Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

2016 ◽  
Vol 30 (6) ◽  
pp. 04016036 ◽  
Author(s):  
Jui-Sheng Chou ◽  
Kuo-Hsin Yang ◽  
Jie-Ying Lin
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Peak Shear Strength ◽  
Fiber Reinforced ◽  
Reinforced Soils ◽  
Discrete Fiber

scholarly journals Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Peak Shear Strength Along Soil-Geocomposite Drainage Layer Interfaces

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Zhiming Chao ◽  
Gary Fowmes ◽  
S. M. Dassanayake
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Empirical Equation ◽  
Soil Layer ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mechanism Analysis ◽  
Peak Shear Strength ◽  
Engineering Structures

AbstractPeak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot’s Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique.

scholarly journals Effect of Polypropylene Fibers on the Shear Strength–Dilation Behavior of Compacted Lateritic Soils

2021 ◽  
Vol 13 (22) ◽  
pp. 12603
Author(s):  
Maitê Rocha Silveira ◽  
Sabrina Andrade Rocha ◽  
Natália de Souza Correia ◽  
Roger Augusto Rodrigues ◽  
Heraldo Luiz Giacheti ◽  
...  
Keyword(s):  
Shear Strength ◽  
Constitutive Models ◽  
Friction Angle ◽  
Polypropylene Fibers ◽  
Fiber Reinforced ◽  
Lateritic Soils ◽  
Reinforced Soils ◽  
Direct Shear Tests ◽  
Fibers Orientation ◽  
Soil Mix

The stress–dilatancy relationship for fiber-reinforced soils has been the focus of recent studies. This relationship can be used as a foundation for the development of constitutive models for fiber-reinforced soils. The present study aims to investigate the effect of recycled polypropylene fibers on the shear strength–dilation behavior of two lateritic soils using the stress–dilatancy relationship for direct shear tests. Results show that fibers improved the shear strength behavior of the composites, observed by increases in the friction angle. Fibers’ orientation at the sheared interface could be observed. The volumetric change during shearing was altered by the presence of fibers in both soils. Overall, results indicate that the stress–dilatancy relationship is affected by inclusions in the soil mix. Results can be used to implement constitutive modeling for fiber-reinforced soils.

scholarly journals Shear Strength Characteristics of Ribbed Short Fiber-reinforced Soils

2001 ◽  
Vol 16 ◽  
pp. 9-12 ◽  
Author(s):  
Katsuhiko MAKIUCHI ◽  
Kunio MINEGISHI ◽  
Shingo YAMAGUCHI ◽  
Ikuo HOSHINO
Keyword(s):  
Shear Strength ◽  
Short Fiber ◽  
Strength Characteristics ◽  
Fiber Reinforced ◽  
Reinforced Soils

scholarly journals Effects of Fiber Properties on Shear Strength of Short Fiber-reinforced Soils

2000 ◽  
Vol 15 ◽  
pp. 38-43
Author(s):  
Katsuhiko MAKIUCHI ◽  
Kunio MINEGISHI ◽  
Shingo YAMAGUCHI
Keyword(s):  
Shear Strength ◽  
Short Fiber ◽  
Fiber Reinforced ◽  
Reinforced Soils ◽  
Fiber Properties

Prediction of shear strength of soft soil using machine learning methods

CATENA ◽  
2018 ◽  
Vol 166 ◽  
pp. 181-191 ◽  
Author(s):  
Binh Thai Pham ◽  
Le Hoang Son ◽  
Tuan-Anh Hoang ◽  
Duc-Manh Nguyen ◽  
Dieu Tien Bui
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Soft Soil ◽  
Learning Methods ◽  
Machine Learning Methods

Shear strength of reinforced concrete beams with a fiber concrete matrix

2006 ◽  
Vol 33 (6) ◽  
pp. 726-734 ◽  
Author(s):  
Fariborz Majdzadeh ◽  
Sayed Mohamad Soleimani ◽  
Nemkumar Banthia
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Fiber Volume Fraction ◽  
Volume Fraction ◽  
Fiber Reinforcement ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Rc Beams ◽  
Fiber Reinforced ◽  
Fiber Volume

The purpose of this study was to investigate the influence of fiber reinforcement on the shear capacity of reinforced concrete (RC) beams. Both steel and synthetic fibers at variable volume fractions were investigated. Two series of tests were performed: structural tests, where RC beams were tested to failure under an applied four-point load; and materials tests, where companion fiber-reinforced concrete (FRC) prisms were tested under direct shear to obtain material properties such as shear strength and shear toughness. FRC test results indicated an almost linear increase in the shear strength of concrete with an increase in the fiber volume fraction. Fiber reinforcement enhanced the shear load capacity and shear deformation capacity of RC beams, but 1% fiber volume fraction was seen as optimal; no benefits were noted when the fiber volume fraction was increased beyond 1%. Finally, an equation is proposed to predict the shear capacity of RC beams.Key words: shear strength, fiber-reinforced concrete, RC beam, stirrups, energy absorption capacity, steel fiber, synthetic fiber.

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