Modelling the Shear Behaviour of Clean Rock Discontinuities Using Artificial Neural Networks

Neto, Silvrano Adonias Dantas; Indraratna, Buddhima; Oliveira, David; Assis, André Pacheco de

doi:10.1007/s00603-017-1197-z

Cited by 21 publications

(10 citation statements)

References 29 publications

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“…In order simplify the predictive process of the shearing behavior of discontinuities, eliminating many existing problems in the use of the current analytical proposals, it is worth mentioning some predictive models developed with artificial neural networks (ANN), fuzzy logic and neurofuzzy techniques, for instance the proposals by Dantas Neto et al (2017), Matos (2018), and Matos et al (2019a, b). Such models do not intend to replace other models or tests, but they present themselves as tools that can be used for estimating dilation and shear stress data of rock discontinuities enabling fast applications compatible with the day-today demands in engineering.…”

Section: Shear Behavior Of Rock Discontinuitiesmentioning

confidence: 99%

“…Nowadays Artificial Neural Networks (ANN) are the computer models most commonly used in different areas of knowledge (Schmidhuber, 2015). ANN are based on the functioning of the human brain and its capacity to perceive and learn complex, nonlinear and multivariate phenomena (Dantas Neto et al, 2017;Chen et al, 2018;Schmidhuber, 2015).…”

Section: Basic Conceptsmentioning

confidence: 99%

“…The majority of them contemplate the prediction of intact rock properties such as the uniaxial compressive strength (Grima et al, 2000; Mosh- refii et al, 2018), Young's modulus (Sonmez et al, 2016;Yilmaz & Yusek, 2008;Dehghan et al, 2010), and even tensile strength (Singh et al, 2001). Concerning the development of predictive models for rock discontinuities using MLP, it is worth mentioning the studies by Dantas Neto et al (2017) and Leite et al (2019).…”

Section: Applications Of the Artificial Neural Network In Rock Mechanicsmentioning

confidence: 99%

“…Nowadays, one of the increasingly used modelling techniques in geotechnical engineering to manage complex, multivariate and nonlinear phenomena are the artificial neural networks (ANN), especially those known as multilayer perceptrons (MLP). Many geotechnical applications have confirmed the efficiency of this tool in model-ling physical phenomena in geotechnics, such as, definition of longitudinal pavement defects (Farias et al, 2003), prediction of physical properties of asphalt materials for paving (Dantas Neto et al, 2004), development of models for estimating settlements in deep pile foundations (Amancio et al, 2014;Dantas Neto et al, 2014); predictive shear behavior in unfilled rock discontinuities (Dantas Neto et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A predictive model for the peak shear strength of infilled soft rock joints developed with a multilayer perceptron

Leite¹,

Neto²

2020

S&R

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Several analytical methodologies help estimate the shear strength of rock discontinuities whose main limitations are the difficulty to obtain all necessary parameters to satisfactorily represent the boundary conditions and influence of infill materials. The objective of this study is to present a predictive model of peak shear strength for soft rock discontinuities developed making use of an artificial neural network known as multilayer perceptron. The model's input variables are: normal stiffness; initial normal stress acting on the discontinuity; joint roughness coefficient (JRC); ratio t/a (fill thickness/asperity height); uniaxial compressive strength and the basic friction angle of the intact rock; and finally the internal friction angle of infill material. To do so, results from 115 direct shear tests, with different soft rock discontinuities conditions were used. The herein proposed ANN predictive model, with an architecture 7-20-1, have shown coefficient of correlation in training and validation of 99.8 % and 99 %, respectively. The results from the model satisfactorily fit the experimental data and were also able to represent the influence of the input variables on the peak shear strength of soft rock discontinuities for different infill and boundary conditions.

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Section: Shear Behavior Of Rock Discontinuitiesmentioning

confidence: 99%

Section: Basic Conceptsmentioning

confidence: 99%

Section: Applications Of the Artificial Neural Network In Rock Mechanicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A predictive model for the peak shear strength of infilled soft rock joints developed with a multilayer perceptron

Leite¹,

Neto²

2020

S&R

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“…The model by Indraratna and Haque (2000) is the most advanced existing model to predict the shear strength of clean rock joints and has the advantage of helping to predict shear stress and shear displacement under both CNL and CNS conditions. Similarly, Dantas Neto et al (2017) proposed a model using artificial neural network techniques, which enables the shear behavior of discontinuities to be completely defined without the need for any special laboratory test.…”

Section: Introductionmentioning

confidence: 99%

A Takagi-Sugeno fuzzy model for predicting the clean rock joints shear strength

2019

Self Cite

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It is well known that learning about the mechanical behavior of a fractured rock mass depends on the shear behavior of its discontinuities. Several studies have shown that the shear behavior of unfilled rock discontinuities depends on their boundary conditions, roughness characteristics and the properties of the joints walls. Currently, there are several analytical models that can be used to predict the shear behavior of clean rock joints. However, they are all purely deterministic in nature because their input variables are defined without considering the uncertainties inherent in the formative process of the rock mass and the discontinuity itself, i.e., they need an auxiliary tool to consider the variability of their parameters such as the Monte Carlo or Point Estimation Methods. Therefore, the purpose of this article is to present a model to predict the shear strength of clean rock joints incorporating uncertainties in the variables that govern their shear behavior with a zero-order Takagi-Sugeno fuzzy controller. The model is developed based on the results of 44 direct shear tests carried out on different joints. The model input variables are the normal boundary stiffness and initial normal stress acting on the joint, its roughness expressed by the JRC value, the uniaxial compressive strength and basic friction angle of the intact rock, as well as the shear displacement imposed on the joint. The results showed that the predicted shear strength of clean rock joints obtained by the fuzzy model fit the experimental data satisfactorily and helped define the shear behavior of the discontinuity.

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Determination of surface roughness of rocks based on 2D profiles using machine learning methods

Pakdaman,

Moosavi

2023

Arch Appl Mech

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Modelling the Shear Behaviour of Clean Rock Discontinuities Using Artificial Neural Networks

Cited by 21 publications

References 29 publications

A predictive model for the peak shear strength of infilled soft rock joints developed with a multilayer perceptron

A predictive model for the peak shear strength of infilled soft rock joints developed with a multilayer perceptron

A Takagi-Sugeno fuzzy model for predicting the clean rock joints shear strength

Determination of surface roughness of rocks based on 2D profiles using machine learning methods

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