Predicting the Shear Strength of Unfilled Rock Joints with the First-Order Takagi-Sugeno Fuzzy Approach

Matos, Yago Machado Pereira de; Neto, Silvrano Adonias Dantas; Barreto, Guilherme A.

doi:10.28927/sr.421021

Cited by 3 publications

(2 citation statements)

References 31 publications

(42 reference statements)

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“…The correlation coefficient between the shear stress and dilation output by the model and the test sample reached 0.99 which shows that the model had good capability of predicting the shear behavior of rock. Matos et al (Matos et al, 2019) pointed out the same problem. The author argued that previous analytical models that predicted the shear behavior of unfilled rock joints could not take into account the uncertainties inherent in the rock formation process.…”

Section: Methods Used Referencementioning

confidence: 83%

“…Previous studies (Papaliangas et al, 1993;Sklavounos and Sakellariou, 1995;Indraratna et al, 1999;Wang et al, 2000;Jin et al, 2006;Vardakos. et al, 2007;Tiryaki, 2008;Zorlu et al, 2008;Ceryan et al, 2012;Samani and Bafghi, 2012;Yagiz et al, 2012;Rajesh Kumar et al, 2013;Zheng et al, 2013;Gu et al, 2015;Liu et al, 2015;Song et al, 2015;Wang et al, 2015;Raissi et al, 2018;Matos et al, 2019;Almajid and Abu-Alsaud, 2021;Dantas Neto et al, 2021;Deng and Pan, 2021;Haghighat et al, 2021;Hasanipanah et al, 2021;Fathipour-Azar, 2022;Garg et al, 2022;Li and Chen, 2022;Mahmoodzadeh et al, 2022) have shown that machine learning methods can provide new ideas for rock mechanics problems, and this issue is no exception (Ghaboussi and Sidarta, 1998;Jaksa. and Maier., 2009).…”

Section: Determining Constitutive Behaviorsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for rock mechanics problems; an insight

Taleghani

Balushi

et al. 2022

Front. Mech. Eng.

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Due to inherent heterogeneity of geomaterials, rock mechanics involved with extensive lab experiments and empirical correlations that often lack enough accuracy needed for many engineering problems. Machine learning has several characters that makes it an attractive choice to reduce number of required experiments or develop more effective correlations. The timeliness of this effort is supported by several recent technological advances. Machine learning, data analytics, and data management have expanded rapidly in many commercial sectors, providing an array of resources that can be leveraged for subsurface applications. In the last 15 years, deep learning in the form of deep neural networks, has been used very effectively in diverse applications, such as computer vision, seismic inversion, and natural language processing. Despite the remarkable success in these and related areas, deep learning has not yet been widely used in the field of scientific computing specially when it comes to subsurface applications due to the lack of large amount of data to train algorithms. In this paper, we review such efforts and try to envision future game-changing advances that may impact this field.

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Section: Methods Used Referencementioning

confidence: 83%

Section: Determining Constitutive Behaviorsmentioning

confidence: 99%

Machine learning for rock mechanics problems; an insight

Taleghani

Balushi

et al. 2022

Front. Mech. Eng.

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A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm

Hong

Nguyen

Bui

et al. 2021

Engineering with Computers

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Risk assessment in a rock slope stability analysis using a Mamdani fuzzy controller

Matos,

Dantas Neto,

Barreto

2023

REM, Int. Eng. J.

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The shearing behavior of discontinuities is one of the factors with major influence on rock slope stability analysis, which is predictable when adopting different analytical models. However, these methodologies, generally of a purely deterministic nature, do not allow an assessment of the influence of the variability of the input parameters of the models on the shear behavior of unfilled rock discontinuities and, consequently, on the risk involved in the rock slope stability analyses. The purpose of this article is to present a methodology for rock slope risk assessment based on the development of a model to predict the shear behavior of unfilled rock discontinuity considering the variability of its input parameters, using a Mamdani fuzzy controller. The input variables of the model are the boundary normal stiffness and initial normal stress acting on the discontinuity, its roughness, the uniaxial compressive strength, the basic angle of friction of the intact rock and the shear displacement imposed on the discontinuity. The model outputs are the membership functions for the shear strength and dilation of the unfilled rock discontinuity, and from which the membership function can be defined for the factor of safety of the rock slope considering the failure mechanism governed by discontinuity. The results reveal the use and importance of fuzzy logic and fuzzy number operations in assessing the risk in rock slopes and may be used even in situations where there are major uncertainties on the existing information of the characteristics of the unfilled discontinuities.

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Predicting the Shear Strength of Unfilled Rock Joints with the First-Order Takagi-Sugeno Fuzzy Approach

Cited by 3 publications

References 31 publications

Machine learning for rock mechanics problems; an insight

Machine learning for rock mechanics problems; an insight

A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm

Risk assessment in a rock slope stability analysis using a Mamdani fuzzy controller

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