Experimental Study of the Bearing Characteristics of a Novel Energy-Saving and Environmentally Friendly Pile: Drilling with Prestressed Concrete Pipe Cased Piles

Hou, Zhenkun; Liu, Yupeng; Han, Zejun; Tang, Mengxiong; Gong, Xing; Su, Dingli; Wang, Lei

doi:10.1061/ijgnai.gmeng-9062

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…(2) Estimate the peak shear strength of marine soft clay sediment based on the soil sample properties and test conditions using Equation (7). (3) Compare the peak shear strength obtained from the analytical equation and machine learning model, with the results illustrated in Figure 11 [91][92][93][94][95]. As shown in Figure 11, the peak shear strengths of marine soft clay sediments predicted using the analytical equations are very close to the peak shear strengths obtained from laboratory tests [96][97][98].…”

Section: Validation Of the Predicting Performance With The Physical T...mentioning

confidence: 99%

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hu,

Li,

Wang

et al. 2024

Water

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In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.

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Section: Validation Of the Predicting Performance With The Physical T...mentioning

confidence: 99%

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Hu,

Li,

Wang

et al. 2024

Water

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“…Marine clayey sediment is extensively distributed worldwide [1][2][3][4]. The unfavorable behavior of marine clay deposits has been exposed over the past few years during the building of marine engineering indicates which has a detrimental impact on the dependability of engineering projects by causing issues such as sliding and splitting [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Cui,

Zhou,

Gao

et al. 2024

Water

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In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

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A Practical Swelling Constitutive Model of Anhydrite and Its Application on Tunnel Engineering

Wu,

Lin,

Zhou

et al. 2024

Advances in Materials Science and Engineering

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Swelling of anhydrite rock causes serious damage to the tunnel and generates high additional costs in the process of tunnel construction and operation and has gradually become one of the main factors that threaten the safety of the tunnel. It is extremely difficult to predict swelling pressures and deformations accurately based on conventional swelling constitutive models. Thus, a new practical swelling constitutive model of anhydrite for tunnel engineering has been developed. First, swelling tests of natural anhydrite samples focusing on the time effect have been designed and conducted, whose test results show that swelling strain-time can be described by the S-curve model and that swelling stress-strain can be described by the quadratic model. Second, a swelling constitutive model with considering the time effect has been developed to reproduce the swelling behavior of anhydrite observed in swelling tests. This model can track the evolution of swelling activity in tunneling, which has practical significance for process simulation and process control of swelling disaster. Then, this model has been implemented within ANSYS for numerical simulation of the Lirang tunnel. Based on simulation results, useful measures have been proposed. Satisfactory results have been achieved according to the feedback from the site.

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Experimental Study of the Bearing Characteristics of a Novel Energy-Saving and Environmentally Friendly Pile: Drilling with Prestressed Concrete Pipe Cased Piles

Cited by 7 publications

References 24 publications

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

A Practical Swelling Constitutive Model of Anhydrite and Its Application on Tunnel Engineering

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