Abstract
Ahmed and Siddiqua (2016) introduced a new regression-based relationship in the form of a three-parameter Weibull function, herein referred to as the 3P-W model, for modeling the compressibility behavior (i.e. the void ratio-effective stress relationship) of soils. In this discussion, important drawbacks associated with the newly proposed 3P-W model were outlined. In addition, an attempt was made to extend the practicality of the proposed model by introducing a simple analytical solution to derive regression-based equations for determining the compressibility curve variables (i.e. the recompression index, pre-consolidation pressure and compression index). The proposed analytical concept is primarily intended to replace the current conventional graphical method by providing consistent-error free results. The proposed equations in this discussion accompanied by the original 3P-W model construct a unique regression aided-analytical framework which can be employed for advance numerical simulations.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmed SI, Siddiqua S (2016) Compressibility behavior of soils: a statistical approach. Geotech Geol Eng. doi:10.1007/s10706-016-9996-7
Casagrande A (1936) The determination of pre-consolidation load and its practical significance. In: Casagrande A, Rutledge PC, Watson JD (eds) 1st International conference on soil mechanics and foundation engineering. ASCE, Cambridge,MA, pp 60–64
Çelik S, Tan Ö (2005) Determination of preconsolidation pressure with artificial neural network. Civ Eng Environ Syst 22(4):217–231. doi:10.1080/10286600500383923
Cui YJ, Delage P (1996) Yielding and plastic behaviour of an unsaturated compacted silt. Géotechnique 46(2):291–311. doi:10.1680/geot.1996.46.2.291
Jose BT, Sridharan A, Abraham BM (1989) Log-log method for determination of preconsolidation pressure. Geotech Test J 12(3):230–237. doi:10.1520/GTJ10974J
Kordnaeij A, Kalantary F, Kordtabar B, Mola-Abasi H (2015) Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties. Soils Found 55(6):1335–1345. doi:10.1016/j.sandf.2015.10.001
Mohammadzadeh SD, Bolouri Bazaz J, Alavi AH (2014) An evolutionary computational approach for formulation of compression index of fine-grained soils. Eng Appl Artif Intell 33:58–68. doi:10.1016/j.engappai.2014.03.012
Nagaraj TS, Murthy BRS (1985) Prediction of the preconsolidation pressure and recompression index of soils. Geotech Test J 8(4):199–202. doi:10.1520/GTJ10538J
Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay-bearing soils. Bull Eng Geol Environ 67(4):537–545. doi:10.1007/s10064-008-0168-8
Park HI, Lee SR (2011) Evaluation of the compression index of soils using an artificial neural network. Comput Geotech 38(4):472–481. doi:10.1016/j.compgeo.2011.02.011
Sridharan A, Nagaraj HB (2000) Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties. Can Geotech J 37(3):712–722. doi:10.1139/t99-128
Sridharan A, Abraham BM, Jose BT (1991) Improved technique for estimation of preconsolidation pressure. Géotechnique 41(2):263–268. doi:10.1680/geot.1991.41.2.263
Tavenas F, Des Rosiers JP, Leroueil S, La Rochelle P, Roy M (1979) The use of strain energy as a yield and creep criterion for lightly overconsolidated clays. Géotechnique 29(3):285–303. doi:10.1680/geot.1979.29.3.285
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Soltani, A. Discussion of “Compressibility Behavior of Soils: A Statistical Approach” by Syed Iftekhar Ahmed and Sumi Siddiqua [Geotechnical and Geological Engineering, doi: 10.1007/s10706-016-9996-7]. Geotech Geol Eng 34, 1687–1692 (2016). https://doi.org/10.1007/s10706-016-0062-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10706-016-0062-2