Abstract
In this study, pressuremeter modulus (Ep) and limit pressure (PL) were predicted using artificial intelligence methods through the available results of in situ and laboratory tests (the pressuremeter tests) obtained from the same place in Isfahan city metro line 2 in the east–west distance. In this regard, the results of pressuremeter experiments, standard penetration test, and downhole seismic geophysical test, as well as the results of laboratory tests such as grain size, density, and triaxial compression tests performed in fine-grained sediments (clay and silt), were used as training data. To predict the values of pressuremeter modulus and limit pressure for some other places close to the mentioned location, artificial neural network (ANN) and multi-adaptive neuro-fuzzy inference system (MANFIS) were trained using the available experimental data. It outperforms ANN predictive model where the values of R2, RMSE were 0.86% and 0.17, respectively. The values of correlation coefficient R2 and the root mean squares error (RMSE) in MANFIS model was equal to 0.94% and 0.05, respectively. Totally, the results implied that MANFIS model has the ability to provide more realistic outputs with higher accuracy compared to ANN. The most advantage of the method presented in this research is that since the cost of conducting in situ and lab tests is usually high, when the test values for some parts of a location are available, it is possible to estimate these values for a limited number of other close locations and/or in the range with a good accuracy without the need to perform extra tests.
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References
Ahmadi-Nedushan B (2012) Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models. Constr Build Mater 36:665–673. https://doi.org/10.1016/j.conbuildmat.2012.06.002
Aladag CH, Kayabasi A, Gokceoglu C (2013) Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models. Neural Comput Appl 23:333–339. https://doi.org/10.1007/s00521-012-0900-y
ASTM D4719-20 (2020) Standard test methods for prebored pressuremeter testing in soils. ASTM International, West Conshohocken, PA. i. https://doi.org/10.1520/D4719-07.2
Barzegari G, Nadiri AA, Javid H (2019) Prediction of maximum settlement in EPB mechanized twin tunneling using supervised combined artificial intelligence model. Adv Appl Geo 9:256–271. https://doi.org/10.22055/aag.2019.28287.1929
Basarir H, Tutluoglu L, Karpuz C (2014) Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Eng Geol 173:1–9. https://doi.org/10.1016/j.enggeo.2014.02.006
Briaud J-L (2019) The pressuremeter, 1st edn. Routledge, London
Cabalar AF, Cevik A (2009) Modelling damping ratio and shear modulus of sand-mica mixtures using neural networks. Eng Geol 104:31–40. https://doi.org/10.1016/j.enggeo.2008.08.005
Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33. https://doi.org/10.1016/j.compgeo.2011.09.008
Cabalar AF, Cevik A, Guzelbey IH (2010) Constitutive modeling of Leighton Buzzard Sands using genetic programming. Neural Comput Appl 19:657–665. https://doi.org/10.1007/s00521-009-0317-4
Carter M, Bentley SP (2016) Soil properties and their correlations. Wiley, Chichester, UK
Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput J 11:2587–2594. https://doi.org/10.1016/j.asoc.2010.10.008
Cheshomi A, Ghodrati M (2015) Estimating Menard pressuremeter modulus and limit pressure from SPT in silty sand and silty clay soils. A case study in Mashhad, Iran. Geomech Geoengin 10:194–202. https://doi.org/10.1080/17486025.2014.933894
da Fonseca AV, Silva SR, Cruz N (2010) Geotechnical characterization by in situ and lab tests to the back-analysis of a supported excavation in metro do porto. Geotech Geol Eng 28:251–264. https://doi.org/10.1007/s10706-008-9183-6
Daneshvar M, Asghari E, Ghanbari A, Shahbazi M (2010) Geotechnical and geological aspects of Amir Kabir tunnel of Tehran, pp 26–28
Edincliler A, Cabalar AF, Cagatay A, Cevik A (2012) Triaxial compression behavior of sand and tire wastes using neural networks. Neural Comput Appl 21:441–452. https://doi.org/10.1007/s00521-010-0430-4
Edincliler A, Cabalar AF, Cevik A (2013) Modelling dynamic behaviour of sand-waste tires mixtures using neural networks and neuro-fuzzy. Eur J Environ Civ Eng 17:720–741. https://doi.org/10.1080/19648189.2013.814552
Emami M, Yasrobi SS (2014) Modeling and interpretation of pressuremeter test results with artificial neural networks. Geotech Geol Eng 32:375–389. https://doi.org/10.1007/s10706-013-9720-9
Fattahi H (2016) Adaptive neuro fuzzy inference system based on fuzzy C-means clustering algorithm, a technique for estimation of TBM penetration rate. Int J Optim Civ Eng Int J Optim Civ Eng 6:159–171
Fawaz A, Hagechehade F, Farah E (2014) A study of the pressuremeter modulus and its comparison to the elastic modulus of soil. Study Civ Eng Archit 3:7–15
Ghorbani B, Arulrajah A, Narsilio G, Horpibulsuk S (2020) Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes. Transp Geotech 24:100365. https://doi.org/10.1016/j.trgeo.2020.100365
Guha Roy D, Singh TN (2020) Predicting deformational properties of Indian coal: soft computing and regression analysis approach. Meas J Int Meas Confed 149:106975. https://doi.org/10.1016/j.measurement.2019.106975
Hagan MT, Demuth HB, Jesús ODE (1966) Scientific immigrants in the United States. Endeavour 25:58. https://doi.org/10.1016/0160-9327(66)90069-X
Hajian A, Bayat M (2022) Prediction of maximum shear modulus (Gmax) of granular soil using empirical, neural network and adaptive neuro fuzzy, inference system models. Geomech Eng 31(3):291–304. https://doi.org/10.12989/gae.2022.31.3.291
Hajian A, Styles P (2018) Application of soft computing and intelligent methods in geophysics. Springer International Publishing, Cham
Hajian A, Styles P, Zomorrodian H (2011) Depth estimation of cavities from microgravity data through multi adaptive neuro fuzzy interference system. In: Near Surf 2011 - 17th Eur Meet Environ Eng Geophys, pp 12–14. https://doi.org/10.3997/2214-4609.20144374
Horikawa Sichi, Furuhashi T, Okuma S, Uchikawa Y (1990) Composition methods of fuzzy neural networks. IECON Proceedings (Industrial Electronics Conference). pp. 1253–1258.
Ilkhchi AK, Rezaee M, Moallemi SA (2006) A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field. J Geophys Eng 3:356–369. https://doi.org/10.1088/1742-2132/3/4/007
Jahed Armaghani D, Tonnizam Mohamad E, Momeni E et al (2015) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ 74:1301–1319. https://doi.org/10.1007/s10064-014-0687-4
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 Manage 289:112420. https://doi.org/10.1016/j.jenvman.2021.112420
Jang J-SR (1993) ANFIS architecture. IEEE Trans. Syst. Man Cybern. 23:665–685
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. IEEE Trans Automat Contr 42:1482–1484. https://doi.org/10.1109/TAC.1997.633847
Kanungo DP, Sharma S, Pain A (2014) Artificial neural network (ANN) and regression tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters. Front Earth Sci 8:439–456. https://doi.org/10.1007/s11707-014-0416-0
Kayabasi A (2012) Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey. Environ Earth Sci 66:2171–2183. https://doi.org/10.1007/s12665-011-1439-4
Kimiaefar R. Siahkoohi HR, Hajian A., Kalhor A.,(2018) Random noise attenuation by Wiener-ANFIS filtering, J App Geophy,159,453-459.
Mobarra M, Hajian A, Rahgozar M (2013) Application of artificial neural networks to the prediction of TBM penetration rate in TBM-driven Golab water transfer tunnel. In: International Conference on Civil Engineering Architecture & Urban Sustainable Development 27&28 November 2013, Tabriz, Iran
Nadiri AA, Chitsazan N, Tsai FT-C, Moghaddam AA (2014) Bayesian artificial intelligence model averaging for hydraulic conductivity estimation. J Hydrol Eng 19:520–532. https://doi.org/10.1061/(asce)he.1943-5584.0000824
Nalbant M, Gokkaya H, Toktaş İ (2007) Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning. Model Simul Eng 2007:1–14. https://doi.org/10.1155/2007/92717
Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22:5054–5066. https://doi.org/10.1002/hyp.7129
Sarmadian F, Keshavarzi A (2010) Developing pedotransfer functions for estimating some soil properties using artificial neural network and multivariate regression approaches. World Acad Sci Eng Technol 72:501–507
Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech J 36:49–62
Sharma LK, Singh R, Umrao RK et al (2017a) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33:497–507. https://doi.org/10.1007/s00366-016-0486-6
Sharma LK, Vishal V, Singh TN (2017b) Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties. Meas J Int Meas Confed 102:158–169. https://doi.org/10.1016/j.measurement.2017.01.043
Sihag P, Tiwari NK, Ranjan S (2019) Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS). ISH J Hydraul Eng 25:132–142. https://doi.org/10.1080/09715010.2017.1381861
Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. IFAC Proc 16:55–60. https://doi.org/10.1016/S1474-6670(17)62005-6
Taşan S, Demir Y (2020) Comparative analysis of MLR, ANN, and ANFIS models for prediction of field capacity and permanent wilting point for Bafra plain soils. Commun Soil Sci Plant Anal 51:604–621. https://doi.org/10.1080/00103624.2020.1729374
Tayfur G, Nadiri AA, Moghaddam AA (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28:1173–1184. https://doi.org/10.1007/s11269-014-0553-y
Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231. https://doi.org/10.1016/S0895-4356(96)00002-9
Umrao RK, Sharma LK, Singh R, Singh TN (2018) Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: an ANFIS predictive technique. Meas J Int Meas Confed 126:194–201. https://doi.org/10.1016/j.measurement.2018.05.064
Wu M, Congress SSC, Liu L et al (2021) Prediction of limit pressure and pressuremeter modulus using artificial neural network analysis based on CPTU data. Arab J Geosci 14. https://doi.org/10.1007/s12517-020-06324-4
Yang ZR, Yang Z (2014) Artificial neural networks. Compr Biomed Phys 6:1–17. https://doi.org/10.1016/B978-0-444-53632-7.01101-1
Yesiloglu-Gultekin N (2021) Performance prediction modeling of standard penetration blow count of clayey soils by two non-linear tools. Arab J Geosci 14. https://doi.org/10.1007/s12517-021-06649-8
Yusefzadeh S, Nadiri AA (2021) Estimation hydraulic conductivity via intelligent models using geophysical data. Adv App Geo 11:382–404. https://doi.org/10.22055/AAG.2020.29223.1970
Zaki MFM, Ismail MAM, Govindasamy D, Leong FCP (2020) Prediction of pressuremeter modulus (E M) using GMDH neural network: a case study of Kenny Hill Formation. Arab J Geosci 13. https://doi.org/10.1007/s12517-020-05336-4
Zhang W, Zhang R, Wu C et al (2020) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11:1095–1106. https://doi.org/10.1016/j.gsf.2019.12.003
Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: IECON Proceedings (Industrial Electronics Conference), pp 229–234
Ziaie Moayed R, Kordnaeij A, Mola-Abasi H (2018) Pressuremeter modulus and limit pressure of clayey soils using GMDH-type neural network and genetic algorithms. Geotech Geol Eng 36:165–178. https://doi.org/10.1007/s10706-017-0314-9
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Asieh Alidousti: conceptualization, methodology, visualization, investigation, and writing—reviewing and editing. Rassoul Ajalloeian: investigation, resources, supervision, and reviewing and editing. Alireza Hajian: methodology, validation, supervision, and reviewing and editing.
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Alidousti Shahraki, A., Ajalloeian, R. & Hajian, A. ANN and MANFIS to predict pressuremeter modulus and limit pressure, case study: Isfahan metro line 2. Arab J Geosci 16, 104 (2023). https://doi.org/10.1007/s12517-022-11170-7
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DOI: https://doi.org/10.1007/s12517-022-11170-7