Comparative Study Between MLR and ANN Techniques to Predict Swelling Pressure of Expansive Clays

Bag, Ramakrishna; Bharti, Abhishek; Jadda, Koteswaraarao; Kumar, Munna

doi:10.1007/s10706-022-02099-5

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…To address the issue with the help of a relatively larger database having more robustness, numerous soft computing approaches were used for predicting the P s -ES to enable better use of soil for geotechnical engineering purposes. In this regard, multiple regression (MLR), the artifcial neural network (ANN) approach [2,49] using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) [50], the adaptive neural fuzzy inference system (ANFIS) [51][52][53], and gene expression programming (GEP) [2] have been previously used to predict the P s -ES. Among these methods, the GEP technique is an efcient approach that exhibits the capability to estimate complex as well as highly nonlinear problems [2,54,55].…”

Section: Introductionmentioning

confidence: 99%

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

Jalal

Iqbal

Khan

et al. 2023

Advances in Materials Science and Engineering

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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.

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