Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques

Ebid, Ahmed M.; Onyelowe, Kennedy C.; Arinze, Emmanuel E.

doi:10.1155/2021/3267018

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…As well as other disciplines of civil engineering, including structural and earthquake engineering [ 36 , 37 ]. However, according to the literature, only one researcher has used the EPR technique to estimate BC on sloped ground [ 38 ], and to date, no research has been conducted using EPR to assess the interference impact on BC caused by strip foundations.…”

Section: Introductionmentioning

confidence: 99%

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

Ismael,

Sulaiman

2024

PLoS ONE

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It is imperative to understand how foundations behave on earthen slopes to accurately predict their allowable carrying capacity in geotechnical engineering. A comprehensive finite element (FE) simulation with PLAXIS 2D was conducted to assess the effects of various parameters on the bearing capacity (BC) of single- and double-strip foundations placed near the earth’s slope crest. The specified parameters include foundation width (B) and depth (Df/B); setback distance between the slope edge and foundation (b/B); soil internal friction (ϕ) and cohesion (c); slope inclination (β); and spacing between foundations (S/B). In addition, the numerically simulated database was used to develop simple mathematical expressions for predicting the capacities in both cases using evolutionary polynomial regression (EPR). The results revealed that the bearing capacity of single- and double-strip foundations increased with an increase in all studied parameters except slope inclination. For single-strip foundations, the outcomes demonstrated that slope inclination has no impact on BC when it is located 6B from the slope edge. However, under interference conditions, the critical center-to-center spacing between foundations is 3–4B, beyond which they behave as individual foundations. Additionally, EPR provides a robust method of predicting the BC of single- and double-strip foundations within slope crests based on the strong correlation of various statistical criteria between simulated and predicted results from training, validation, and testing. Finally, according to sensitivity analysis, in both single and double-strip foundations resting on an earthen slope crest, b/B, B, and ϕ are the most important input parameters that impact the output results.

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Section: Introductionmentioning

confidence: 99%

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

Ismael,

Sulaiman

2024

PLoS ONE

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“…The complex task of predicting the bearing capacity of shallow foundations lying on geosynthetic-reinforced soils was recently analyzed using AI techniques [30][31][32][33][34]. Other studies focused on analyzing the bearing capacity of shallow foundations on natural slopes using machine learning (ML) techniques [35][36][37]. However, none of the existing studies focused on the application of ML methods for the prediction of the seismic bearing capacity of shallow foundations positioned on the crest of geosyntethic-reinforced soil structures.…”

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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“…For example, Das et al [9] experimentally compared the enhancement in bearing capacity of strip footing rested on sand and clay layers when they are reinforced with geo-grid; Arvin et al [10] utilized shakedown theory to determine the static and dynamic bearing capacity of strip footings under varying repeated loads; and Gnananandarao et al [11] numerically studied the bearing capacity of multi-edge skirted footings resting on sand. Finally, Ebid et al estimated the bearing capacity of strip footing nearing or within slopes [12] and resting on multi-layered soil [13] using different AI techniques. Generally, all previous literature agreed that the ultimate bearing capacity of strip footing rested on pure undrained clay and subjected to a centric vertical load is about five times the undrained shear strength of the clay [14].…”

Section: Introductionmentioning

confidence: 99%

Using FEM-AI Technique to Predict the Behavior of Strip Footing Rested on Undrained Clay Layer Improved with Replacement and Geo-Grid

et al. 2023

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The objective of this research is to predict how strip footings behave when rested on an undrained clay layer enhanced using a top replacement layer with and without a geo-grid. The study was conducted in several stages, including collecting load-settlement curves from "Finite Element Method" (FEM) models with different clay strengths, replacement thicknesses, and axial stiffnesses of the geo-grid. These curves were then idealized using a hyperbolic model, and the idealized hyperbolic parameters were predicted using three different AI techniques. According to the numerical results, the ultimate bearing pressure of pure clay models was found to be five times the undrained strength of the clay. These findings align with most established empirical bearing capacity formulas for undrained clays. The results also suggest that the initial modulus of the subgrade reaction is solely influenced by replacement thickness. Additionally, the enhancement in subgrade reaction due to the replacement layer decreases with increasing clay strength. However, the percentage of improvement decreased with higher clay strength. Moreover, the impact of the geo-grid was significant for settlement beyond 50mm, and it was more impactful in soft clay than in stiff clay. Finally, the research proposed predictive models employing the "Genetic Programming" (GP), "Artificial Neural Networks" (ANN), and "Evolutionary Polynomial Regression" (EPR) techniques, and these models exhibited an accuracy of about 88%. Doi: 10.28991/CEJ-2023-09-05-014 Full Text: PDF

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Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques

Cited by 7 publications

References 27 publications

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests

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

Using FEM-AI Technique to Predict the Behavior of Strip Footing Rested on Undrained Clay Layer Improved with Replacement and Geo-Grid

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