Top

Innovative Infrastructure Solutions

Published in:

01-12-2020 | Practice-oriented paper

A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine

Authors: Mohammad Ebdali, Emad Khorasani, Sohrab Salehin

Published in: Innovative Infrastructure Solutions | Issue 3/2020

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

search-config

AI-assisted search

Off

Abstract

In this paper, the relationships between engineering properties of travertine rock samples including uniaxial compressive strength, density, Brazilian tensile strength and compressional and shear wave velocities were evaluated. The Bukan travertine mine located in Iran was considered as case study here. Various data analysis approaches including simple regression method, multiple regression method and artificial neural network (ANN) have been used for finding optimum estimation model for uniaxial compression strength of travertine rocks. Rock sample preparations difficulties and conducting expensive tests such as UCS motivated many researchers to study different regression methods to estimate UCS from other rock mechanic tests. In this paper, different statistical methods as well as some ANN optimization algorithms that were used by several researchers are compared to find the optimum solution for UCS estimation problem of travertine rock samples. These optimization tools comprising genetic algorithm, particle swarm optimization and imperialist competitive algorithm were applied to improve the efficiency of ANN analysis. Finally, after comparing all of the presented methods, the best results were obtained by ANN-PSO algorithm.

previous article Effectiveness of Pongamia pinnata oil as rejuvenator for higher utilization of reclaimed asphalt (RAP) material

next article Evaluating the use of steel scrap, waste tiles, waste paving blocks and silica fume in flexural behavior of concrete

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

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

Available only for authorised users

Horsrud P (2001) Estimating mechanical properties of shale from empirical correlations. Soc Pet Eng. https://doi.org/10.2118/56017-PACrossRef

Kahraman S (2001) Evaluation of simple methods for assessing the uniaxial compressive strength rock. International Journal of Rock Mechanics and Mining Sciences 38(7):981–994. https://doi.org/10.1016/s1365-1609(01)00039-9CrossRef

Yasar E, Erdogan Y (2004) Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Int J Rock Mech Min Sci 41(5):871–875. https://doi.org/10.1016/j.ijrmms.2004.01.012CrossRef

Yaşar E, Erdoğan Y (2004) Estimation of rock physicomechanical properties using hardness methods. Eng Geol 71(3–4):281–288. https://doi.org/10.1016/s0013-7952(03)00141-8CrossRef

Chang C, Zoback MD, Khaksar A (2006) Empirical relations between rock strength and physical properties in sedimentary rocks. J Petrol Sci Eng 51(3–4):223–237. https://doi.org/10.1016/j.petrol.2006.01.003CrossRef

Çobanoğlu İ, Çelik SB (2008) Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull Eng Geol Env 67(4):491–498. https://doi.org/10.1007/s10064-008-0158-xCrossRef

Yagiz S (2008) Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer. Bull Eng Geol Env 68(1):55–63. https://doi.org/10.1007/s10064-008-0172-zCrossRef

Diamantis K, Gartzos E, Migiros G (2009) Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: test results and empirical relations. Eng Geol 108(3–4):199–207. https://doi.org/10.1016/j.enggeo.2009.07.002CrossRef

Moradian Z, Behnia M (2009) Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test. Int J Geomech 9(1):14–19CrossRef

10.

Minaeian B, Ahangari K (2011) Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab J Geosci 6(6):1925–1931. https://doi.org/10.1007/s12517-011-0460-yCrossRef

11.

Singh TN, Kainthola A, Venkatesh A (2011) Correlation between point load index and uniaxial compressive strength for different rock types. Rock Mech Rock Eng 45(2):259–264. https://doi.org/10.1007/s00603-011-0192-zCrossRef

12.

Bruno G, Vessia G, Bobbo L (2012) Statistical method for assessing the uniaxial compressive strength of carbonate rock by Schmidt Hammer tests performed on core samples. Rock Mech Rock Eng 46(1):199–206. https://doi.org/10.1007/s00603-012-0230-5CrossRef

13.

Azimian A, Ajalloeian R, Fatehi L (2013) An Empirical Correlation of Uniaxial Compressive Strength with P-wave Velocity and Point Load Strength Index on Marly Rocks Using Statistical Method. Geotech Geol Eng 32(1):205–214. https://doi.org/10.1007/s10706-013-9703-xCrossRef

14.

Jahed Armaghani D, Hajihassani M, Yazdani Bejarbaneh B, Marto A, Tonnizam Mohamad E (2014) Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network. Measurement 55:487–498. https://doi.org/10.1016/j.measurement.2014.06.001CrossRef

15.

Karaman K, Kesimal A, Ersoy H (2014) A comparative assessment of indirect methods for estimating the uniaxial compressive and tensile strength of rocks. Arab J Geosci 8(4):2393–2403. https://doi.org/10.1007/s12517-014-1384-0CrossRef

16.

Kaya A, Karaman K (2015) Utilizing the strength conversion factor in the estimation of uniaxial compressive strength from the point load index. Bull Eng Geol Env 75(1):341–357. https://doi.org/10.1007/s10064-015-0721-1CrossRef

17.

Madhubabu N, Singh PK, Kainthola A, Mahanta B, Tripathy A, Singh TN (2016) Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88:202–213. https://doi.org/10.1016/j.measurement.2016.03.050CrossRef

18.

Salehin S (2017) Investigation into engineering parameters of marls from Seydoon dam in Iran. Jo Rock Mech Geotech Eng 9(5):912–923CrossRef

19.

Salehin S, Hadavandi E, Chelgani SC (2020) Exploring relationships between mechanical properties of marl core samples by a coupling of mutual information and predictive ensemble model. Model Earth Syst Environ 6(1):575–583CrossRef

20.

Matin S, Farahzadi L, Makaremi S, Chelgani SC, Sattari G (2018) Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Appl Soft Comput 70:980–987CrossRef

21.

Zhang J, Li D, Wang Y (2020) Toward intelligent construction: prediction of mechanical properties of manufactured-sand concrete using tree-based models. J Clean Prod 258:120665CrossRef

22.

Rabbani E, Sharif F, Koolivand Salooki M (1997) Moradzadeh A (2012) Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Int J Rock Mech Min Sci 56:100–111CrossRef

23.

Manouchehrian A, Sharifzadeh M, Moghadam RH (2012) Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Min Sci Technol 22(2):229–236CrossRef

24.

Dehghan S, Sattari G, Chelgani SC, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol (China) 20(1):41–46CrossRef

25.

Yılmaz I, Yuksek A (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795CrossRef

26.

Mishra D, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54–68CrossRef

27.

Karakus M, Tutmez B (2006) Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock Mech Rock Eng 39(1):45–57CrossRef

28.

Singh R, Umrao RK, Ahmad M, Ansari M, Sharma L, Singh T (2017) Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 99:108–119CrossRef

29.

Sharma L, Vishal V, Singh T (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

30.

Motamedi S, Roy C, Shamshirband S, Hashim R, Petković D, Song K-I (2015) Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology. Ultrasonics 61:103–113CrossRef

31.

Kainthola A, Singh P, Verma D, Singh R, Sarkar K, Singh T (2015) Prediction of strength parameters of himalayan rocks: a statistical and ANFIS approach. Geotech Geol Eng 33(5):1255–1278CrossRef

32.

Singh R, Vishal V, Singh T, Ranjith P (2013) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506CrossRef

33.

Liu Z, Shao J, Xu W, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10(5):651–663CrossRef

34.

Beiki M, Majdi A (1997) Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169CrossRef

35.

Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Env 74(3):745–757CrossRef

36.

Momeni E, Armaghani DJ, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRef

37.

Armaghani DJ, Amin MFM, Yagiz S, Faradonbeh RS, Abdullah RA (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186CrossRef

38.

Kahraman S, Gunaydin O, Fener M (2005) The effect of porosity on the relation between uniaxial compressive strength and point load index. Int J Rock Mech Min Sci 42(4):584–589CrossRef

39.

Sharma PK, Singh TN (2007) A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Env 67(1):17–22. https://doi.org/10.1007/s10064-007-0109-yCrossRef

40.

Khandelwal M, Singh TN (2009) Correlating static properties of coal measures rocks with P-wave velocity. Int J Coal Geol 79(1–2):55–60. https://doi.org/10.1016/j.coal.2009.01.004CrossRef

41.

Sarkar K, Vishal V, Singh TN (2011) An empirical correlation of index geomechanical parameters with the compressional wave velocity. Geotech Geol Eng 30(2):469–479. https://doi.org/10.1007/s10706-011-9481-2CrossRef

42.

Khandelwal M (2012) Correlating P-wave velocity with the physico-mechanical properties of different rocks. Pure Appl Geophys 170(4):507–514. https://doi.org/10.1007/s00024-012-0556-7CrossRef

43.

Standard A (2008) D2845-08 Standard test method for laboratory determination of pulse velocities and ultrasonic elastic constants of rock. ASTM International, West Conshohocken

44.

ISRM X X (1979) ISRM suggested methods for determining water content, porosity, density, absorption and related properties and swelling and slake-durability index properties. Int J Rock Mechan Min Sci Geomechan Abstr 16(2):143–151CrossRef

45.

Brown E (1981) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Rock characterization, testing and monitoring—ISRM suggested methods. Pergamon Press, Oxford, pp 113–116

46.

Bieniawski Z, Bernede M (1979) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials: part 1. Suggested method for determining deformability of rock materials in uniaxial compression. Int J Rock Mech Min Sci Geomech 2:138–140CrossRef

47.

Carneiro F (1947) Une novelle methode d’essai pour determiner la Resistance à la traction du beton. Reunion dês Laboratoires d’Essai de Materiaux

48.

Akazawa T (1953) Tension test methods for concretes, international union of testing and research laboratories for-materials and structures (RILEM), Paris. Bulletin 16:11–23

49.

Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36(1):29–39CrossRef

50.

Lee S-C (2003) Prediction of concrete strength using artificial neural networks. Eng Struct 25(7):849–857. https://doi.org/10.1016/s0141-0296(03)00004-xCrossRef

51.

Yılmaz I, Yuksek AG (2007) An Example of Artificial Neural Network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795. https://doi.org/10.1007/s00603-007-0138-7CrossRef

52.

Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1–2):51–60CrossRef

53.

Singh R, Vishal V, Singh TN, Ranjith PG (2012) A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 23(2):499–506. https://doi.org/10.1007/s00521-012-0944-zCrossRef

54.

Simpson PK (1990) Artificial neural system—foundation, paradigm, application and implementations. Pergamon, New York

55.

Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using Artificial Neural Networks technique. J Sci Ind Res 63:32–38

56.

Haykin S, Network N (2004) A comprehensive foundation. Neural Netw 2(2004):41

57.

Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236CrossRef

58.

Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann

59.

Paola J (1994) Neural network classification of multispectral imagery. Master Tezi, The University of Arizona, USA

60.

Ripley BD (1993) Statistical aspects of neural networks. Networks and chaos—statistical and probabilistic aspects 50:40-123

61.

Wang C (1994) A theory of generalization in learning machines with neural network applications

62.

Demuth H, Beale M (1993) Neural network toolbox for use with matlab–User’S guide verion 3.0

63.

Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47(2):246–253. https://doi.org/10.1016/j.ijrmms.2009.09.011CrossRef

64.

Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 4661–4667

65.

Wang H, Pan J, Wang S, Zhu H (2015) Relationship between macro-fracture density, P-wave velocity, and permeability of coal. J Appl Geophys 117:111–117. https://doi.org/10.1016/j.jappgeo.2015.04.002CrossRef

66.

Teng J, Yao Y, Liu D, Cai Y (2015) Evaluation of coal texture distributions in the southern Qinshui basin, North China: investigation by a multiple geophysical logging method. Int J Coal Geol 140:9–22. https://doi.org/10.1016/j.coal.2014.12.014CrossRef

67.

Kassab MA, Weller A (2015) Study on P-wave and S-wave velocity in dry and wet sandstones of Tushka region, Egypt. Egypt J Petrol 24(1):1–11. https://doi.org/10.1016/j.ejpe.2015.02.001CrossRef

68.

El Sayed NA, Abuseda H, Kassab MA (2015) Acoustic wave velocity behavior for some Jurassic carbonate samples, north Sinai, Egypt. J Afr Earth Sci 111:14–25CrossRef

69.

Heidari M, Momeni AA, Naseri F (2013) New weathering classifications for granitic rocks based on geomechanical parameters. Eng Geol 166:65–73. https://doi.org/10.1016/j.enggeo.2013.08.007CrossRef

70.

Lee J-S, Yoon H-K (2017) Characterization of rock weathering using elastic waves: a Laboratory-scale experimental study. J Appl Geophys 140:24–33. https://doi.org/10.1016/j.jappgeo.2017.03.008CrossRef

Title: A comparative study of various hybrid neural networks and regression analysis to predict unconfined compressive strength of travertine
Authors: Mohammad Ebdali
Emad Khorasani
Sohrab Salehin
Publication date: 01-12-2020
Publisher: Springer International Publishing
Published in: Innovative Infrastructure Solutions / Issue 3/2020
Print ISSN: 2364-4176
Electronic ISSN: 2364-4184
DOI: https://doi.org/10.1007/s41062-020-00346-3

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"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2020

Numerical analysis and preliminary design of topside of an offshore platform using FGM and X52 steel under special loads

Strength characterization of concrete using industrial waste as cement replacing materials for rigid pavement

Performance of basalt fibre-reinforced concrete for pavement and flooring applications

Finite difference probabilistic slope stability analysis based on collocation-based stochastic response surface method (CSRSM)

Seismic finite element analysis of non-homogeneous embankment located in asymmetric valley

Effectiveness of Pongamia pinnata oil as rejuvenator for higher utilization of reclaimed asphalt (RAP) material