Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network

Ocak, Ibrahim; Şeker, Şadi Evren

doi:10.1007/s00603-012-0236-z

Cited by 55 publications

(9 citation statements)

References 15 publications

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“…Finally the evaluations of feature hashing methods are applied on the author recognition via the classification algorithms, k-nearest neighborhood (KNN) [9]. The results are evaluated via the root mean square error (RMSE) [9] and relative absolute error (RAE) [10].…”

Section: ) Levenshtein Distance 2) Jaccard Index 3) Maxkfreqhashingmentioning

confidence: 99%

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A Novel String Distance Function Based on Most Frequent K Characters

Şeker¹,

Altun²,

Ayan³

et al. 2014

IJMLC

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Abstract-This study aims to publish a novel similarity metric to increase the speed of comparison operations. Also the new metric is suitable for distance-based operations among strings.Most of the simple calculation methods, such as string length are fast to calculate but doesn't represent the string correctly. On the other hand the methods like keeping the histogram over all characters in the string are slower but good to represent the string characteristics in some areas, like natural language.We propose a new metric, easy to calculate and satisfactory for string comparison.Method is built on a hash function, which gets a string at any size and outputs the most frequent K characters with their frequencies.The outputs are open for comparison and our studies showed that the success rate is quite satisfactory for the text mining operations.

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Section: ) Levenshtein Distance 2) Jaccard Index 3) Maxkfreqhashingmentioning

confidence: 99%

“…The results are evaluated via the root mean square error (RMSE) [9] and relative absolute error (RAE) [10]. …”

Section: ) Levenshtein Distance 2) Jaccard Index 3) Maxkfreqhashingmentioning

confidence: 99%

A Novel String Distance Function Based on Most Frequent K Characters

Şeker¹,

Altun²,

Ayan³

et al. 2014

IJMLC

Self Cite

View full text Add to dashboard Cite

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“…erefore, the study of the elastic modulus of fractured rock mass is more suitable for engineering practice than the complete rock mass. Most scholars carried out many works to obtain more practical elastic modulus, such as empirical estimation method [2][3][4][5][6][7][8], acoustic wave test method [9,10], and in situ test method [11]. However, the empirical estimation method is subject to subjective factors, which leads to the estimation results vary with different engineers.…”

Section: Introductionmentioning

confidence: 99%

Calculation of Elastic Modulus for Fractured Rock Mass Using Dimensional Analysis Coupled with Numerical Simulation

Ren

Zhang

Ouyang

et al. 2021

Mathematical Problems in Engineering

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Underground mining activities make the fractures in the natural rock mass develop randomly. The elastic modulus of the fractured rock mass Em is changed with the redistribution and development of the fractures. An equivalent model of fractured rock mass is structured to represent the hydraulic conductivity and the rock mass strain because of the continuum theory. Dimensional analysis is very useful to build relationship of the parameters in complex physical phenomena. Based on the engineering phenomenon of groundwater flowing into the goaf along the fracture in the rock mass, a fuzzy expression among parameters such as the parameter Em, the seepage flow Q, and the exposed area of the goaf A is obtained using dimensionless analysis. To calculate the parameter Em, the fuzzy relationship is then characterized by Darcy’s law and numerical simulation. Under the scripting environment of Python, an automated program to realize the numerical simulation of all scenarios is established, which also provides convenience for drawing the dimensionless flux charts. The results show that the parameter Em can be calculated by the dimensional analysis coupled with numerical simulation. In addition, the parameter Em decreases with the increase of the parameter Q, and the integrity of rock mass is also worse. Finally, a mine example is used to verify the feasibility of the method.

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“…Mert et al (2011) and Gholami et al (2013) presented an approach to assess the total RMR classification system using a simulation-based on neural networks. Ocak and Seker (2012) developed a neural network to estimate the elastic modulus of intact rocks, since its difficult determination in Fig. 1 Failure mechanisms for shallow foundation: a general shear failure; b local failure through large spacing discontinuities; c columnar failure; d punching shear failure laboratory tests because high-quality cores are required.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

2021

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Calculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

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Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network

Cited by 55 publications

References 15 publications

A Novel String Distance Function Based on Most Frequent K Characters

A Novel String Distance Function Based on Most Frequent K Characters

Calculation of Elastic Modulus for Fractured Rock Mass Using Dimensional Analysis Coupled with Numerical Simulation

Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

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