Random finite element analysis on uplift bearing capacity and failure mechanisms of square plate anchors in spatially variable clay

Chen, Xuejian; Fu, Yong; Liu, Yong

doi:10.1016/j.enggeo.2022.106677

Cited by 44 publications

(3 citation statements)

References 49 publications

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“…It has been widely used in applications such as computer vision and speech processing [48][49][50][51]. In geotechnical engineering, variable and uncertain geological environments can cause challenges during the operation of the tunnelling process [52][53][54]. Although deep learning models used in tunnelling such as RNN, LSTM, GRU, and Conv1D [36,37,47,[55][56][57] show the ability to handle such complex data, they do not provide insights into which features of the data have the most influence on the predictions.…”

Section: Deep Learning Technique For Predicting Operation Parametersmentioning

confidence: 99%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

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Pipe jacking is a trenchless technology used to install buried pipelines, such as sewer lines in wastewater management systems. Existing mechanistic approaches based on geomaterial strength parameters (i.e., friction angle and apparent cohesion) can provide an estimation of the potential jacking forces during construction. However, extracting intact rock cores for strength characterisation is challenging when dealing with highly weathered ‘soft rocks’ which exhibit RQD values of zero. Such was the case for a pipe jacking drive traversing the highly weathered lithology underlying Kuching City, Malaysia. Furthermore, mechanistic approaches face limitations during construction when jacking forces are dependent on operation parameters, such as jacking speed and lubrication. To address these knowledge gaps, the primary objectives of this study are the development of rock strength parameters based on in situ pressuremeter testing for the purpose of estimating jacking forces. Furthermore, this study investigates the influence of various pipe jacking operation parameters, with a particular focus on their impact on jacking forces in weathered ‘soft rocks’. To achieve this, a novel deep learning model with an attention mechanism is introduced. The proposed methods of rock strength parameters derived from pressuremeter testing and the utilisation of deep learning will help to provide insights into the key factors affecting the development of jacking forces. This paper successfully shows the use of in situ pressuremeter testing in developing Mohr–Coulomb (MC) parameters directly from the site. In addition, the developed deep learning model with an attention mechanism successfully highlights the significance of pipe jacking operation parameters with an accuracy of 88% in predicting the jacking forces.

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Section: Deep Learning Technique For Predicting Operation Parametersmentioning

confidence: 99%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

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show abstract

“…In this study, a new type of real-time observation of NT-CEP double-pile half-section small-scale model undisturbed soil test method and ANSYS finite element software simulation analysis method were used for comparative analysis [13][14][15][16]. Two-, four-, six-, and nine-pile models encompassing the corner, side, and middle piles were set up using the test and ANSYS finite element software.…”

Section: Introductionmentioning

confidence: 99%

Influence of Plate Position on Uplift Failure State and Bearing Capacity of NT-CEP Pile Groups

Qian,

Chen,

Sun

et al. 2024

Advances in Civil Engineering

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The previous research results of the research group demonstrated that the plate position is the main factor affecting the uplift bearing capacity of the new type of concrete expanded plate pile (NT-CEP pile) group and the failure state of the soil around the pile. In this study, the visual half-section pile small-scale model of undisturbed soil tension test and ANSYS Finite Element Software comparative analysis two-pile small-scale test model and two-, four-, six-, and nine-pile models, which included the corner, side, and middle piles, were developed. The effect of the plate position on the displacement, stress, and bearing mechanism of the NT-CEP pile group under vertical tension was determined, which further improved the design concept of the NT-CEP pile group and provided strong theoretical support for its widespread application.

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“…However, the soil in most of these analyses is treated as an idealized material, with homogeneous or linear increases in shear strength. In nature, the inherent properties of soil deposits generally vary spatially [10][11][12][13][14][15][16][17][18]. It has been widely recognized that such spatial variability in soil parameters can cause the mechanical behaviors of offshore structures to deviate from the results of deterministic analyses.…”

Section: Introductionmentioning

confidence: 99%

Uplift Behavior of Pipelines Buried at Various Depths in Spatially Varying Clayey Seabed

et al. 2022

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The behavior of buried offshore pipelines subjected to upheaval buckling has attracted much attention in recent years. Numerous researchers have made great efforts to investigate the influence of different soil cover depth ratios, soil strengths and pipe-soil interfaces on failure mechanisms and bearing capacities during pipeline uplift. However, attention to soil spatial variability has been relatively limited. To address this gap, a random small-strain finite element analysis has been conducted and reported in this paper to evaluate the influence of the random distribution of soil strength on pipe uplift response. The validity of the numerical model was verified by comparison with the results presented in the previous literature. The spatial variation of soil strength was simulated by a random field. The effect of soil variability on the failure mechanism was determined by comparing the displacement contours of each random realization. Probabilistic analyses were performed on the random uplift capacity obtained by a series of Monte Carlo simulations, and the relationship between the failure probability and the safety factor was also determined. The findings of the present work might serve as a reference for the safety designs of pipelines.

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Random finite element analysis on uplift bearing capacity and failure mechanisms of square plate anchors in spatially variable clay

Cited by 44 publications

References 49 publications

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Influence of Plate Position on Uplift Failure State and Bearing Capacity of NT-CEP Pile Groups

Uplift Behavior of Pipelines Buried at Various Depths in Spatially Varying Clayey Seabed

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