Details
Title
Evaluation of the unit weight of organic soils from a CPTM using an Artificial Neural NetworksJournal title
Archives of Civil EngineeringYearbook
2021Volume
vol. 67Issue
No 3Affiliation
Straż, Grzegorz : Rzeszow University of Technology, Faculty of Civil and Environmental Engineering and Architecture Civil Engineering, Powstańców Warszawy 12 Av., 35-959 Rzeszow, Poland ; Borowiec, Artur : Rzeszow University of Technology, Faculty of Civil and Environmental Engineering and Architecture Civil Engineering, Powstańców Warszawy 12 Av., 35-959 Rzeszow, PolandAuthors
Keywords
soil unit weight ; artificial neural networks ; organic soils ; organic content ; cone penetration testDivisions of PAS
Nauki TechniczneCoverage
259-281Publisher
WARSAW UNIVERSITY OF TECHNOLOGY FACULTY OF CIVIL ENGINEERING and COMMITTEE FOR CIVIL ENGINEERING POLISH ACADEMY OF SCIENCESBibliography
[1] EN 1997-1: 2008. Eurocode 7: Geotechnical Design – Part 1: General rules.
[2] EN 1997-2: 2009. Eurocode 7: Geotechnical Design – Part 2: Ground Investigation and Testing.
[3] P.K. Robertson, K.L. Cabal, “Guide to Cone Penetration Testing for Geotechnical Engineering”. Gregg Drilling & Testing, Inc, 5-th Edition, 2012.
[4] Y. Cal, “Soil classification by neural-network”, Adv. Eng. Softw. 22: pp. 95–97, 1995.
[5] A. Goh, “Empirical design in geotechnics using neural networks”, Geotechnique 45: pp. 709–714, 1995. https://doi.org/10.1680/geot.1995.45.4.709
[6] M. Shahin, M. Jaksa, H. Maier, “Artificial neural network applications in geotechnical engineering”, Aust. Geomech. 36: 49–62, 2001.
[7] N. Nawari, R, Liang, J. Nusairat, “Artificial intelligence techniques for the design and analysis of deep foundations”. Electron. J. Geotech. Eng. 4: pp 1–21, 1999. Available online: http://geotech.civeng.okstate.edu/ejge/ppr9909/index.html (accessed on).
[8] D. Penumadu, C. Jean-Lou, “Geomaterial modeling using artificial neural networks”, In Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE: Reston, WV, USA, pp 160–184, 1997.
[9] C.H. Zhiming, M. Guotao, Z. Ye, Z. Yanjie, H. Hengyang, “The application of artificial neural network in geotechnical engineering”, In Proceedings of the 2018 International Conference on Civil and Hydraulic Engineering (IConCHE 2018), Qingdao, China, 23–25 November 2018; IOP Publishing: Bristol, UK, 2018; http://dx.doi.org/10.1088/1755-1315/189/2/022054
[10] Z. Wang, Y. Li, “Correction of soil parameters in calculation of embankment settlement using a BP network back-analysis model”, Eng. Geol. 91: pp. 168–177, 2007. https://doi.org/10.1016/j.enggeo.2007.01.007
[11] M.J. Sulewska, “Applying Artificial Neural Networks for analysis of geotechnical problems”, Comput. Assist. Methods Eng. Sci. 18: pp. 230–241, 2011.
[12] M.J. Sulewska, “Artificial Neural modeling of compaction characteristics of cohesionless soil”, Comput. Assist. Methods Eng. Sci. 17: pp. 27–40, 2010.
[13] M.J. Sulewska, “Artificial Neural Networks in the Evaluation of Non-Cohesive Soil Compaction Parameters”, Committee Civil Engineering of the Polish Academy of Sciences: Warsaw, Poland, 2009.
[14] M.J. Sulewska, “Prediction Models for Minimum and Maximum Dry Density of Non-Cohesive Soils”, Pol. J. Environ. Stud. 19: pp. 797–804, 2010.
[15] M. Ochmański, J. Bzówka, “Selected examples of the use of artificial neural networks in geotechnics”, Civ. Environ. Eng. 4: pp. 287–294, 2013.
[16] A. Borowiec, K. Wilk, “Prediction of consistency parameters of fen soils by neural networks”, Comput. Assist. Methods Eng. Sci.21: pp. 67–75, 2014.
[17] M. Kłos, M.J. Sulewska, Z. Waszczyszyn, “Neural identification of compaction characteristics for granular soils”, Comput. Assist. Methods Eng. Sci.18: pp. 265–273, 2011.
[18] G. Wrzesiński, M.J. Sulewska, Z. Lechowicz, “Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network”, Appl. Sci. 8: p. 781, 2018. https://doi.org/10.3390/app8050781
[19] Z. Lechowicz, M. Fukue, S. Rabarijoely, M.J. Sulewska, “Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks”, Appl. Sci. 8: p. 1395, 2018. https://doi.org/10.3390/app8081395
[20] S. Rabarijoely, “A new Approach to the Determination of Mineral and Organic Soil Types Based on Dilatometer Tests (DMT)”, Appl. Sci.8 (11):, p. 2249, 2018. https://doi.org/10.3390/app8112249
[21] G. Straż, A. Borowiec, “Estimating the Unit Weight of Local Organic Soils from Laboratory Tests Using Artificial Neural Networks”, Appl. Sci. 10 (7): p. 2261, 2020. http://dx.doi.org/10.3390/app10072261
[22] Voivodship Inspectorate for Environmental Protection in Rzeszów, “Report on the state of the environment of the Podkarpackie Voivodeship in 2013–2015”, Environmental Monitoring Library, Rzeszow, 2016.
[23] Geotech, Ltd. Department of Geological Services Design and Construction and the Environment, “Geological and Engineering Geological Conditions for Recognition – Engineering for the Construction of Multi-Storey Building at UL; Witolda in Rzeszów”: Rzeszow, Poland, 2010.
[24] PN-EN ISO 17892-2:2014. Geotechnical Investigation and Testing – Laboratory Testing of Soil – Part 2: Determination of Bulk Density.
[25] PN-EN ISO 22476-12:2009. Geotechnical Investigation and Testing – Field Testing – Part 12: Mechanical Cone Penetration Test.
[26] L. Wysokiński, W. Kotlicki, T. Godlewski, “Geotechnical design according to Eurocode 7”, Guide. ITB, Warsaw, 2011.
[27] P.W. Mayne, G.J. Rix, “Correlations Between Shear Wave Velocity and Cone Tip Resistance in Clays”, Soils and Foundations 35 (2): pp. 107–110, 1995.
[28] P.W. Mayne, “The 2nd James K. Mitchell Lecture: Undisturbed Sand Strength from Seismic Cone Tests,” Geomechanics and Geoengineering Vol. 1, No. 4: pp. 239–247, 2006.
[29] P.W. Mayne, “Cone Penetration Testing”, “A Synthesis of Highway Practice”, NCHRP Synthesis 368; Transportation Research Board: Washington, DC, USA, 2007.
[30] P.W. Mayne, J. Peuchen, D. Bouwmeester, “Soil unit weight estimation from CPTs”, In Proceedings of the 2nd International Symposium on Cone Penetration Testing, Huntington Beach, CA, USA, 9–11 May, pp 169–176, 2010.
[31] P.W. Mayne, “Evaluating effective stress parameters and undrained shear strengths of soft-firm clays from CPTu and DMT”, Geotechnical and Geophysical Site Characterisation 5 – Lehane, Acosta-Martínez & Kelly (Eds) © Australian Geomechanics Society, Sydney, Australia, 2016.
[32] P. Robertson, K. Cabal, “Estimating soil unit weight from CPT”, In Proceedings of the 2nd International
[33] Symposium on Cone Penetration Testing, Huntington Beach, CA, USA, 9–11 May, 2010.
[34] A.T. Ozer, S.F. Bartlett, E.C. Lawton, “CPTU and DMT for estimating soil unit weight of Lake Bonneville Clay”, Geotechnical and Geophysical Site Characterization 4: pp. 291–296, 2012.
[35] R.K. Ghanekar, “Unit weight estimation from CPT for Indian offshore soft calcareous clay”, in: “CPTU and DMT in soft clays and organic soils” (eds. Z. Młynarek and J. Wierzbicki), Exlemplum Press, Poznań, Poland, pp. 31–44, 2014.
[36] M.S. Kovacevic, K.G. Gavin, C. Reale, L. Libric, “The use of neural networks to develop CPT correlations for soils in northern Croatia”, Cone Penetration Testing 2018 – Hicks, Pisano & Peuchen (eds), Delft University of Technology, June 2018, The Netherlands.
[37] G. Straż, “Estimating soil unit weight from CPT for selected organic soils”, in: “Selected technical, economic and ecological aspects of contemporary construction” (eds. K. Pujer), Exante, pp. 63–77, 2016.
[38] S.O. Haykin, “Neural Networks and Learning Machines”, 3rd ed.; Pearson Education: Upper Saddle River, NJ, USA, 798, 2009.
[39] M.T. Hagan, H.B. Demuth, M.H. Beale, “Neural Network Design”, PWS Publishing: Boston, MA, USA, 1996.
[40] D. Marquardt, “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”, SIAM J. Appl. Math.3: 11, pp. 431–441, 1963.
[41] M.T. Hagan, M. Menhaj, “Training feed-forward networks with the Marquardt algorithm”. IEEE Trans. Neural Netw. 5: pp. 989–993, 1994.
[42] J.E. Dennis, R.B. Schnabel, “Numerical Methods for Unconstrained Optimization and Nonlinear Equations”, Prentice-Hall: Englewood Clis, NJ, USA, 1983.
[43] D.J.C. MacKay, “Bayesian interpolation”, Neural Comput.4: pp. 415–447, 1992.
[44] Beale, M.H.; Hagan, M.T.; Demuth, H.B.Neural Network ToolboxUser’s Guide; TheMathWorks: Natick, MA,USA, 2010.
[45] GEO5. Geotechnical software. Fine – Civil Engineering Softwere. https://www.finesoftware.pl/.
[46] Statistica 13.3. TIBCO Software Inc. https://www.statsoft.pl/Czytelnia .