Innovative Infrastructure Solutions 6(2):1–17. https://doi.org/10.1007/s41062-021-00467-3CrossRef

Alebouyeh A, Dehghan AN, Goshtasbi K (2019) Identifying the geological hazards during mechanized tunneling in urban areas–the case of Tehran alluvium conditions. In: Tunnels and underground cities: engineering and innovation meet archaeology, architecture and art. CRC Press, pp 5264–5274

Alipour Shahsavari M, Afzal P, Hekmatnejad A (2020) Identification of Geochemical Anomalies Using Fractal and LOLIMOT Neuro-Fuzzy modeling in Mial Area, Central Iran. J Min Environ 11(1):99–117. https://doi.org/10.22044/jme.2019.8465.1727CrossRef

Allen AD (1973) The bootstrap from the perspective of formal logic. Found Phys 3:473–475. https://doi.org/10.1007/BF00709115CrossRef

Anand V, Pandey S (2017) Particle swarm optimization and harmony search-based clustering and routing in wireless sensor networks. Int J Comput Intell Sys 10(1):1252–1262CrossRef

Anderson CA (1993) Zalta’s intensional logic. Philos Stud 69:221–229. https://doi.org/10.1007/BF00990086CrossRef

Armaghani DJ, Koopialipoor M, Marto A, Yagiz S (2019) Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J Rock Mech Geotech Eng 11(4):779–789. https://doi.org/10.1016/j.jrmge.2019.01.002CrossRef

Armetti G, Migliazza MR, Ferrari F, Berti A, Padovese P (2018) Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy). Tunn Undergr Space Technol 77:115–126. https://doi.org/10.1016/j.tust.2018.02.012CrossRef

Ayvaz MT (2009) Application of harmony search algorithm to the solution of groundwater management models. Adv Water Resour 32(6):916–924CrossRef

Ayvaz MT, Kayhan AH, Ceylan H, Gurarslan G (2009) Hybridizing the harmony search algorithm with a spreadsheet ‘solver’ for solving continuous engineering optimization problems. Eng Optim 41(12):1119–1144CrossRef

Barton N (2000) TBM tunneling in jointed and fault rock. Balkema, Rotterdam

Bassimir B, Schmitt M, Wanka R (2020) Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves. Swarm Intell 14:285–311. https://doi.org/10.1007/s11721-020-00185-zCrossRef

Bamford WE (1984) Rock test indices are being successfully correlated with tunnel boring machine performance. In: Proceedings, fifth Australian tunneling conference, Sydney, pp 218–221

Bieniawski ZT, Celada B, Galera JM, Alvarez M (2006) Rock mass excavability indicator: new way to selecting the optimum tunnel construction method. Tunn Undergr Space Technol 21(3–4):237. https://doi.org/10.1016/j.tust.2005.12.016CrossRef

Bieniawski, Z.T., Celada, B., Galera, J.M., (2007) Predicting TBM Excavability Tunnels & Tunnelling International., September 2007, pp. 32–35

Brumberg-Chaumont J (2015) Universal logic and aristotelian logic: formality and essence of logic. Log Univers 9:253–278. https://doi.org/10.1007/s11787-015-0123-yCrossRef

Cassinelli F, Cina S, Innaurato N (1982) Power consumption and metal wear in tunnel boring machines, analysis of tunnel operation in hard rock. In: Proceedings, tunneling 82, London, IMM, pp 73–81

Cassinelli F, Cina S, Innaurato N (1983) Power consumption and metal wear in tunnel-boring machines: analysis of tunnel-boring operation in hard rock. Int J Rock Mech Min Sci Geomech Abstr 20(1):A25. https://doi.org/10.1016/0148-9062(83)91823-5CrossRef

Chawra VK, Gupta GP (2021) Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks. Peer-to-Peer Netw Appl 14:1303–1315. https://doi.org/10.1007/s12083-020-01052-8CrossRef

Dehghan AN, Shafiee SM, Rezaei F (2012) 3-D stability analysis and design of the primary support of Karaj metro tunnel: based on convergence data and back analysis algorithm. Eng Geol 141:141–149. https://doi.org/10.1016/j.enggeo.2012.05.008CrossRef

Dehghan AN (2020) Influence of Forepoling Umbrella on the settlements induced by shallow urban tunneling. Geotech Geol Eng 38:5005–5022CrossRef

Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunn Tunn Int 12(6):22–25

Font JM, Verdú V (1993) Algebraic logic for classical conjunction and disjunction. Stud Logica 52:181. https://doi.org/10.1007/BF01053070CrossRef

Ge Y, Wang J, Li K (2013) Prediction of hard rock TBM penetration rate using least square support vector machine. IFAC Proc Vol 46(13):347–352. https://doi.org/10.3182/20130708-3-CN-2036.00105CrossRef

Gholamnejad, JAVAD Tayarani, NARGES (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Mining Science and Technology (China) 20(5) 727–733. https://doi.org/10.1016/S1674-5264(09)60271-4CrossRef

Goetschel R, Voxman W (1986) Elementary fuzzy calculus. Fuzzy Sets Syst 18:31–43. https://doi.org/10.1016/0165-0114(86)90026-6CrossRef

Goker N, Dursun M (2019) A fuzzy scenario-based approach to analyzing neuromarketing technology evaluation factors. Soft Comput 23(23):12295–12304. https://doi.org/10.1007/s00500-019-03770-5CrossRef

Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46(1):8–18. https://doi.org/10.1016/j.ijrmms.2008.03.003CrossRef

Gu Y, Soutchanski MA (2010) description logic-based situation calculus. Ann Math Artif Intell 58:3–83. https://doi.org/10.1007/s10472-010-9176-zCrossRef

Hassanpour J, Rostami J, Khamehchiyan M, Bruland A (2009) Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel. Geomech Geoeng 4(4):287–297. https://doi.org/10.1080/17486020903174303CrossRef

Hassanpour J, Rostami J, Zhao J (2011) A new hard rock TBM performance prediction model for project planning. Tunn Undergr Space Technol 26(5):595–603. https://doi.org/10.1016/j.tust.2011.04.004CrossRef

Hintikka J (2012) Which mathematical Logic is the logic of mathematics? Log Univers 6:459–475. https://doi.org/10.1007/s11787-012-0065-6CrossRef

Innaurato N, Mancini R, Rondena E, Zaninetti A (1991) Forecasting and effective TBM performances in a rapid excavation of a tunnel in Italy. In: Seventh international congress ISRM, Aachen, pp 1009–1014

Khademi Hamidi J, Shahriar K, Rezai B, Rostami J (2010) Performance prediction of hard rock TBM using rock mass rating (RMR) system. Tunn Undergr Space Technol 25(4):333–345. https://doi.org/10.1016/j.tust.2010.01.008CrossRef

Kitsikoudis V, Spiliotis M, Hrissanthou V (2016) Fuzzy regression analysis for sediment incipient motion under turbulent flow conditions. Environ Process 3(3):663–679. https://doi.org/10.1007/s40710-016-0154-2CrossRef

Konovalov AY (2019) Absolute L-realizability and intuitionistic logic. Mosc Univ Math Bull 74:79–82. https://doi.org/10.3103/S0027132219020086CrossRef

Kooi B, Tamminga A (2013) Three-valued logics in modal Logic. Stud Logica 101:1061–1072. https://doi.org/10.1007/s11225-012-9420-0CrossRef

Lislerud A et al (1983) Hard rock tunnel boring. Project Rep 1–83, Univ. Trondheim, Norwegian Institute of Technology, Division Construction Engineering, 159

Mahdevari S, Shirzad Haghighat H, Torabi SR (2013) A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunn Undergr Space Technol 38:59–68. https://doi.org/10.1016/j.tust.2013.05.002CrossRef

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. https://doi.org/10.1016/j.ijrmms.2014.09.012CrossRef

Negoita C, Ralescu D (1975) Representation theorems for fuzzy concepts. Kybernetes 4(3):169–174. https://doi.org/10.1108/eb005392CrossRef

Pandit A, Biswal KC (2019) Prediction of earthquake magnitude using adaptive neuro fuzzy inference system. Earth Sci Inf 12(4):513–524. https://doi.org/10.1007/s12145-019-00397-wCrossRef

Papadopoulos B, Sirpi M (2004) Similarities and distances in fuzzy regression modeling. Soft Comput 8(8):556–561. https://doi.org/10.1007/s00500-003-0314-yCrossRef

Peters G (1994) Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst 1:45–55. https://doi.org/10.1016/0165-0114(94)90144-9CrossRef

Pietarinen A (2002) Quantum logic and quantum theory in a Game-theoretic perspective. Open Syst Inf Dyn 9:273–290. https://doi.org/10.1023/A:1019712730037CrossRef

Poonguzhali PK, Ananthamoorthy NP (2020) Design of mutated harmony search algorithm for data dissemination in wireless sensor network. Wireless Pers Commun 111(2):729–751CrossRef

Ratnaweera A, Halgamure SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255. https://doi.org/10.1109/TEVC.2004.826071CrossRef

Razmi J, Songhori MJ, Khakbaz MH (2009) An integrated fuzzy group decision making/fuzzy linear programming (FGDMLP) framework for supplier evaluation and order allocation. Int J Adv Manuf Technol 43:590. https://doi.org/10.1007/s00170-008-1719-7CrossRef

Ribacchi R, Lembo-Fazio A (2005) Influence of rock mass parameters on the performance of a TBM in a gneissic formation (Varzo Tunnel). Rock Mech Rock Eng 38(2):105–127. https://doi.org/10.1007/s00603-004-0032-5CrossRef

Saad E, Pontelli E (2007) A new approach to hybrid probabilistic logic programs. Ann Math Artif Intell 50:417–418. https://doi.org/10.1007/s10472-007-9082-1CrossRef

Sadeghi M, Pourhashemi SM, Dehghan AN, Ahangari K (2016) The effect of excavation progress on the behavior of Hakim Highway tunnel using geotechnical instrumentation. In: ITA-AITES world tunnel congress, pp. 22–28

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004CrossRef

Sfiris D, Papadopoulos B (2014) Non-asymptotic fuzzy estimators based on confidence intervals. Inf Sci 279:446–459. https://doi.org/10.1016/j.ins.2014.03.131CrossRef

Shakouri H, Nadimi R, Ghaderi SF (2017) Investigation on objective function and assessment rule in fuzzy regressions based on equality possibility, fuzzy union and intersection concepts. Comput Ind Eng 110:207–215. https://doi.org/10.1016/j.cie.2017.05.032CrossRef

Shirmohammadi M, Goushchi SJ, Keshtiban PM (2021) Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm. Prog Addit Manuf 6:199–215. https://doi.org/10.1007/s40964-021-00166-6CrossRef

Tanaka H (1987) Fuzzy data analysis by possibilistic linear models. Fuzzy Sets Syst 24:363–375. https://doi.org/10.1016/0165-0114(87)90033-9CrossRef

Tarkoy PJ (1973) Prediction TBM penetration rates in selected rock types. In: Proceedings, ninth canadian rock mechanics symposium, Montreal

Vergara IM, Saroglou C (2017) Prediction of TBM performance in mixed-face ground conditions. Tunn Undergr Space Technol 69:116–124. https://doi.org/10.1016/j.tust.2017.06.015CrossRef

Weyland D (2015) A critical analysis of the harmony search algorithm—how not to solve sudoku. Oper Res Perspect 2:97–105

Wheeler G (2008) Applied logic without psychologism. Stud Logica 88:137–156. https://doi.org/10.1007/s11225-008-9095-8CrossRef

Yabuuchi Y (2017) Possibility grades with vagueness in fuzzy regression models. Procedia Comput Sci 112:1470–1478. https://doi.org/10.1016/j.procs.2017.08.025CrossRef

Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23(3):326–339. https://doi.org/10.1016/j.tust.2007.04.011CrossRef

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(4–5):808–814. https://doi.org/10.1016/j.engappai.2009.03.007CrossRef

Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48(3):427–433. https://doi.org/10.1016/j.ijrmms.2011.02.013CrossRef

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. https://doi.org/10.1016/j.ijrmms.2015.09.019CrossRef

Yazdi Z, Rad AJ, Aghazadeh M, Afzal P (2019) Porphyry copper prospectivity mapping using fuzzy and fractal modeling in sonajeel area, NW Iran. Bull Miner Res Explor 158(158):235–250

Zare Naghadehi M, Samaei M, Ranjbarnia M, Nourani V (2018) State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming. Measurement 126:46–57. https://doi.org/10.1016/j.measurement.2018.05.049CrossRef

Zhao Y, Yang H, Chen Z, Chen X, Huang L, Liu S (2018) Effects of Jointed rock mass and mixed ground conditions on the cutting efficiency and cutter wear of tunnel boring machine. Rock Mech Rock Eng 52(5):1303–1313. https://doi.org/10.1007/s00603-018-1667-yCrossRef

Zhu Q, Tang X, Li Y, Yeboah MO (2020) An improved differential-based harmony search algorithm with linear dynamic domain. Knowl-Based Sys 187:104809CrossRef

Zimmermann E (2002) A Predicate logical extension of a subintuitionistic propositional logic. Studia Logica 72:401–410. https://doi.org/10.1023/A:1021897508223CrossRef

Title: Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm
Authors: Alireza Afradi
Arash Ebrahimabadi
Tahereh Hallajian
Publication date: 08-09-2021
Publisher: Springer International Publishing
Published in: Geotechnical and Geological Engineering / Issue 3/2022
Print ISSN: 0960-3182
Electronic ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-021-01982-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 3/2022

Study on Rockburst Proneness of Deep Tunnel Under Different Geo-Stress Conditions Based on DEM

Rock Type Identification Using Analysis of the Acoustic Signal Frequency Contents Propagated While Drilling Operation

Analysis of Faults' Effect on the Stability of Surface Lignite Mining Areas Using the Distinct Element Method

Frack Evolution and Destruction Characteristics of Mining Bottom Plate on Pressurized Water Based on Similar Simulation

Experimental study on bursting liability index of coal considering creep pre-damage

Construction of Structural Geological Model Using Monte Carlo Simulation