Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks

Intelligent Systems with Applications, Год журнала: 2024, Номер 21, С. 200329 - 200329

Опубликована: Янв. 13, 2024

In underground space technology, the issue of tunnel stability is a fundamental concern that significantly causes catastrophe. Owing to sedimentation and deposition processes, strengths clays are anisotropic, where magnitudes undrained shear in vertical horizontal directions different. The anisotropic (AUS) model effective at considering anisotropy clayey soils when analyzing geotechnical issues. This study aims assess rectangular tunnels by adjusting dimensionless overburden factor, cover-depth ratio, width-depth ratio clay with various strength ratios. analysis these involves employing finite element limit AUS identify planes soil collapse response aforementioned variations. addition, this presents development soft-computing models utilizing artificial neural networks (ANNs) forecast across combinations input parameters. findings presented form design charts, tables, facilitate practical applications.

Язык: Английский

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Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

Transportation Geotechnics, Год журнала: 2024, Номер 48, С. 101347 - 101347

Опубликована: Авг. 23, 2024

Язык: Английский

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Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104323 - 104323

Опубликована: Фев. 1, 2025

Язык: Английский

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Stability evaluation of elliptical tunnels in natural clays by integrating FELA and ANN

Results in Engineering, Год журнала: 2023, Номер 19, С. 101280 - 101280

Опубликована: Июль 4, 2023

The stability of tunnels in clayey soil is a major concern for underground space technology. Clay has anisotropy shear strength induced by depositional and sedimentation processes. For the numerical analysis geotechnical problems, anisotropic undrained (AUS) model can account this soils. In study, elliptical tunnel (stability factor: σs-σt/suc) with varying shape (width-depth ratio: B/D) placed at different embedment depths (cover-depth C/D) clay (anisotropic re) dimensionless overburden factor (overburden γD/suc) evaluated using finite element limit AUS model. failure planes are also above variations. Based on outcome, artificial neural network (ANN) utilized to establish equation predicting shapes (i.e., width-depth ratio), overburden, cover-depth ratio, ratio clay. present study results presented as design charts, tables, equations so that they be used practice.

Язык: Английский

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Data-driven modelling of bearing capacity of footings on spatially random anisotropic clays using ANN and Monte Carlo simulations

International Journal of Geotechnical Engineering, Год журнала: 2024, Номер unknown, С. 1 - 17

Опубликована: Июнь 11, 2024

The fundamental issue of bearing capacity footings on anisotropic clays holds significant importance in geotechnical engineering. Previous investigations predominantly focused deterministic analyses, disregarding the spatial variability soil. A probabilistic analysis is conducted this paper, incorporating clays. To achieve this, Random Adaptive Finite Element Limit Analysis (RAFELA) and Monte Carlo simulations are utilised to capture full spectrum potential outcomes under parametric uncertainty. impact soil strength explored across three input parameters such as ratios, coefficients variation, dimensionless correlation lengths. In order establish surrogate models capable predicting random clays, Artificial Neural Network (ANN) developed. use proposed ANN presents a more convenient computationally efficient approach for ultimate vertical load spatially

Язык: Английский

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Application of artificial neural networks for predicting lateral and uplift capacity of buried rectangular box carrying pipelines

Marine Georesources and Geotechnology, Год журнала: 2024, Номер 42(12), С. 1907 - 1923

Опубликована: Янв. 5, 2024

Urban and offshore subterranean comprehensive pipelines are currently utilized frequently as infrastructure for fitting different engineering used electricity, signal transmission, natural gas, petroleum, heating, water supply, drainage systems. This study utilizes the finite element limit analysis (FELA) artificial neural network (ANN) to evaluate pull-out capacity factor of buried rectangular box carrying with internal inclined force in cohesive soil. In FELA, rigorous upper bound lower solutions performed achieve exact result. this study, a dimensionless parametric is carried out by considering effect five parameters viz. depth ratio, width-depth overburden factor, inclination angles, interface on box, which can be called pipeline. Two significant samples investigated selected soil failure pattern (failure envelope) using shear dissipation pipeline subjected loading. Using FELA results, predictive equations proposed ANN, then sensitivity examine each essential parameter stability underground The ANN model pipe loading presented design charts practice further investigation.

Язык: Английский

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Soft Computing-Based Models for Estimating the Ultimate Bearing Capacity of an Annular Footing on Hoek–Brown Material

Arabian Journal for Science and Engineering, Год журнала: 2023, Номер 49(4), С. 5989 - 6006

Опубликована: Дек. 28, 2023

Язык: Английский

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An ANN-based model for constructing undrained failure envelopes for circular piles under general loading

Ships and Offshore Structures, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Янв. 30, 2025

Язык: Английский

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Coupled Finite Element Analysis and Multivariate Adaptive Regression Splines for predicting the bearing capacity of conical footings on slopes

Journal of Physics Conference Series, Год журнала: 2025, Номер 2949(1), С. 012025 - 012025

Опубликована: Фев. 1, 2025

Abstract Exploring the behavior of conical footings is a significant aspect geotechnical engineering, particularly in supporting wind turbine towers on mountain slopes. This study employs Finite Element Analysis (FEA) within Plaxis 3D to investigate footings. The Mohr-Coulomb material assumed, and research focuses two pivotal parameters soil shear strength: c (cohesion) φ (friction angle). aims assess impacts geometry parameters, consisting angle ( α ), setback ratio b/B slope β failure mechanisms footing. Furthermore, Multivariate Adaptive Regression Splines (MARS) models are implemented with FEA dataset propose predictive formula UBC (ultimate bearing capacity) factor, indicating relationship between these five input outcome UBC. A sensitivity analysis was also examined, revealing contribution each variable

Язык: Английский

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Evaluating the Bearing Capacity of Conical Foundations on Dense Sand ~ FELA, Bolton Yield Criterion, and Hybrid ANFIS Algorithms

International Journal of Geosynthetics and Ground Engineering, Год журнала: 2025, Номер 11(3)

Опубликована: Июнь 1, 2025

Язык: Английский

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