Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques

Sultana, Parbin; Dey, Ashim Kanti; Kumar, Dhawal

doi:10.1016/j.rineng.2022.100489

Cited by 13 publications

(2 citation statements)

References 44 publications

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“…In the machine learning studies [ 29 , [173] , [174] , [175] , [176] , [177] , [178] ], SVM are employed for emotion recognition. KNN [ 179 , 180 ], ANN [ 181 , 182 ], RF [ 31 ], Decision Tree (DT) [ 183 ], MLP [ 184 ], RGNN, FG-SVM [ 185 ], LSSVM [ 186 ], MC-SVM [ 184 ] are employed for recognize the human emotions (see Fig. 20 Overview of the ER based on ML mothed).…”

Section: Framework For Ermentioning

confidence: 99%

EEG-based emotion recognition systems; comprehensive study

Hamzah,

Abdalla

2024

Heliyon

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Section: Framework For Ermentioning

confidence: 99%

EEG-based emotion recognition systems; comprehensive study

Hamzah,

Abdalla

2024

Heliyon

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“…AI is frequently utilized in a variety of engineering domains, including geotechnical engineering, to map non-linear correlations between input and target variables in complex problems (e.g., [24][25][26]). Nowadays such AI-based approaches are becoming increasingly popular, mainly because computational resources and extensive databases are more readily available (e.g., [27][28][29]). These enormous amounts of data are difficult to analyze using standard approaches.…”

Section: Introductionmentioning

confidence: 99%

On the Potential of Using Random Forest Models to Estimate the Seismic Bearing Capacity of Strip Footings Positioned on the Crest of Geosynthetic-Reinforced Soil Structures

Ausilio,

Durante,

Zimmaro

2023

Geosciences

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Geosynthetic-reinforced soil structures are often used to support shallow foundations of various infrastructure systems including bridges, railways, and highways. When such infrastructures are located in seismic areas, their performance is linked to the seismic bearing capacity of the foundation. Various approaches can be used to calculate this quantity such as analytical solutions and advanced numerical models. Building upon a robust upper bound limit analysis, we created a database comprising 732 samples. The database was then used to train and test a model based on a random forest machine learning algorithm. The trained random forest model was used to develop a publicly available web application that can be readily used by researchers and practitioners. The model considers the following input factors: (1) the ratio of the distance of the foundation from the edge and the width of the foundation (D/B), (2) the slope angle (β), (3) the horizontal seismic intensity coefficient (kh), and (4) the dimensionless geosynthetic factor, which accounts for the tensile strength of the geosynthetic. Leveraging the model developed in this study, we show that the most important features to predict the seismic bearing capacity of strip footings positioned on the crest of geosynthetic-reinforced soil structures are D/B and kh.

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Artificial Intelligence for Bearing Capacity Evaluation of Shallow Foundation: an Overview

Khajehzadeh,

Keawsawasvong

2024

Geotech Geol Eng

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Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques

Cited by 13 publications

References 44 publications

EEG-based emotion recognition systems; comprehensive study

EEG-based emotion recognition systems; comprehensive study

On the Potential of Using Random Forest Models to Estimate the Seismic Bearing Capacity of Strip Footings Positioned on the Crest of Geosynthetic-Reinforced Soil Structures

Artificial Intelligence for Bearing Capacity Evaluation of Shallow Foundation: an Overview

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