Rock mass classification method applying neural networks to minimize geomechanical characterization in underground Peruvian mines

Brousset, Julyans; Pehovaz, Humberto; Quispe, Grimaldo; Raymundo, Carlos; Moguerza, Javier M.

doi:10.1016/j.egyr.2023.05.246

Cited by 5 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From structural engineering to multi-scale modeling and geotechnical engineering, as well as hydraulics and transportation, all subtopics of infrastructure design have been employing NNs to reduce the analyses needed for the design and response prediction of physical and mechanical systems. For the investigation of rock mass characteristics and neural networks, scientific publications imply the computational machine learning tools, among other methods, for tasks such as rock mass classification [18,19], predicting the bearing capacity of pile tips embedded rock masses [20], combining and comparing results with fuzzy and genetic programming [21,22], and estimating abutments stresses through neural network models using data obtained from numerical analyses [23]. Most of these have the advantage that they fit with the given data and their estimations are fairly reliable.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

Savvides,

Antoniou,

Papadopoulos

et al. 2023

Geotechnics

View full text Add to dashboard Cite

Rock mechanics and the estimation of their material properties through field tests are important aspects and challengees in civil and geotechnical engineering. However, this procedure is expensive and difficult to attain, while the machine learning and neural network theory provide a computational tool for estimating the material properties with limited data. In this work, an estimation of the Young Modulus and the cohesion of a clayey-originated rock through feed-forward neural networks constructed from in situ data measurements is given. The input values come from the Geological Strength Index (GSI) proposed values of the point load index Is50, the uniaxial compression strength σs, as well as the specific gravity γ of the rock mass. The convergence analysis revealed that the convergence occurs at approximately 2000 epochs, with the largest L2 mean square error norm being no greater than 10−5. In addition, it is demonstrated that augmenting γ results in the estimation of rock that is stiffer and stronger. The aforementioned increase in the specific site may be up to 20% for the stiffness and up to 25% for the cohesion. This model, aside from readability and accuracy, offers the convenience of enriching it with more in situ data, thereby enhancing the flexibility of the proposed numerical tool proposed. However, its applicability is limited to the specific data acquired from the particular site, so a more general estimation requires a substantially larger dataset. Finally, the justification of the proposed model has been carried out based on suggestions from the literature for common values of clayey-oriented rock, which is fairly disintegrated as seen in the field.

show abstract

Section: Introductionmentioning

confidence: 99%

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

Savvides,

Antoniou,

Papadopoulos

et al. 2023

Geotechnics

View full text Add to dashboard Cite

show abstract

Developing rock mass classification method using precise description of joints

Jin,

Liang,

Cui

et al. 2023

Environ Earth Sci

View full text Add to dashboard Cite

Rock mass classi cation is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classi cation methods in that they only collect joint information from onedimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in threedimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classi cation method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and eldapplicable and also reduces the in uence of subjective factors on rock mass quality evaluation and enhances the classi cation reliability.

show abstract

Is exponential stability achievable in singular perturbed delayed systems with time-varying parameters? A comprehensive analysis

Chen,

Ouyang,

Zhang

et al. 2024

Heliyon

View full text Add to dashboard Cite

Rock mass classification method applying neural networks to minimize geomechanical characterization in underground Peruvian mines

Cited by 5 publications

References 14 publications

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

Developing rock mass classification method using precise description of joints

Is exponential stability achievable in singular perturbed delayed systems with time-varying parameters? A comprehensive analysis

Contact Info

Product

Resources

About