A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks

Arabet, Leila; Hidjeb, M.; Belaabed, Faris

doi:10.48084/etasr.5394

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…In the field of material modeling, some researchers [9][10][11] have used a back-propagation ANN to model the behavior of concrete in the state of plane stress under monotonic biaxial loading and compressive uniaxial cycle loading. Their findings appear to be very promising.…”

Section: Introductionmentioning

confidence: 99%

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Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

Cao Van

2024

Eng. Technol. Appl. Sci. Res.

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Piles are typically designed to ensure the bearing capacity and settlement elastic behavior. However, some projects seem over-designed, leading to unnecessary waste, whereas others experience excessive settlement. This could be caused by various factors, such as site investigation, sampling and testing methods, selection of soil behavior model, and calculation programs. To achieve a successful pile design, engineers must consider, among others, the loads applied to the pile, the resistance capacity of the piles, the pile material's bearing capacity, the pile's displacement, and the soil's settlement. On the other hand, the input parameters for geotechnical problems, in general, and pile design problems, in particular, often do not reflect the actual behavior of the soil due to its heterogeneous and anisotropic nature. To address these challenges, an Artificial Neural Network (ANN) approach is proposed for pile design, using a relatively wide range of soil input data. This study establishes a numerical program for pile design combined with the ANN approach, validated by verifying the pile design of a project constructed in Vietnam. The results indicate that the proposed program can reasonably simulate pile group behavior and assist engineers in deploying appropriate safety factors.

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Section: Introductionmentioning

confidence: 99%

“…Their findings appear to be very promising. Authors in [12] demonstrated the effectiveness of ANNs in characterizing composite materials. They employed a back-propagation ANN to predict composite thermal properties accurately.…”

Section: Introductionmentioning

confidence: 99%

Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

Cao Van

2024

Eng. Technol. Appl. Sci. Res.

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Effect of addition of eggshell powder to improve the compressibility parameters of Doukane’s clay soils: a case study Tebessa-Algeria

Malaoui,

Djellali,

Arabet

et al. 2024

SEES

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This study examines the effects of Eggshell Powder (ESP) as an additive material, used in varying amounts from 2% to 8%. It focuses on how ESP influences the physical properties of expansive clay soils, particularly concerning compressibility parameters, the oedometer modulus (E′), and swelling pressure. Additionally, the study investigates how ESP affects the behavior of clayey soil subjected to consolidation and swelling tests. It observed that the investigated soil compressibility index had a high value and decreased with the addition of ESP, evidencing further that soil is highly compressive. It found that a decrease in the compressibility index with an increased ESP increases the mechanical stability of the soil by reducing the capacity for compressive strain. The high swelling index with the addition of ESP shows a tendency toward possible swelling when contacting water. The initial addition of ESP increases the susceptibility of the soil to expansion, and the volumetric compressibility coefficient, mv, decreases. The addition of ESP increases the oedometric modulus E′ from 359 kN/m² to 1166 kN/m². The ESP has drastically enhanced the swelling characteristics of soil, having swelling pressures of 720 kN/m² at 0%, 750 kN/m² at 2%, 760 kN/m² at 5%, and 780 kN/m² at 8%.

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A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks

Cited by 3 publications

References 25 publications

Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Effect of addition of eggshell powder to improve the compressibility parameters of Doukane’s clay soils: a case study Tebessa-Algeria

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