Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

Abstract

In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit w L , plastic limit w P , plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content w o p t ) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.

Problems with reinforced concrete industrial floors with regard to subsoil swelling

“…Their empirical sector chart was supplemented in 1981 with areas of the selected clay minerals occurrence [19]. Many empirical graphs showing the expected level of soil swelling susceptibility can be found in the literature [20][21][22][23]. In general, however, none is universally valid for all soils or locations.…”

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

“…Many studies suggest that MEP's iterative process of generating and evaluating mathematical expressions, along with its multi-expression chromosome, makes it a useful tool for solving optimization problems [ [56] , [57] , [58] , [59] ]. The flow chart of MEP algorithm [ 60 ] is shown in Fig. 2 .…”

Predictive modelling of strength characteristics of stabilized medium expansive soils

“…Compared to traditional approaches, these strategies have outstanding predictive capabilities [12]. The development of swelling soils' swell-strength (i.e., Ps and unconfined compression strength UCS) as well as the effects of particle gradation, plasticity, compaction, and other factors have all been extensively studied in the past ten years with regard to compacted hydrophilic clays [13][14][15]. For the preceding design of pavements, residential areas, bridge constructions, water lines, canals, and a variety of lightly loaded infrastructure, a correct estimation of dmax, Wopt, Ps, and the UCS of hydrophilic clays is crucial [16][17][18][19].…”