Top

Neural Computing and Applications

Published in:

03-02-2022 | Original Article

The prediction of dynamic energy behavior of a Brazilian disk containing nonpersistent joints subjected to drop hammer test utilizing heuristic approaches

Authors: Jamshid Shakeri, Mostafa Asadizadeh, Nima Babanouri

Published in: Neural Computing and Applications | Issue 12/2022

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

search-config

AI-assisted search

Off

Abstract

Crack development initiated from nonpersistent joints in rock mass plays a key role in the instability of rock structures. In particular, the dynamic behavior of nonpersistent discontinuities can result in the coalescence and failure of rock structures. The effect and contribution of such joint parameters on rock structures’ failure under impact loading have not been thoroughly investigated by researchers. In this paper, 68 concrete Brazilian disks, manufactured to include several nonpersistent joints and joint sets, are subjected to impact loading to explore the impact of joint continuity factor, joint spacing, bridge angle, and loading direction on the required dynamic energy for crack initiation (DECI) and coalescence (DECC). Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and its combination with particle swarm optimization (PSO) and genetic algorithm (GA) have been developed to predict the energy indexes. The performance of the models was evaluated using statistical indicators. The results show that intelligent methods can predict both energy indexes and that their outputs are consistent with laboratory results. The R-squared index for the test data of the ANN, ANFIS, and ANFIS-PSO/GA models to predict DECI parameters is 0.97, 0.96, 0.96, and 0.97, respectively, and for DECC is 0.98, 0.96, 0.96, and 0.93, respectively. The ANN have the best performance for the test data based on all statistical indexes. In addition, multiple parametric sensitivity analysis shows that both the joint continuity and joint spacing have the most significant effect while bridge angle and loading direction have the minimal effect on the energy indexes.

previous article A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process

next article DEIDS: a novel intrusion detection system for industrial control systems

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

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!

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

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

Zhang C, Cao P, Cao Y, Li J (2013) Using finite element software to simulation fracture behavior of three-point bending beam with initial crack. J Softw 8:1145–1150. https://doi.org/10.4304/jsw.8.5.1145-1150CrossRef

Yang X-X, Jing H-W, Tang C-A, Yang S-Q (2017) Effect of parallel joint interaction on the mechanical behavior of jointed rock mass models. Int J Rock Mech Min Sci 92:40–53. https://doi.org/10.1016/j.ijrmms.2016.12.010CrossRef

Fan X, Li K, Lai H et al (2018) Internal stress distribution and cracking around flaws and openings of rock block under uniaxial compression: a particle mechanics approach. Comput Geotech 102:28–38. https://doi.org/10.1016/j.compgeo.2018.06.002CrossRef

Aziznejad S, Esmaieli K, Hadjigeorgiou J, Labrie D (2018) Responses of jointed rock masses subjected to impact loading. J Rock Mech Geotech Eng 10:624–634. https://doi.org/10.1016/j.jrmge.2017.12.006CrossRef

Hatheway AW (2009) The complete ISRM suggested methods for rock characterization, testing and monitoring; 1974–2006. Environ Eng Geosci 15:47–48. https://doi.org/10.2113/gseegeosci.15.1.47CrossRef

Eberhardt E (2009) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Commission on testing methods. International Society for Rock Mechanics, 628 pp.,€ 25 (ISRM members),€ 30 (non-members)

Asadizadeh M, Masoumi H, Roshan H, Hedayat A (2019) Coupling Taguchi and response surface methodologies for the efficient characterization of jointed rocks’ mechanical properties. Rock Mech Rock Eng 52:4807–4819. https://doi.org/10.1007/s00603-019-01853-1CrossRef

Asadizadeh M, Moosavi M, Hossaini MF, Masoumi H (2018) Shear strength and cracking process of non-persistent jointed rocks: an extensive experimental investigation. Rock Mech Rock Eng 51:415–428. https://doi.org/10.1007/s00603-017-1328-6CrossRef

Bahaaddini M, Sharrock G, Hebblewhite BK (2013) Numerical direct shear tests to model the shear behaviour of rock joints. Comput Geotech 51:101–115. https://doi.org/10.1016/j.compgeo.2013.02.003CrossRef

10.

Karimi J, Asadizadeh M, Hossaini MF et al (2021) Compressive strength of flawed cylindrical specimens subjected to axial loading. Geomech Eng 1:87–99. https://doi.org/10.12989/gae2021.27.1.087CrossRef

11.

Hudson J, Harrison J, Popescu M (2002) Engineering rock mechanics: an introduction to the principles. Appl Mech Rev 55:B30. https://doi.org/10.1115/1.1451165CrossRef

12.

Zhang H, Elsworth D, Wan Z (2018) Failure response of composite rock-coal samples. Geomech Geophys Geo-Energy Geo-Resour 4:175–192. https://doi.org/10.1007/s40948-018-0082-xCrossRef

13.

Li K, Cheng Y, Yin Z-Y et al (2020) Size effects in a transversely isotropic rock under Brazilian tests: laboratory testing. Rock Mech Rock Eng 53:2623–2642. https://doi.org/10.1007/s00603-020-02058-7CrossRef

14.

Asadizadeh M, Hossaini MF, Moosavi M et al (2019) Mechanical characterisation of jointed rock-like material with non-persistent rough joints subjected to uniaxial compression. Eng Geol 260:105224. https://doi.org/10.1016/j.enggeo.2019.105224CrossRef

15.

Shakeri J, Asadizadeh M, Babanouri N et al (2022) The evolution of dynamic energy during drop hammer testing of Brazilian disk with non-persistent joints: an extensive experimental investigation. Theor Appl Fract Mech 117:103162. https://doi.org/10.1016/j.tafmec.2021.103162CrossRef

16.

Saksala T, Hokka M, Kuokkala VT, Mäkinen J (2013) Numerical modeling and experimentation of dynamic Brazilian disc test on Kuru granite. Int J Rock Mech Min Sci 59:128–138. https://doi.org/10.1016/j.ijrmms.2012.12.018CrossRef

17.

Li SH, Zhu WC, Niu LL et al (2018) Dynamic characteristics of green sandstone subjected to repetitive impact loading: phenomena and mechanisms. Rock Mech Rock Eng 51:1921–1936. https://doi.org/10.1007/s00603-018-1449-6CrossRef

18.

Fakhimi A, Azhdari P, Kimberley J (2018) Physical and numerical evaluation of rock strength in Split Hopkinson Pressure Bar testing. Comput Geotech 102:1–11. https://doi.org/10.1016/j.compgeo.2018.05.009CrossRef

19.

Tong LH, Yu Y, Lai SK, Lim CW (2019) Dynamic weakening of sandstone subjected to repetitive impact loading. Rock Mech Rock Eng 52:2197–2206. https://doi.org/10.1007/s00603-018-1713-9CrossRef

20.

Wang Y, Lin H, Zhao Y et al (2020) Analysis of fracturing characteristics of unconfined rock plate under edge-on impact loading. Eur J Environ Civ Eng 24:2453–2468. https://doi.org/10.1080/19648189.2018.1509021CrossRef

21.

Imani M, Nejati HR, Goshtasbi K (2017) Dynamic response and failure mechanism of Brazilian disk specimens at high strain rate. Soil Dyn Earthq Eng 100:261–269. https://doi.org/10.1016/j.soildyn.2017.06.007CrossRef

22.

Zhao Y, Zhao GF, Jiang Y (2013) Experimental and numerical modelling investigation on fracturing in coal under impact loads. Int J Fract 183:63–80. https://doi.org/10.1007/s10704-013-9876-6CrossRef

23.

Dai F, Xu Y, Zhao T et al (2016) Loading-rate-dependent progressive fracturing of cracked chevron-notched Brazilian disc specimens in split Hopkinson pressure bar tests. Int J Rock Mech Min Sci 88:49–60. https://doi.org/10.1016/j.ijrmms.2016.07.003CrossRef

24.

Zhao S, Zhang Q (2019) Dynamic crack propagation and fracture behavior of pre-cracked specimens under impact loading by split hopkinson pressure bar. Adv Mater Sci Eng 2019:1–11. https://doi.org/10.1155/2019/2383861CrossRef

25.

Tariq Z, Elkatatny S, Mahmoud M et al (2017) A new technique to develop rock strength correlation using artificial intelligence tools. In: Day 3 Wed, May 10, 2017. SPE, pp 1340–1353

26.

Pham BT, Nguyen MD, Bui K-TT et al (2019) A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. CATENA 173:302–311. https://doi.org/10.1016/j.catena.2018.10.004CrossRef

27.

Ashrafian A, Shokri F, Taheri Amiri MJ et al (2020) Compressive strength of Foamed Cellular Lightweight Concrete simulation: new development of hybrid artificial intelligence model. Constr Build Mater 230:117048. https://doi.org/10.1016/j.conbuildmat.2019.117048CrossRef

28.

Rezaei M, Asadizadeh M (2020) Predicting unconfined compressive strength of intact rock using new hybrid intelligent models. J Min Environ 11:231–246. https://doi.org/10.22044/jme.2019.8839.1774CrossRef

29.

Lawal AI, Kwon S (2021) Application of artificial intelligence to rock mechanics: an overview. J Rock Mech Geotech Eng 13:248–266. https://doi.org/10.1016/j.jrmge.2020.05.010CrossRef

30.

Jalal FE, Xu Y, Iqbal M et al (2021) Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP. J Environ Manag 289:112420. https://doi.org/10.1016/j.jenvman.2021.112420CrossRef

31.

Ngo HTT, Pham TA, Vu HLT, Van GL (2021) Application of artificial intelligence to determined unconfined compressive strength of cement-stabilized soil in Vietnam. Appl Sci 11:1949. https://doi.org/10.3390/app11041949CrossRef

32.

Asadizadeh M, Majdi A (2019) Developing new Adaptive Neuro-Fuzzy Inference Systems to predict granular soil groutability. Int J Min Geo-Eng 53:133–142. https://doi.org/10.22059/IJMGE.2018.255209.594728CrossRef

33.

Qi C, Tang X (2018) Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Comput Ind Eng 118:112–122. https://doi.org/10.1016/j.cie.2018.02.028CrossRef

34.

Shang Y, Nguyen H, Bui X-N et al (2020) A Novel Artificial Intelligence Approach To Predict Blast-Induced Ground Vibration In Open-Pit Mines Based On The Firefly Algorithm And Artificial Neural Network. Nat Resour Res 29:723–737. https://doi.org/10.1007/s11053-019-09503-7CrossRef

35.

Kardani N, Zhou A, Nazem M, Shen S-L (2021) Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J Rock Mech Geotech Eng 13:188–201. https://doi.org/10.1016/j.jrmge.2020.05.011CrossRef

36.

Mohammad R, Mostafa A, Abbas M, Mohammad Farouq H (2015) Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system. Int J Min Sci Technol 25:23–30. https://doi.org/10.1016/j.ijmst.2014.11.007CrossRef

37.

Asadizadeh M, Hossaini MF (2016) Predicting rock mass deformation modulus by artificial intelligence approach based on dilatometer tests. Arab J Geosci 9:96. https://doi.org/10.1007/s12517-015-2189-5CrossRef

38.

Zhang X, Nguyen H, Bui X-N et al (2020) Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunn Undergr Space Technol 103:103517. https://doi.org/10.1016/j.tust.2020.103517CrossRef

39.

Feng P, Dai F, Liu Y et al (2018) Effects of strain rate on the mechanical and fracturing behaviors of rock-like specimens containing two unparallel fissures under uniaxial compression. Soil Dyn Earthq Eng 110:195–211. https://doi.org/10.1016/j.soildyn.2018.03.026CrossRef

40.

Asadizadeh M, Rezaei M (2019) Surveying the mechanical response of non-persistent jointed slabs subjected to compressive axial loading utilising GEP approach. Int J Geotech Eng. https://doi.org/10.1080/19386362.2019.1596610CrossRef

41.

Nguyen HQ, Ly H-B, Tran VQ et al (2020) Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression. Materials (Basel) 13:1205. https://doi.org/10.3390/ma13051205CrossRef

42.

Asadizadeh M, Karimi J, Hossaini MF, Alipour A, Nowak S, Sherizadeh T (2022) The effect of central flaw on the unconfined strength of rock-like specimens: an intelligent approach. Iran J Sci Technol Trans Civil Eng. https://doi.org/10.1007/s40996-022-00825-wCrossRef

43.

Brady BHG, Brown ET (2004) Rock mechanics for underground mining. Springer Netherlands, Dordrecht

44.

Lu X, Hasanipanah M, Brindhadevi K et al (2020) ORELM: a novel machine learning approach for prediction of flyrock in mine blasting. Nat Resour Res 29:641–654. https://doi.org/10.1007/s11053-019-09532-2CrossRef

45.

Nikafshan Rad H, Bakhshayeshi I, Wan Jusoh WA et al (2020) Prediction of flyrock in mine blasting: a new computational intelligence approach. Nat Resour Res 29:609–623. https://doi.org/10.1007/s11053-019-09464-xCrossRef

46.

Shojaeian A, Asadizadeh M (2020) Prediction of surface tension of the binary mixtures containing ionic liquid using heuristic approaches; an input parameters investigation. J Mol Liq 298:111976. https://doi.org/10.1016/j.molliq.2019.111976CrossRef

47.

Asadizadeh M, Hossaini MF, Moosavi M, Mohammadi S (2016) A laboratory study on mix design to properly resemble a jointed brittle rock. Int J Min Geo-Eng 50:201–210. https://doi.org/10.22059/ijmge.2016.59830CrossRef

48.

Zhou J, Aghili N, Ghaleini EN et al (2020) A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Eng Comput 36:713–723. https://doi.org/10.1007/s00366-019-00726-zCrossRef

49.

Díaz-Rodríguez P, Cancilla JC, Matute G, Torrecilla JS (2015) Viscosity estimation of binary mixtures of ionic liquids through a multi-layer perceptron model. J Ind Eng Chem 21:1350–1353. https://doi.org/10.1016/j.jiec.2014.06.005CrossRef

50.

Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRef

51.

Moghaddamnia A, Ghafari Gousheh M, Piri J et al (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97. https://doi.org/10.1016/j.advwatres.2008.10.005CrossRef

52.

Jahanmiri S, Asadizadeh M, Alipour A et al (2021) Predicting the contribution of mining sector to the Gross Domestic Product (GDP) Index utilizing heuristic approaches. Appl Artif Intell. https://doi.org/10.1080/08839514.2021.1997225CrossRef

53.

Huang J, Asteris PG, Manafi Khajeh Pasha S et al (2020) A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Eng Comput. https://doi.org/10.1007/s00366-020-01207-4CrossRef

54.

Hajihassani M, Jahed Armaghani D, Monjezi M et al (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74:2799–2817. https://doi.org/10.1007/s12665-015-4274-1CrossRef

55.

Victoire TAA, Jeyakumar AE (2004) Hybrid PSO–SQP for economic dispatch with valve-point effect. Electr Power Syst Res 71:51–59. https://doi.org/10.1016/j.epsr.2003.12.017CrossRef

56.

Cheng G, An Y, Wang Z, Zhu K (2012) Oil well placement optimization using niche particle swarm optimization. In: 2012 Eighth international conference on computational intelligence and security. IEEE, pp 61–64

57.

Holland JH (1992) Adaptation in natural and artificial systems. The MIT PressCrossRef

58.

Martin A, Gayathri V, Saranya G et al (2011) A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy C-means and mars. Int J Soft Comput 2:12–24. https://doi.org/10.5121/ijsc.2011.2102CrossRef

59.

Höglund H (2017) Tax payment default prediction using genetic algorithm-based variable selection. Expert Syst Appl 88:368–375. https://doi.org/10.1016/j.eswa.2017.07.027CrossRef

60.

Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007CrossRef

61.

Haeri H, Sarfarazi V, Zhu Z (2016) Analysis of crack coalescence in concrete using neural networks. Strength Mater 48:850–861. https://doi.org/10.1007/s11223-017-9831-2CrossRef

62.

Correa JM, Farret FA, Popov VA, Simoes MG (2005) Sensitivity analysis of the modeling parameters used in simulation of proton exchange membrane fuel cells. IEEE Trans Energy Convers 20:211–218. https://doi.org/10.1109/TEC.2004.842382CrossRef

Title: The prediction of dynamic energy behavior of a Brazilian disk containing nonpersistent joints subjected to drop hammer test utilizing heuristic approaches
Authors: Jamshid Shakeri
Mostafa Asadizadeh
Nima Babanouri
Publication date: 03-02-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-06964-5

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"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 12/2022

Efficiency evaluation research of a regional water system based on a game cross-efficiency model

Optimal path planning for drones based on swarm intelligence algorithm

A novel self-organizing TS fuzzy neural network for furnace temperature prediction in MSWI process

Enhancing the detection ability of control charts in profile monitoring by adding RBF ensemble model

Research on enterprise knowledge service based on semantic reasoning and data fusion

An improved ensemble pruning for mammogram classification using modified Bees algorithm

Premium Partner