A framework to predict the load-settlement behavior of shallow foundations in a range of soils from silty clays to sands using CPT records

MolaAbasi, Hossein; Khajeh, Aghileh; Chenari, Reza Jamshidi; Payan, Meghdad

doi:10.1007/s00500-021-06485-8

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In this study, the root mean square error (RMSE), the determination coefficient (R 2 ), the mean absolute error (MAE), the prediction accuracy (U 1 ), the prediction quality (U 2 ), and the variance accounted for (VAF) were used as performance indicators to evaluate the reliability and accuracy of all AI models and empirical formula for predicting UCS. These indicators are defined as follows [47,49,[86][87][88][89][90][91][92][93][94][95]:…”

Section: Performance Indices For the Assessment Of Modelsmentioning

confidence: 99%

A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock

Zhou

Dias

et al. 2022

Applied Sciences

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Uniaxial compressive strength (UCS) is one of the most important parameters to characterize the rock mass in geotechnical engineering design and construction. In this study, a novel kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model was proposed to predict the UCS of 271 rock samples. Four parameters namely the porosity (Pn, %), Schmidt hardness rebound number (SHR), P-wave velocity (Vp, km/s), and point load strength (PLS, MPa) were considered as the input variables, and the UCS is the output variable. To verify the effectiveness and accuracy of the KELM-GWO model, extreme learning machine (ELM), KELM, deep extreme learning machine (DELM) back-propagation neural network (BPNN), and one empirical model were established and compared with the KELM-GWO model to predict the UCS. The root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), prediction accuracy (U1), prediction quality (U2), and variance accounted for (VAF) were adopted to evaluate all models in this study. The results demonstrate that the proposed KELM-GWO model was the best model for predicting UCS with the best performance indices. Additionally, the identified most important parameter for predicting UCS is the porosity by using the mean impact value (MIV) technique.

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