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Original Articles

Artificial neural network prediction models of heavy metal polluted soil resistivity

, , &
Pages 1570-1590 | Received 16 Aug 2015, Accepted 18 Feb 2019, Published online: 05 Apr 2019

References

  • Abdul, A. S., Gibson, T. L., & Rai, D. N. (1990). Laboratory studies of the flow of some organic solvents and their aqueous solutions through bentonite and kaolin clays. Ground Water, 28(4), 524–533. doi:10.1111/j.1745-6584.1990.tb01708.x
  • Abu-Hassanein, Z. S., Benson, C. H., & Blotz, L. R. (1996). Electrical resistivity of compacted clays. Journal of Geotechnical Engineering, 122(5), 397–406. doi:10.1061/(ASCE)0733-9410(1996)122:5(397)
  • Amegashie, F., Shang, J. Q., Yanful, E. K., Ding, W., & Al-Martini, S. (2006). Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil. Canadian Geotechnical Journal, 43(1), 100–109. doi:10.1139/t05-085
  • Arulanandan, K., & Smith, S. S. (1973). Electrical dispersion in relation to soil structure. Journal of Soil Mechanics & Foundations Division, 99, 1113–1133.
  • ASTM, D. G. (2012). Standard Test Method for Field Measurement of Soil Resistivity Using the Wenner Four-Electrode Method. PA, USA: ASTM International.
  • Banimahd, M., Yasrobi, S. S., & Woodward, P. K. (2005). Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification. Computers and Geotechnics, 32(5), 377–386.
  • Caglar, N., & Arman, H. (2007). The applicability of neural networks in the determination of soil profiles. Bulletin of Engineering Geology and the Environment, 66(3), 295–301. doi:10.1007/s10064-006-0075-9
  • Caudill, M. (1988). Neural networks primer. Part III. AI Expert, 3(6), 53–59.
  • Chabukdhara, M., & Nema, A. K. (2013). Heavy metals assessment in urban soil around industrial clusters in Ghaziabad, India: Probabilistic health risk approach. Ecotoxicology and Environmental Safety, 87, 57–64. doi:10.1016/j.ecoenv.2012.08.032
  • Chu, Y., Liu, S. Y., Cai, G. J., & Bian, H. L. (2016a). A study in the micro-characteristic and electricity properties of silt clay contaminated by heavy metal zinc. Japanese Geotechnical Society Special Publication, 2(14), 556–559. doi:10.3208/jgssp.CHN-17
  • Chu, Y., Liu, S. Y., Cai, G. J., & Bian, H. L. (2016b). Research of influencing factor resistivity model of heavy metal pollution soil based on the orthogonal analysis. Journal of Southeast University, 46(4), 866–871.
  • Demond, A. H., & Roberts, P. V. (1993). Estimation of two‐phase relative permeability relationships for organic liquid contaminants. Water Resources Research, 29(4), 1081–1090. doi:10.1029/92WR02987
  • Du, Y. J., Jiang, N. J., Shen, S. L., & Jin, F. (2012). Experimental investigation of influence of acid rain on leaching and hydraulic characteristics of cement-based solidified/stabilized lead contaminated clay. Journal of Hazardous Materials, 225, 195–201. doi:10.1016/j.jhazmat.2012.04.072
  • Du, Y. J., Jiang, N. J., Liu, S. Y., Jin, F., Singh, D. N., & Puppala, A. J. (2014). Engineering properties and microstructural characteristics of cement-stabilized zinc-contaminated kaolin. Canadian Geotechnical Journal, 51(3), 289–302. doi:10.1139/cgj-2013-0177
  • Ellis, G. W., Yao, C., Zhao, R., & Penumadu, D. (1995). Stress–strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering, 121(5), 429–435. doi:10.1061/(ASCE)0733-9410(1995)121:5(429)
  • Erzin, Y., Rao, B. H., & Singh, D. N. (2008). Artificial neural network models for predicting soil thermal resistivity. International Journal of Thermal Sciences, 47(10), 1347–1358. doi:10.1016/j.ijthermalsci.2007.11.001
  • Flood, I., & Kartam, N. (1994). Neural networks in civil engineering. I: Principles and understanding. Journal of Computing in Civil Engineering, 8(2), 131–148. doi:10.1061/(ASCE)0887-3801(1994)8:2(131)
  • Foreman, D. E., & Daniel, D. E. (1986). Permeation of compacted clay with organic chemicals. Journal of Geotechnical Engineering, 112(7), 669–681. doi:10.1061/(ASCE)0733-9410(1986)112:7(669)
  • Fukue, M., Minato, T., Horibe, H., & Taya, N. (1999). The micro-structures of clay given by resistivity measurements. Engineering Geology, 54(1-2), 43–53. doi:10.1016/S0013-7952(99)00060-5
  • Gajo, A., & Maines, M. (2007). Mechanical effects of aqueous solutions of inorganic acids and bases on a natural active clay. Géotechnique, 57(8), 687–699. doi:10.1680/geot.2007.57.8.687
  • Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143–151. doi:10.1016/0954-1810(94)00011-S
  • Gokceoglu, C. (2002). A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Engineering Geology, 66(1–2), 39–51. doi:10.1016/S0013-7952(02)00023-6
  • Gokceoglu, C., & Zorlu, K. (2004). A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence, 17(1), 61–72. doi:10.1016/j.engappai.2003.11.006
  • Grima, M. A., & Babuška, R. (1999). Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanics and Mining Sciences, 36(3), 339–349. doi:10.1016/S0148-9062(99)00007-8
  • Hamed, S. A. (1990). Steady-state modeling, analysis, and performance of transistor-controlled AC power conditioning systems. IEEE Transactions on Power Electronics, 5(3), 305–313. doi:10.1109/63.56521
  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. doi:10.1016/0893-6080(89)90020-8
  • Kim, H., Rauch, A. F., & Haas, C. T. (2004). Automated quality assessment of stone aggregates based on laser imaging and a neural network. Journal of Computing in Civil Engineering, 18(1), 58–64. doi:10.1061/(ASCE)0887-3801(2004)18:1(58)
  • Kurup, P. U., & Griffin, E. P. (2006). Prediction of soil composition from CPT data using general regression neural network. Journal of Computing in Civil Engineering, 20(4), 281–289. doi:10.1061/(ASCE)0887-3801(2006)20:4(281)
  • Järup, L. (2003). Hazards of heavy metal contamination. British Medical Bulletin, 68(1), 167–182. doi:10.1093/bmb/ldg032
  • JTG E40. (2007). Test Methods of Soils for Highway Engineering, Ministry of Construction of the People's Republic of China.
  • Lee, I. M., & Lee, J. H. (1996). Prediction of pile bearing capacity using artificial neural networks. Computers and Geotechnics, 18(3), 189–200. doi:10.1016/0266-352X(95)00027-8
  • Liu, G. H., Wang, Z. Y., & Huang, J. P. (2004). Research on electrical resistivity feature of soil and it's application. Chinese Journal of Geotechnical Engineering-Chinese Edition, 26(1), 83–87.
  • Mitchell, J. K., & Arulanandan, K. (1968). Electrical dispersion in relation to soil structure. Journal of the Soil Mechanics and Foundations Division, 94(2), 447–472.
  • Najjar, Y., Basheer, I., & McReynolds, R. (1996). Neural modeling of Kansas soil swelling. Transportation Research Record: Journal of the Transportation Research Board, 1526(1), 14–19. doi:10.3141/1526-03
  • Powers, S. E., Anckner, W. H., & Seacord, T. F. (1996). Wettability of NAPL-contaminated sands. Journal of Environmental Engineering, 122(10), 889–896. doi:10.1061/(ASCE)0733-9372(1996)122:10(889)
  • Saarenketo, T. (1998). Electrical properties of water in clay and silty soils. Journal of Applied Geophysics, 40(1–3), 73–88. doi:10.1016/S0926-9851(98)00017-2
  • Sakellariou, M. G., & Ferentinou, M. D. (2005). A study of slope stability prediction using neural networks. Geotechnical and Geological Engineering, 23(4), 419. doi:10.1007/s10706-004-8680-5
  • Samouëlian, A., Cousin, I., Tabbagh, A., Bruand, A., & Richard, G. (2005). Electrical resistivity survey in soil science: a review. Soil and Tillage Research, 83(2), 173–193. doi:10.1016/j.still.2004.10.004
  • Sapkota, B., & Cioppa, M. T. (2012). Assessing the use of magnetic methods to monitor vertical migration of metal pollutants in soil. Water, Air, & Soil Pollution, 223(2), 901–914. doi:10.1007/s11270-011-0911-9
  • Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2004). Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering, 18(2), 105–114. doi:10.1061/(ASCE)0887-3801(2004)18:2(105)
  • Shea, P. F., & Luthin, J. N. (1961). An investigation of the use of the four-electrode probe for measuring soil salinity in situ. Soil Science, 92(5), 331–339.
  • Shi, J. J. (2000). Reducing prediction error by transforming input data for neural networks. Journal of Computing in Civil Engineering, 14(2), 109–116. doi:10.1061/(ASCE)0887-3801(2000)14:2(109)
  • Sinha, S. K., & Wang, M. C. (2008). Artificial neural network prediction models for soil compaction and permeability. Geotechnical and Geological Engineering, 26(1), 47–64. doi:10.1007/s10706-007-9146-3
  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B (Methodological), 36(2), 111–147. doi:10.1111/j.2517-6161.1974.tb00994.x
  • Sun, Y., Zhou, Q., Xie, X., & Liu, R. (2010). Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China. Journal of Hazardous Materials, 174(1–3), 455–462. doi:10.1016/j.jhazmat.2009.09.074
  • Teh, C. I., Wong, K. S., Goh, A. T. C., & Jaritngam, S. (1997). Prediction of pile capacity using neural networks. Journal of Computing in Civil Engineering, 11(2), 129–138. doi:10.1061/(ASCE)0887-3801(1997)11:2(129)
  • Twomey, J. M., & Smith, A. E. (1997). Validation and verification. In chapter 4: Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE Press, New York, 44–64.
  • Wahid, A. S., Gajo, A., & Di Maggio, R. (2011). Chemo-mechanical effects in kaolinite. Part 1: Prepared samples. Géotechnique, 61(6), 439–447. doi:10.1680/geot.8.P.067
  • Yoon, G. L., Oh, M. H., & Park, J. B. (2002). Laboratory study of landfill leachate effect on resistivity in unsaturated soil using cone penetrometer. Environmental Geology, 43(1-2), 18–28. doi:10.1007/s00254-002-0649-1
  • Young-Su, K., & Byung-Tak, K. (2006). Use of artificial neural networks in the prediction of liquefaction resistance of sands. Journal of Geotechnical and Geoenvironmental Engineering, 132(11), 1502–1504. doi:10.1061/(ASCE)1090-0241(2006)132:11(1502)
  • Zurada, J. M. (1992). Introduction to Artificial neural systems. St. Paul: West.

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