Evaluation of ANFIS and Regression Techniques in Estimating Soil Compression Index for Cohesive soils
محتوى المقالة الرئيسي
الملخص
Generally, direct measurement of soil compression index (Cc) is expensive and time-consuming. To save time and effort, indirect methods to obtain Cc may be an inexpensive option. Usually, the indirect methods are based on a correlation between some easier measuring descriptive variables such as liquid limit, soil density, and natural water content. This study used the ANFIS and regression methods to obtain Cc indirectly. To achieve the aim of this investigation, 177 undisturbed samples were collected from the cohesive soil in Sulaymaniyah Governorate in Iraq. Results of this study indicated that ANFIS models over-performed the Regression method in estimating Cc with R2 of 0.66 and 0.48 for both ANFIS and Regression models, respectively. This work is an effort to practice the advantages of machine learning techniques to build a robust and cost-effective model for Cc estimation by designers, decision makers, and stakeholders.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Al-Busoda, B.S., and Al-Taie, A.J., 2010. Statistical Estimation of the Compressibility of Baghdad Cohesive Soil, Journal of Engineering, 16 (4), pp. 5863-5876.
Al-Taie, A.J., Al-Bayati, A.F., and Taki, Z.N.M., 2017. Compression index and compression ratio prediction by artificial neural networks, Journal of Engineering, 23 (12), pp.96-106.
Alzabeebee, S., Alshkane, Y.M. and Rashed, K.A., 2021. Evolutionary computing of the compression index of fine-grained soils, Arabian Journal of Geosciences, 14(19), pp.1-17.
Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., Van Griensven, A., Van Liew, M.W., and Kannan, N., 2012. SWAT: Model use, calibration, and validation, Transactions of the ASABE, 55(4), pp.1491-1508.
ASTM D2216-10, 2010. Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass, ASTM International, West Conshohocken, PA, www.astm.org
ASTM D2435 / D2435M-11, 2011. Standard Test Methods for One-Dimensional Consolidation Properties of Soils Using Incremental Loading, ASTM International, West Conshohocken, PA, www.astm.org.
ASTM D2487-17e1, 2017. Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System), ASTM International, West Conshohocken, PA, www.astm.org
ASTM D4318-10, 2010. Standard Test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils, ASTM International, West Conshohocken, PA, www.astm.org.
ASTM D7263-09 (2018) e2, 2018. Standard Test Methods for Laboratory Determination of Density (Unit Weight) of Soil Specimens, ASTM International, West Conshohocken, PA, www.astm.org.
Bianconi, A., Zuben, C.J.V., Serapião, A.B.D.S. and Govone, J.S., 2010. Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala, Journal of Insect Science, 10(1), p.58.
Bowles, J.E., 1979. Physical and geotechnical properties of soils. McGraw-Hill Book Company.
Cobaner, M., 2011. Evapotranspiration estimation by two different neuro-fuzzy inference systems, Journal of Hydrology, 398(3-4), pp.292-302.
Hamaamin, Y.A., 2014. Applications of soft computing and statistical methods in water resources management, Michigan State University.
Jang J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans Syst Man Cybern, 23 (3), pp.665–685.
Kisi, O., Haktanir, T., Ardiclioglu, M., Ozturk, O., Yalcin, E. and Uludag, S., 2009. Adaptive neuro-fuzzy computing technique for suspended sediment estimation, Advances in Engineering Software, 40(6), pp.438-444.
Lyman, O.R., and Longnecker, M., 2010. An Introduction to Statistical Methods and Data Analysis, 6th edition, BROOKS/COLE Cengage Learning.
MathWorks, 2018. MATLAB Fuzzy Logic Toolbox User’s Guide2, The MathWorks, Inc.
Mokhtari, M., Heshmati R, A.A. ,and Shariatmadari, N., 2014. Compression ratio of municipal solid waste simulation using artificial neural network and adaptive neurofuzzy system, Earth Sciences Research Journal, 18(2), pp.165-171.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE, 50(3), pp.885-900
Nayak, P.C., and Jain, S.K., 2011. Modeling runoff and sediment rate using aneuro-fuzzy technique, In Proceedings of the Institution of Civil Engineers-Water Management, 164 (4), pp. 201-209.
Pham, B.T., Nguyen, M.D., Van Dao, D., Prakash, I., Ly, H.B., Le, T.T., Ho, L.S., Nguyen, K.T., Ngo, T.Q., Hoang, V., and Ngo, H.T.T., 2019. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis, Science of The Total Environment, 679, pp.172-184.
Rai, R.K. and Mathur, B.S., 2008. Event-based sediment yield modeling using artificial neural network, Water Resources Management, 22(4), pp.423-441.
Saadat, M., and Bayat, M., 2022. Prediction of the unconfined compressive strength of stabilised soil by Adaptive Neuro Fuzzy Inference System (ANFIS) and Nonlinear Regression (NLR), Geomechanics and Geoengineering, 17(1), pp.80-91.
Shahin, M.A., Jaksa, M.B., and Maier, H.R., 2001. Artificial Neural Network Applications In Geotechnical Engineering, Australian Geomechanics, 36 (1), pp.49-62.
Skempton, A.W., and Jones, O.T., 1944. Notes on the compressibility of clays, Quarterly Journal of the Geological Society, 100(1-4), pp.119-135, https://doi.org/10.1144/GSL.JGS.1944.100.01-04.08
Sridharan, A., and Nagaraj, H.B., 2000. Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties, Canadian Geotechnical Journal, 37(3), pp.712-722.
Srokosz, P.E., and Bagińska, M., 2020. Application of adaptive neuro-fuzzy inference system for numerical interpretation of soil torsional shear test results, Advances in Engineering Software, 143, pp. 102793.
Terzaghi, K., and Peck, R.B., 1967. Soil mechanics in engineering practice, 2nd edition. Wiley.
Thipparat, T., 2012. Application of Adaptive Neuro Fuzzy, Fuzzy Logic - Algorithms, Techniques and Implementations-Ch.6, Prof. Elmer Dadios (Ed.), ISBN: 978-953-51-0393-6, InTech.