Application of Genetic Programming for Estimation of Soil Compaction Parameters

Abstract

The aim of this study is to propose two numerical models by a well-known soft computing method, Genetic Programming (GP), for the estimation of soils compaction parameters. Genetic Programming is a pattern recognition approach that has the ability of modeling the non-linear behavior of complex engineering problems. The input variables were the soil classification properties, and the outputs were the Optimum Moisture Content (OMC) and Maximum Dry Density (MDD). To provide model, a database including properties of different soils classified as CH, CI, CL, GC, GM, MH, MI, ML and SC was used. In addition, a new Multiple Linear Regression (MLR) based formula using the database, compared with the GP based model. Study results revealed that the proposed formula by GP can predict the compaction parameters of soils in a highly precise manner, and its outputs were in satisfactory conformity with real test results. Performances of the proposed models evaluated using the regression statistical analyses. The proposed formulae can be useful for the preliminary design of engineering projects and are more useful for cases with time and financial limitations.

Estimation of Optimum Moisture Content and Maximum Dry Unit Weight of Fine-Grained Soils using Numerical Methods

“…Then, the values of γ dmax will decrease upon an increment of clay content. These results are in line with the outcomes of the investigations conducted by [2,3,[7][8][9][12][13][14] which revealed that the γ dmax decreases when the LL increases. Moreover, the results corroborate the studies performed by [5,6,9,10] that reported the reduction of the γ dmax upon the increment of the PI.…”

“…Atterberg limits are good predictors of the γ dmax . However, the utilization of the specific gravity can improve the accuracy of the γ dmax value like previously proposed by [5][6][7]. A 3-dimensional representation of the correlation between the W OP , liquid limit and plasticity index is shown in Figure 2.…”

“…The input variables are the Atterberg limits (PL, LL) and the specific gravity (Gs). Mathematical models to predict the W OP and the γ dmax are given by: Naderi et al [7] developed 2 mathematical models to predict the W OP and γ dmax utilizing the genetic programming technique. The process relies on a pattern identification method that models the non-linear behaviour of complex design issues.…”

“…It is better to use LL (higher range of values, no Fine-Grained Soils using Numerical Methods Armand Augustin FONDJO et al http://wjst.wu.ac.th need to evaluate plastic limit PL) than PI. Besides, Naderi et al [7] and Kok Shien et al [10] utilized the grain size distribution parameters and the Atterberg limits in their models. Noor et al [5]; Singh and Noor [6] proposed models using the specific gravity and Atterberg limits.…”

Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

“…Likewise, in reference [37], the authors predicted the performance characteristics of stabilized soils by utilizing a variant of genetic programming, namely, linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA. In their own efforts, Naderi et al [38] proposed two numerical models by employing GP for the estimation of soil compaction parameters. However, their input variables were the soil classification properties and the output variables were the optimum moisture content (OMC) and maximum dry density (MDD).…”