Determination of Bearing Capacity of Stone Column with Application of Neuro-fuzzy System

Das, Manita; Dey, Ashim Kanti

doi:10.1007/s12205-017-1497-6

Cited by 17 publications

(2 citation statements)

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“…Machine learning (ML) approaches have recently attracted much attention for developing paradigms that can precisely predict complex system parameters [12,[26][27][28][29][30][31][32][33][34]. Al-Obaidy and Al-Shueli [30] predicted the bearing capacity of stone columns using MATLAB's Neural Network Toolbox.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed model showed reliable results, allowing the model to be used as a forecasting tool instead of relying on the results of expensive and time-consuming field or experimental tests. Similarly, Das and Dey [28] applied the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the bearing capacity of stone columns. The authors developed three models: ANFIS-E, which used experimental results as input; ANFIS-A, which used analytical results as input; and ANFIS-EA, which used both experimental and analytical results as input.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Bearing Capacity of Composite Grounds Made of Geogrid-Reinforced Sand over Encased Stone Columns Floating in Soft Soil Using a White-Box Machine Learning Model

Zeini

Lwti

Imran³

et al. 2023

Applied Sciences

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Stone columns have been extensively advocated as a traditional approach to increase the undrained bearing capacity and reduce the settlement of footings sitting on cohesive ground. However, due to the complex interaction between the soil and the stone columns, there currently needs to be a commonly acknowledged approach that can be used to precisely predict the undrained bearing capacity of the system. For this reason, the bearing capacity of a sandy bed reinforced with geogrid and sitting above a collection of geogrid-encased stone columns floating in soft clay was studied in this research. Using a white-box machine learning (ML) technique called Multivariate Polynomial Regression (MPR), this work aims to develop a model for predicting the bearing capacity of the referred foundation system. For this purpose, two hundred and forty-five experimental results were collected from the literature. In addition, the model was compared to two other ML models, namely, a black-box model known as Random Forest (RF) and a white-box ML model called Linear Regression (LR). In terms of R2 (coefficient of determination) and RMSE (Root Mean Absolute Error) values, the newly proposed model outperforms the two other referred models and demonstrates robust estimation capabilities. In addition, a parametric analysis was carried out to determine the contribution of each input variable and its relative significance on the output.

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