Tytuł artykułu
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Wave velocity is used to determine rock material, porosity, degree of petrification, fluid type, and mechanical and behavioral properties. In this study, after assessing the relationship between the static elastic modulus (Es) and the dynamic elastic modulus (Ed), various models using statistical and intelligent methods were presented for predicting shear wave velocity (Vs) and compressional wave velocity (Vp) based on porosity (P), Brazilian tensile strength (BTS), density (D), point load index (PLI), and water absorption (A) of sedimentary rocks. The Vp and Vs were estimated using simple and multiple regression, back-propagation artificial neural network (BPANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) methods. The examination of necessary assumptions of the models such as analysis of variance (ANOVA), variance inflation factor (VIF), mean absolute percentage error (MAPE), root-mean-square error (RMSE), variance accounted for (VAF), and independence of errors showed the high accuracy of the obtained model using multiple linear regression. The SVR approach using the radial basis kernel function with R2=100% and 99% showed the best accuracy in estimating Vs and Vp, respectively. The average ratio of Ed/Es, dynamic-to-static Poisson ratio ( νd∕νs ) , and Vp/Vs were obtained as 2.52, 2.92, and 2.82, respectively. The most accurate relationship between Ed and Es was developed in the form of a power function with R2=0.88.
Wydawca
Czasopismo
Rocznik
Tom
Strony
649--670
Opis fizyczny
Bibliogr. 139 poz.
Twórcy
autor
- Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi’an 710123, China
autor
- Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi’an 710123, China
- State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
- Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
autor
- Department of Civil Engineering, Engineering Faculty of Khoy, Urmia University of Technology, Urmia, Iran
autor
- School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
autor
- Structural Engineering Department, Structural Engineering and Construction Management, Future University in Egypt, New Cairo 11845, Egypt
Bibliografia
- 1. ASTM (1996) Standard test method for splitting tensile strength of intact rock core specimens. Annual book of ASTM standards, D3967: 03.01and 14.02
- 2. Abraham A (2005) Artificial neural networks. In: Sydenham PH, Thorn R (eds) Handbook of measuring system design. Wiley, Stillwater, pp 901–908
- 3. Adegbuyi O, Ogunyele AC, Akinyemi OM (2018) Petrology and geochemistry of basement GneissicRocks around Oka-Akoko, Southwestern Nigeria. MJG 2(2):11–16
- 4. Alam Z, Sun L, Zhang C, Samali B (2022) Influence of seismic orientation on the statistical distribution of nonlinear seismic response of the stiffness-eccentric structure. Structures 39:387–404. https://doi.org/10.1016/j.istruc.2022.03.042
- 5. Alizadeh SM, Iraji A, Tabasi S, Ahmed AAA, Motahari MR (2022) Estimation of dynamic properties of sandstones based on index properties using artificial neural network and multivariate linear regression methods. Acta Geophys 2:1–18. https://doi.org/10.1007/s11600-021-00705-3
- 6. Ameen MS, Smart BGD, Somerville JMC, Hammilton S, Naji NA (2009) Prediction rock mechanical properties of carbonated from wireline logs (A case study: Arab-D reservoir, Ghavar field, Saudi Arabia). Int J Rock Mech Min 26:430–444
- 7. Ansari Y, Hashemi A (2017) Neural network approach in assessment of fiber concrete impact trength. J Civ Eng Mater Appl 1(3):88–97
- 8. Aqil M, Kita I, Yano A, Nishiyama S (2007) Analysis and prediction of flow from local source in a river basin using a Neuro– fuzzy modeling tool. JEM 85:215–223
- 9. ASTM (1983) Test methods for ultra violet velocities determination. Designation D2845
- 10. ASTM (2002) Standard test method for determination of the point load strength index of rock. ASTM International, West Conshohocken D5731
- 11. Bai B, Wang Y, Rao D, Bai F (2022) The effective thermal conductivity of unsaturated porous media deduced by pore-scale SPH simulation. Front Earth Sci. https://doi.org/10.3389/feart.2022.943853
- 12. Behnamnia A, Barati M (2019) Seismic behavior of steel-concrete composite columns under cyclic lateral loading. J Civ Eng Mater Appl 3(4):183–192
- 13. Behnia D, Ahangari K, Moeinossadat SR (2017) Modeling of shear wave velocity in limestone by soft computing methods. Int J Min Sci Technol 27(3):423–430
- 14. Bell FG (1978) Petrographical factors relating to porosity and permeability in the Fell Sandstone. Q J Eng Geol Hydrogeol 11(2):113–126
- 15. Brotons V, Tomás R, Ivorra S, Grediaga A (2014) Relationship between static and dynamic elastic modulus of calcarenite heated at different temperatures: the San Julián’s stone. Bull Eng Geol Environ 73(3):791–799
- 16. Brotons V, Tomás R, Ivorra S, Grediaga A, Martínez-Martínez J, Benavente D, Gómez-Heras M (2016) Improved correlation between the static and dynamic elastic modulus of different types of rocks. Mater Struct 49(8):3021–3037
- 17. Castagna JP, Batzle ML, Kan TK (1993) Rock physics- the link between rock properties and AVO response. In: Castagna JP, Backus MM (eds) Offset-dependent reflectivity-theory and practice of AVO analysis: Society of Exploration Geophysicists, pp 135–171
- 18. Chao L, Zhang K, Wang J, Feng J, Zhang M (2021) A comprehensive evaluation of five evapotranspiration datasets based on ground and grace satellite observations: implications for improvement of evapotranspiration retrieval algorithm. Remote Sens 13(12):2414
- 19. Chua LHC, Wong TSW (2010) Improving event-based rainfall–runoff modeling using a combined artificial neural network–kinematic wave approach. J Hydrol 390(1):92–107
- 20. Daraei A, Zare SA (2019) Model between dynamic and static moduli of limestone in Asmari geological formation based on laboratory and in-situ tests. JEG 12(4):617–634
- 21. Davarpanah SM, Ván P, Vásárhelyi B (2020) Investigation of the relationship between dynamic and static deformation moduli of rocks. Geomech Geophys Geo-Energy Geo-Resour 6(1):1–14
- 22. Dong J, Deng R, Quanying Z, Cai J, Ding Y, Li M (2021) Research on recognition of gas saturation in sandstone reservoir based on capture mode. Appl Radiat Isot 178:109939. https://doi.org/10.1016/j.apradiso.2021.109939
- 23. Dorfan L, Mousavi Haghighi MH, Mousavi SN (2020) Optimized decision-making for shrimp fishery in Dayyer Port using the goal programing model. CJES 18(4):367–381
- 24. Du K, Li X, Su R, Tao M, Lv S, Luo J, Zhou J (2022) Shape ratio effects on the mechanical characteristics of rectangular prism rocks and isolated pillars under uniaxial compression. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2022.01.004
- 25. Dunham RJ (1962) Classification of carbonate rocks according to depositional textures. Science 2:108–121
- 26. Edet A (2018) Correlation between physico-mechanical parameters and geotechnical evaluations of some sandstones along the Calabar/Odukpani–Ikom–Ogoja Highway Transect, Southeastern Nigeria. Geotech Geol Eng 36(1):135–149. https://doi.org/10.1007/s10706-017-0311-z
- 27. Fallah M, Pirali Zefrehei AR, Hedayati SA, Bagheri T (2021) Comparison of temporal and spatial patterns of water quality parameters in Anzali Wetland (southwest of the Caspian Sea) using Support vector machine model. CJES 19(1):95–104
- 28. Fan C, Li H, Qin Q, He S, Zhong C (2020) Geological conditions and exploration potential of shale gas reservoir in Wufeng and Longmaxi Formation of southeastern Sichuan Basin, China. J Pet Sci Eng 191:107138. https://doi.org/10.1016/j.petrol.2020.107138
- 29. Fathi M, Bidehandi N (2010) Investigation of the effect of frequency on the scattering velocity of elastic waves in limestone samples in dry and saturated states. In: Twenty-sixth earth science conference, geological survey and mineral explorations of Iran (in Persian).
- 30. Fattahi H, Ilghani NZ (2021) Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study. Environ Earth Sci 80(1):1–10
- 31. Fei W, Huiyuan B, Jun Y, Yonghao Z (2016) Correlation of dynamic and static elastic parameters of rock. Electron J Geotech Eng 21:1551–1560
- 32. Folk RL (1974) Petrology of sedimentary rocks, Hemphill, Austin, p 600
- 33. Ghadimi H, Ebrahimian H (2015) MLP based islanding detection using histogram analysis for wind turbine distributed generation. UJRSET 3(3):16–26
- 34. Ghafoori M, Rastegarnia A, Lashkaripour GR (2018) Estimation of static parameters based on dynamical and physical properties in limestone rocks. J African Earth Sci 137:22–31
- 35. Ghandehari S (2012) Geomechanical charactrization of a hydrocarbon reservoir using well data to evaluate and design of hydraulic fracture initiation and propagation, case study: one of the Iranian Offshore Oil Company wells. (In Persian)
- 36. Ghavami S, Rajabi M (2021) Investigating the influence of the combination of cement Kiln dust and fly ash on compaction and strength characteristics of high-plasticity clays. JCEMA 5(1):9–16
- 37. Ghobadi MH, Heidari M, Rafiei B, Mousavi SD (2013) Investigation of the relationship between mineralogical and physical properties of sandstones with their tensile strength, the first national conference on geotechnical engineering, Article COI Code: GEOTEC01_371 (In Persian)
- 38. Gholami S, Vafakhah M, Ghaderi K, Javadi MR (2020) Simulation of rainfall-runoff process using geomorphology-based adaptive neuro-fuzzy inference system (ANFIS). CJES 18(2):109–122
- 39. Gholami V, Darvari Z, Mohseni Saravi M (2015) Artificial neural network technique for rainfall temporal distribu-tion simulation (case study: Kechik region). Casp J Environ Sci 13(1):53–60
- 40. Golmohammadi AM, Tavakkoli-Moghaddam R, Jolai F, Golmohammadi AH (2014) Concurrent cell formation and layout design using a genetic algorithm under dynamic conditions. UCT J Resea Sci Eng Technol 2(1):8–15
- 41. Goodman RE (1989) Introduction to rock mechanics. Wiley, New York
- 42. Hassanzadeh R, Beiranvand B, Komasi M, Hassanzadeh A (2021) Investigation of data mining method in optimal operation of Eyvashan earth dam reservoir based on PSO algorithm. J Civ Eng Mater Appl 5(3):125–137
- 43. Huang H, Guo M, Zhang W, Huang M (2022) Seismic behavior of strengthened RC columns under combined loadings. J Bridge Eng 27(6):1140. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871
- 44. Idrisovich Ismagilov I, Ayratovich Murtazin A, Vladimirovna Kataseva D, Sergeevich Katasev A, Olegovna Barinova A (2020) Formation of a knowledge base to analyze the issue of transport and the environment. CJES 18(5):615–621
- 45. ISRM (1981) Rock characterization testing and monitoring. In: Brown, E.T. (Ed.), ISRM Suggested Methods. Pergamon Press, Oxford
- 46. Jalili A, Firouz MH, Ghadimi N (2015) Firefly Algorithm based on Fuzzy Mechanism for Optimal Congestion Management. UJRSET 3(3):1–7
- 47. Jian Xu, Zhou L, Ke Hu, Li Y, Zhou X, Wang S (2022) Influence of wet-dry cycles on uniaxial compression behavior of fissured loess disturbed by vibratory loads. KSCE J Civ Eng. https://doi.org/10.1007/s12205-022-1593-0
- 48. Kalteh AM (2008) Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding. Caspian J Env Sci 6(1):53–58
- 49. Kavyanifar B, Tavakoli B, Torkaman J, Mohammad Taheri A, Ahmadi Orkomi A (2020) Coastal solid waste prediction by applying machine learning approaches (case study: Noor, Mazandaran Province, Iran). Casp J Environ Sci 18(3):227–236
- 50. Keykhah H, Dahan ZB (2018) Stability analysis of upstream slope of earthen dams using the finite element method against sudden change in the water surface of the reservoir, case study: Ilam earthen dam in Ilam Province. J Civ Eng Mater Appl 2(1):24–30
- 51. Kookalani S, Cheng B (2021) Structural analysis of GFRP elastic gridshell structures by particle swarm optimization and least square support vector machine algorithms. J Mater Civ Eng 8:12–23
- 52. Lacy LL (1997) Dynamic rock mechanics testing for optimized fracture designs. In: SPE Paper 38716
- 53. Lan Z, Zhao Y, Zhang J, Jiao R, Khan MN, Sial TA, Si B (2021) Long-term vegetation restoration increases deep soil carbon storage in the Northern Loess Plateau. Sci Rep. https://doi.org/10.1038/s41598-021-93157-0
- 54. Lerman N, Aronofsky L, Aghili B (2021) Investigating the microstructure and mechanical properties of metakaolin-based polypropylene fiber-reinforced geopolymer concrete using different monomer ratios. J Civ Eng Mater Appl 5(3):115–123
- 55. Li X, Li X, Wang Y, Hu Y, Zhou C, Zhang H (2022) Numerical investigation on stratum and surface deformation in underground phosphorite mining under different mining methods. Front Earth Sci 10:1–14. https://doi.org/10.3389/feart.2022.831856
- 56. Liu B, Spiekermann R, Zhao C, Püttmann W, Sun Y, Jasper A, Uhl D (2022) Evidence for the repeated occurrence of wildfires in an upper Pliocene lignite deposit from Yunnan, SW China. Int J Coal Geol 250:103924. https://doi.org/10.1016/j.coal.2021.103924
- 57. Liu B, Yang H, Karekal S (2020) Effect of water content on argillization of mudstone during the tunnelling process. Rock Mech Rock Eng 53(2):799–813
- 58. Liu H, Shi Z, Li J, Liu C, Meng X, Du Y, Chen J (2021a) Detection of road cavities in urban cities by 3D ground-penetrating radar. Geophysics 86(3):A25–A33
- 59. Liu Y, Zhang Z, Liu X, Wang L, Xia X (2021b) Ore image classification based on small deep learning model: evaluation and optimization of model depth, model structure and data size. Miner Eng 172:107020
- 60. Liu Y, Zhang Z, Liu X, Wang L, Xia X (2021c) Efficient image segmentation based on deep learning for mineral image classification. Adv Powder Technol 32(10):3885–3903
- 61. Mahdavi A, Niknejad M, Karami O (2015) A fuzzy multi-criteria decision method for ecotourism development locating. CJES 13(3):221–236
- 62. Maleki MA, Emami M (2019) Application of SVM for investigation of factors affecting compressive strength and consistency of geopolymer concretes. J Mater Civ Eng J Ma 3(2):101–107. https://doi.org/10.22034/JCEMA.2019.92507
- 63. Mehrgini B, Izadi H, Memarian H (2019) Shear wave velocity prediction using Elman artificial neural network. Carbonates Evaporites 34(4):1281–1291
- 64. Miah MI (2021) Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data. J Rock Mech Geotech Eng 13(6):1466–1477
- 65. Mikaeil R, Esmaeilzade A, Shaffiee Haghshenas S (2021) Investigation of the relationship between Schimazek’s F-abrasiveness factor and current consumption in rock cutting process. J Civ Eng Mater Appl 5(2):47–55
- 66. Mokhberi M, Khademi H (2017) The use of stone columns to reduce the settlement of swelling soil using numerical modeling. JCEMA 1(2):45–60
- 67. Moosavi N, Bagheri M, Nabi-Bidhendi M, Heidari R (2021) Fuzzy support vector regression for permeability estimation of petroleum reservoir using well logs. Acta Geophys 70:161–172
- 68. Moshahedi A, Mehranfar N (2021) A comprehensive design for a manufacturing system using predictive fuzzy models. JRSET 9(03):1–23
- 69. Mousavi Davoudi SA, Naghipour M (2019) Studying the buckling behavior of composite columns (CFST) by cyclic loading. JCEMA 3(4):203–213
- 70. Najibi A, Mohammadreza A, Ajal Louian R, Gholam Abbas S (2011) Estimation of mechanical properties of limestone using petrophysical data. J Eng Geol 5(1) (in Persian)
- 71. Naseri F, Lotfollahi S, Bagherzadeh Khalkhali A (2017) Dynamic mechanical behavior of rock materials. JCEMA 1(2):39–44
- 72. Olayiwola T, Sanuade OA (2021) A data-driven approach to predict compressional and shear wave velocities in reservoir rocks. Petroleum 7(2):199–208
- 73. Onaloa D, Oloruntobi O, Adedigba S, Khan F, James L, Butt S (2018) Static Young’s modulus model prediction for formation evaluation. J Pet Sci Eng 171:394–402
- 74. Parvizi SR, Kharrat MR, Asef B, Jahangiry Hashemi A (2015) Prediction of the shear wave velocity from compressional wave velocity for Gachsaran Formation. Acta Geophys 63(5):1231–1243
- 75. Patil PL, Dasog GS, Yerimani SA, Kuligod VB, Hebbara M, Hundekar ST (2020) Morphometric analysis of landforms on basalt, granite gneiss and schist geological formations in north Karnataka, India–a comparison. Geol Ecol Landsc 4(4):288–297
- 76. Pearson K (1895) Correlation coefficient. In: Royal society proceedings, Vol 58, p 214
- 77. Pickett GR (1963) Acoustic character logs and their applications in formation evaluation. JPT 15:650–667
- 78. Plona TJ, Cook JM (1995) Effects of stress cycles on static and dynamic Young's moduli in Castlegate sandstone. In: The 35th US symposium on rock mechanics (USRMS). OnePetro
- 79. Rahimi E, Teshnizi ES, Rastegarnia A, Al-shariati EM (2019) Cement take estimation using neural networks and statistical analysis in Bakhtiari and Karun 4 dam sites, in south west of Iran. Bull Eng Geol Environ 78(4):2817–2834
- 80. Rajabi M, Bohloli B, Ahangar EG (2010) Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: a case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran). Comput Geosci 36(5):647–664
- 81. Rashidi Tazhan O, Pir Bavaghar M, Ghazanfari H (2019) Detecting pollarded stands in Northern Zagros forests, using artificial neural network classifier on multi-temporal lansat-8 (OLI) imageries (case study: Armarde, Baneh). CJES 17(1):83–96
- 82. Rastegarnia A, Lashkaripour GR, Sharifi Teshnizi E, Ghafoori M (2021) Evaluation of engineering characteristics and estimation of dynamic properties of clay-bearing rocks. Environ Earth Sci 80(18):1–24
- 83. Rastegarnia A, Teshnizi ES, Hosseini S, Shamsi H, Etemadifar M (2018) Estimation of punch strength index and static properties of sedimentary rocks using neural networks in south west of Iran. Measurement 128:464–478
- 84. Roslee R, Pirah JA, Zikiri MF, Madri AN (2020) Applicability of the rock mass rating (RMR) system for the Trusmadi formation at Sabah, Malaysia. MJG 4(2):96–102
- 85. Roslee R, Tongkul F (2018) Engineering geological assessment (EGA) on slopes along the Penampang to Tambunan Road, Sabah, Malaysia. MJG 2(1):06–14
- 86. Rustamovich Sultanbekov I, Yurievna Myshkina I, Yurievna Gruditsyna L (2020) Development of an application for creation and learning of neural networks to utilize in environmental sciences. CJES 18(5):595–601
- 87. Saghi H, Behdani M, Saghi R, Ghaffari AR, Hirdaris S (2019) Application of gene expression programming model to present a new model for bond strength of fiber reinforced polymer and concrete. JCEMA 3(1):15–29
- 88. Sajil Kumar PJ, Mohanan AA, Ekanthalu VS (2020) Hydrogeochemical analysis of Groundwater in Thanjavur district, Tamil Nadu; Influences of Geological settings and land use pattern. Geol Ecol Landsc 4(4):306–317
- 89. Salehi M, Ajallouian R, Hashemi M (2011) Comparison of modulus of dynamic and static elasticity of Bazaft Dam Stones. In: 4th National Geological Conference, Payame Noor University of Mashhad (in Persian)
- 90. Samuel AO, Emmanuel A (2021) Mineralization characterization of psammitic rocks in Efon-Alaaye and environs using remote sensing and field studies. AREES 8(1):48–61
- 91. Sanaei F, Kazemi MAA, Ahmadi H (2015) Designing and implementing fuzzy expert system for diagnosis of psoriasis. JRSET 3(02):41–49
- 92. Seyfi R (2017) Application of artificial neural network in modeling separation of microalgae. UJRSET 5(04):43–49
- 93. Shafiei Nikabadi M, Azimi A (2015) Demand forecasting in a supply chain using machine learning algorithms. CMES 13(41):127–136
- 94. Shamsashtiany R, Ameri M (2018) Road accidents prediction with multilayer perceptron MLP modelling case study: roads of Qazvin, Zanjan and Hamadan. JCEMA 2(4):181–192
- 95. Sharifi A, Amini J, Pourshakouri F (2016) Development of an allometric model to estimate above-ground biomass of forests using MLPNN algorithm, case study: hyrcanian forests of Iran. CJES 14(2):125–137
- 96. Sharifi J, Nooraiepour M, Mondol NH (2020) December. Application of the Analysis of Variance for Converting Dynamic to Static Young’s Modulus. In: 82nd EAGE annual conference and exhibition, vol 2020, no 1, pp 1–5
- 97. Siddig O, Gamal H, Elkatatny S, Abdulraheem A (2021) Applying different artificial intelligence techniques in dynamic Poisson’s ratio prediction using drilling parameters. J Energy Resour Technol 144(7):073006
- 98. Sobhani J, Jafarpour F, Firozyar F, Pourkhorshidi AR (2022) Simulated C3A effects on the chloride binding in portland cement with NaCl and CaCl2 cations. J Civil Eng Mater Appl 6(1):41–54
- 99. Sobhani B, Safarianzengir V (2020) Monitoring and prediction of drought using TIBI fuzzy index in Iran. Casp J Environ Sci 18(3):237–250
- 100. Sobhani J, Khanzadi M, Movahedian AH (2013) Support vector machine for prediction of the compressive strength of no-slump concrete. Comput Concr 11(4):337–350
- 101. Tabatabaei M, Salehpour Jam A (2017) Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network. Casp J Environ Sci 15(4):385–399
- 102. Taheri S, Ziad H (2021) Analysis and comparison of moisture sensitivity and mechanical strength of asphalt mixtures containing additives and carbon reinforcement. J Civil Eng Mater Appl 5(1):01–08
- 103. Tatham RH (1982) Vp/Vs and lithology. Geophysics 47(3):336–344
- 104. Tekin A (2014) Early prediction of students’ grade point averages at graduation: a data mining approach. Eurasian J Educ Res 54:207–226
- 105. Ulusay R, Türeli K, Ider MH (1994) Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariate statistical techniques. Eng Geo 38(1–2):135–157
- 106. Vahedi AA (2002) Relationship between static and dynamic elastic parameters of limestone in Seymareh dam site. In: The first conference of Iranian rock mechanics, Tehran (in Persian)
- 107. Vapnik V, Vapnik V (1998) Statistical learning theory. Wiley, New York
- 108. Wang J, Cao J (2021) Data-driven S-wave velocity prediction method via a deep-learning-based deep convolutional gated recurrent unit fusion network. Geophysics 86(6):M185–M196
- 109. Waszkiewicz S, Krakowska-Madejska P, Puskarczyk E (2019) Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland. Acta Geophys 67(6):1885–1894
- 110. Wu Z, Xu J, Chen H, Shao L (2022) Shear strength and mesoscopic characteristics of basalt fiber-reinforced loess after dry-wet cycles. J Mater Civ Eng 34(6):550. https://doi.org/10.1061/(ASCE)MT.1943-5533.0004225
- 111. Xie W, Nie W, Saffari P, Robledo LF, Descote P, Jian W (2021) Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China. Nat Hazards 109(1):931–948
- 112. Xu J, Zhou L, Li Y, Ding J (2022) Experimental study on uniaxial compression behavior of fissured loess before and after vibration. Int J Geomech. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002259
- 113. Yang H, Song K, Zhou J (2022) Automated recognition model of geomechanical information based on operational data of tunneling boring machines. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-021-02723-5
- 114. Yang H, Wang Z, Song K (2020) A new hybrid grey wolf optimizer-feature weighted-multiple kernelsupport vector regression technique to predict TBM performance. Eng Compu 38:2469–2485
- 115. Yang HQ, Li Z, Jie TQ, Zhang ZQ (2018a) Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunn Undergr Sp Tech 1(81):112–120
- 116. Yang HQ, Xing SG, Wang Q, Li Z (2018b) Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides. Eng Geol 239:119–125
- 117. Yang HQ, Zeng YY, Lan YF, Zhou XP (2014) Analysis of the excavation damaged zone around a tunnel accounting for geo-stress and unloading. Int J Rock Mech Min 69:59–66
- 118. Yazdi JS, Kalantary F, Yazdi HS (2013) Prediction of elastic modulus of concrete using support vector committee method. J Mater Civ Eng 25(1):9–20
- 119. Yin G, Alazzawi FJI, Bokov D, Marhoon HA, El-Shafay AS, Rahman ML, Nguyen HC (2022a) Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions. Arab J Chem 15(3):103608
- 120. Yin G, Alazzawi FJI, Mironov S, Reegu F, El-Shafay AS, Rahman ML, Nguyen HC (2022b) Machine learning method for simulation of adsorption separation: comparisons of model’s performance in predicting equilibrium concentrations. Arab J Chem 15(3):103612
- 121. Zamani Faradonbe M, Eagderi S (2015) Fish assemblages as influenced by environmental factors in Taleghan River (the Caspian Sea basin, Alborz Province, Iran). Casp J Environ Sci 13(4):363–371
- 122. Zarkami RR, Pasvisheh S, Goethals P (2012) Application of genetic algorithm (GA) to select input variables in support vector machine (SVM) for analyzing the occurrence of roach, Rutilus rutilus, in streams. Casp J Environ Sci 10(2):237–246
- 123. Zhan C, Dai Z, Soltanian MR, Zhang X (2022) Stage‐wise stochastic deep learning inversion framework for subsurface sedimentary structure identification. Geophys Res Lett 49(1) n/a-n/a. https://doi.org/10.1029/2021GL095823
- 124. Zhang K, Ali A, Antonarakis A, Moghaddam M, Saatchi S, Tabatabaeenejad A, Moorcroft P (2019a) The sensitivity of North American terrestrial carbon fluxes to spatial and temporal variation in soil moisture: an analysis using radar-derived estimates of root-zone soil moisture. J Geophys Res Biogeosci 124(11):3208–3231
- 125. Zhang K, Wang S, Bao H, Zhao X (2019b) Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. NHESS 19(1):93–105
- 126. Zhang L, Huang M, Li M, Lu S, Yuan X, Li J (2021a) Experimental study on evolution of fracture network and permeability characteristics of bituminous coal under repeated mining effect. Nat Resour Res 31(1):463–486
- 127. Zhang L, Huang M, Xue J, Li M, Li J (2021b) Repetitive mining stress and pore pressure effects on permeability and pore pressure sensitivity of bituminous coal. Nat Resour Res (new York, N.y.) 30(6):4457–4476
- 128. Zhang L, Li J, Xue J, Zhang C, Fang X (2021c) Experimental studies on the changing characteristics of the gas flow capacity on bituminous coal in CO2-ECBM and N-2-ECBM. Fuel (guildford) 291:120115
- 129. Zhang X, Ma F, Dai Z, Wang J, Chen L, Ling H, Soltanian MR (2022) Radionuclide transport in multi-scale fractured rocks: a review. J Hazard Mater 424(Pt C):127550. https://doi.org/10.1016/j.jhazmat.2021.127550
- 130. Zhang X, Ma F, Yin S, Wallace CD, Soltanian MR, Dai Z, Lü X (2021d) Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: a critical review. Appl Energy 303:117603. https://doi.org/10.1016/j.apenergy.2021.117603
- 131. Zhang Z, Luo C, Zhao Z (2020) Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography. Nat Hazards 104:2511–2530
- 132. Zhao X, Xia H, Pan L, Song H, Niu W, Wang R, Qin Y (2021) Drought monitoring over Yellow River Basin from 2003–2019 using reconstructed MODIS land surface temperature in google earth engine. Remote Sens 13(18):3748
- 133. Zhou J, Chen C, Wang M, Khandelwal M (2021a) Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int J Min Sci Technol 31(5):799–812
- 134. Zhou J, Li X, Mitri HS (2016) Classification of rock burst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30(5):04016003
- 135. Zhou J, Qiu Y, Khandelwal M, Zhu S, Zhang X (2021b) Developing a hybrid model of Jaya algorithmbased extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min 145:104856
- 136. Zhou J, Shen X, Qiu Y, Li E, Rao D, Shi X (2021c) Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomech Geophys Geo-Energy Geo-Resour. https://doi.org/10.1007/s40948-021-00285-y
- 137. Zhu Z, Zhu Z, Wu Y, Han J (2022) A prediction method of coal burst based on analytic hierarchy process and fuzzy comprehensive evaluation. Front Earth Sci. https://doi.org/10.3389/feart.2021.834958
- 138. Zoveidavianpoor M, Samsuri A, Shadizadeh SR (2013) Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir. Appl Geophys 89:96–107
- 139. Zuo R, Carranza EJM (2011) Support vector machine: a tool for mapping mineral prospectivity. Comput Geosci 37(12):1967–1975
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-151351f2-5660-472d-8fc0-49c7ffeb0f00