Effect of polypropylene fibers on internal erosional behavior of poorly graded sandy soil stabilized with the binary mixtures of clay and polyvinyl acetate

Vakili, Amir Hossein; Rastegar, Shahram; Golkarfard, Hossein; Salimi, Mahdi; Izadneshan, Zahra

doi:10.1007/s12665-023-10961-9

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…As the fiber content increased, the friction between particles increased, considerably increasing the peak stress. However, excessive fiber content leads to mutual entanglement and agglomeration of fibers [59,60], which inhibits the fiber reinforcement effect and reduces the peak stress. Therefore, the amount of fiber incorporated should be considered in an actual project to achieve the best reinforcement effect.…”

Section: Test Programmentioning

confidence: 99%

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

Wang,

He,

Tang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Basalt fibers are a reinforcing material with excellent mechanical properties and durability. In contrast, although Beibu Gulf sea sand is widely in engineering, it exhibits low strength and poor stability, which can be improved by adding basalt fibers. In this study, the effects of fiber content, fiber length, and effective confining pressure on the static shear strength of fiber-reinforced sea sand were investigated using a triaxial shear test. The maximum improvement on the static shear characteristics and deformation resistance of sea sand were achieved for a fiber content and length of 0.8% and 12 mm, respectively. The cohesion and internal friction angle of sea sand were improved and the secant modulus and strain before and after basalt fiber reinforcement showed a nonlinear attenuation tendency. The reinforcement effect coefficient R and the basalt fiber content under different dosages were in accordance with the law of the Gaussian function. The value of R conformed to a linear growth and exponential function law under different fiber lengths and effective confining pressures, respectively. This study provides a solid theoretical basis for the sustainable utilization of sea sand resources and fiber reinforcement for road and coastal protection engineering in the Beibu Gulf region.

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Section: Test Programmentioning

confidence: 99%

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

Wang,

He,

Tang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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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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Shale mineral particles directional arrangement and its effect on the mesoscopic failure mechanism

Li,

Wang,

Xue

et al. 2024

Bull Eng Geol Environ

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Effect of polypropylene fibers on internal erosional behavior of poorly graded sandy soil stabilized with the binary mixtures of clay and polyvinyl acetate

Cited by 7 publications

References 57 publications

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

Analysis of basalt fiber reinforcement on static shear properties of Beibu Gulf sea sand

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

Shale mineral particles directional arrangement and its effect on the mesoscopic failure mechanism

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