A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques

Ekin Köken; Tümay Kadakci Koca

Article Details

A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques

Authors:

Ekin Köken | Tümay Kadakci Koca

Publisher:

Murat YAKAR

Main Topics:

Engineering

Journal:

Turkish Journal of Engineering Vol.7 Issue 4, pp.296-305

Document Type:

Article

Publication Date:

5/2023

Document Language:

English

Document ID (Org No.):

2001284478

pISSN:

2587-1366

Keywords:

Abstract:

 Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness KIC was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength UCS and Brazilian tensile strength BTS. Several soft computing techniques such as artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, gene expression programming GEP, and multivariate adaptive regression spline MARS were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value R2 for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies.

 Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness KIC was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength UCS and Brazilian tensile strength BTS. Several soft computing techniques such as artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, gene expression programming GEP, and multivariate adaptive regression spline MARS were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value R2 for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies.

 Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness KIC was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength UCS and Brazilian tensile strength BTS. Several soft computing techniques such as artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, gene expression programming GEP, and multivariate adaptive regression spline MARS were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value R2 for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies.

 Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness KIC was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength UCS and Brazilian tensile strength BTS. Several soft computing techniques such as artificial neural network ANN, adaptive neuro-fuzzy inference system ANFIS, gene expression programming GEP, and multivariate adaptive regression spline MARS were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value R2 for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies.

Article Details

A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques

Authors:

Ekin Köken | Tümay Kadakci Koca

Publisher:

Murat YAKAR

Main Topics:

Engineering

Journal:

Turkish Journal of Engineering Vol.7 Issue 4, pp.296-305

Document Type:

Article

Publication Date:

5/2023

Document Language:

English

Document ID (Org No.):

2001284478

pISSN:

2587-1366

Mode-I fracture toughness

Uniaxial compressive strength

Brazilian tensile strength

Soft computing techniques

Issues

Article Details

A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques

Authors: Ekin Köken | Tümay Kadakci Koca

Publisher: Murat YAKAR

Main Topics: Engineering

Journal: Turkish Journal of Engineering Vol.7 Issue 4, pp.296-305

Document Type: Article

Publication Date: 5/2023

Document Language: English

Document ID (Org No.): 2001284478

pISSN: 2587-1366

Mode-I fracture toughness

Uniaxial compressive strength

Brazilian tensile strength

Soft computing techniques

Issues

Authors:

Ekin Köken | Tümay Kadakci Koca

Publisher:

Murat YAKAR

Main Topics:

Engineering

Journal:

Turkish Journal of Engineering Vol.7 Issue 4, pp.296-305

Document Type:

Article

Publication Date:

5/2023

Document Language:

English

Document ID (Org No.):

2001284478

pISSN:

2587-1366