Implementing ensemble learning models for the prediction of shear strength of soil

Rabbani, Ahsan; Samui, Pijush; Kumari, Sudesh

doi:10.1007/s42107-023-00629-x

Cited by 20 publications

(1 citation statement)

References 67 publications

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“…As a result, the following conclusion on the order of signi cance of the factors that in uenced the outcome may be drawn: LI > ρ d > ρ b > e > w c > PL > LL > sample depth > I P > proportion of loam > proportion of sand. It was found, that the factors associated to water were the most important elements in the practice of forecasting SSS (Rabbani et al, 2023). It is appropriate since water reduces friction and linkage among particles of soil, and therefore the SS for soil having lesser water content shall be greater than that of soil having higher water content (Ly and Pham, 2020).…”

Section: Importance Of Variable Used and Its Interpretationmentioning

confidence: 99%

Optimized ANN-based approach for estimation of shear strength of soil

2023

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The shear strength of the soil (SSS) is a signi cant attribute which is employed most frequently throughout the design phase of construction projects. The conventional approach of determining shear strength (SS) in the laboratory is one which is costlier as well as time taken process. The ability to precisely predict the SSS without the need for laborious and expensive testing in a laboratory is just one of the real-world needs of geotechnical professionals. In this paper an attempt has been made to develop a common methodology for predicting the SSS using optimized models. For this purpose, three additional optimized algorithms (GA, MPA, and PSO) were utilized to improve the bias and weight of the ANN's learning parameters, and three optimized ANNs (ANN-GA, ANN-MPA, and ANN-PSO) were developed. Validation of all the developed optimized models was executed using RMSE, R 2 , RSR, WI, and NSE, indices. After validation of optimized models it was found that out of three ANN-GA produces good modelling outcome in training as well as in testing phase and outperforming other models. It has been shown that the GA develops the most trustworthy ANN, and this was also validated by the rank analysis of developed models. When trying to predict SSS, it has been shown that the liquidity index (LI) is the key factor to take into consideration. This was determined by plotting the feature signi cance plot along with the feature selection plot. Following the LI, the water content (w c )) is the second most important input variable that has an effect on the value of the parameter of interest being investigated in the present investigation. In a broad sense, it was found that the factors associated to water were the primary characteristics that impact the prediction of SSS.

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Section: Importance Of Variable Used and Its Interpretationmentioning

confidence: 99%