Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning

Khatti, Jitendra; Grover, Kamaldeep Singh

doi:10.1007/s40515-023-00357-4

Transp. Infrastruct. Geotech.

2023

DOI: 10.1007/s40515-023-00357-4

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Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning

Jitendra Khatti,

Kamaldeep Singh Grover

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2024

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Cited by 15 publications

(1 citation statement)

References 91 publications

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“…For instance, references [16][17][18] demonstrate modeling using the cosine amplitude method, and relevant algorithms were then employed to analyze and predict various engineering issues. Simultaneously, numerous machine learning studies to solve engineering problems have proposed several valuable and innovative designs [19][20][21][22] .…”

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confidence: 99%

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Qi,

Chen,

Yang

et al. 2024

Sci Rep

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A methodology combining physical experiments with simulation was employed to acquire contact parameters of sandy soil precisely for planting tiger nuts in the desert area of Xinjiang. The stacking angle under different parameter combinations was applied as a response value. Through the Plackett–Burman test, several factors that have a significant influence were determined. The steepest ascent test was conducted to establish the finest scope of values for these parameters. The stacking angle was considered the response variable, and non-linear tools were used to optimize these parameters for simulation. The findings showed that applying response surface methodology (RSM) resulted in a relative error of 1.24%. In the case of BP-GA, the relative error compared to the physical test value was 0.34%, while for BP, it was 2.18%. After optimization using Wavelet Neural Network (WNN), the relative error was reduced to only 0.15%. Results suggest that WNN outperforms the RSM model, and the sandy soil model and parameters generated using WNN can be effectively utilized for discrete element simulation research.

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confidence: 99%

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Qi,

Chen,

Yang

et al. 2024

Sci Rep

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Assessing changes in soil electrical conductivity under runoff influences using gradient boosting and metaheuristic algorithms

Zhao,

Lan,

2024

Electr Eng

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Machine learning-based identification of critical factors for cadmium accumulation in rice grains

Yang,

Li,

Nie

et al. 2024

Environ Geochem Health

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Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning

Cited by 15 publications

References 91 publications

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Calibration of contact parameters of sandy soil for planting tiger nut based on non-linear tools

Assessing changes in soil electrical conductivity under runoff influences using gradient boosting and metaheuristic algorithms

Machine learning-based identification of critical factors for cadmium accumulation in rice grains

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