Predicting mode-I fracture toughness of rocks using soft computing and multiple regression

Roy, Debanjan Guha; Singh, T. N.; Kodikara, Jayantha

doi:10.1016/j.measurement.2018.05.069

Cited by 33 publications

(7 citation statements)

References 53 publications

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“…When comparing the performance of the established soft computing models with the ones previously proposed by Guha Roy et al [23] and Afrasiabian and Eftekhari [24], it is clear to state that the proposed ANN model is better than the GEP models proposed by Afrasiabian and Eftekhari [24]. Nevertheless, the GEP model in this study was not as successful as the GEP model proposed by Afrasiabian and Eftekhari [24].…”

Section: Discussionmentioning

confidence: 49%

“…On the other hand, the proposed models presented a lower performance than the models proposed by Guha Roy et al [23]. The reason for this phenomenon can be attributed to the fact that the dataset used in this study involves different rock types unlike the dataset of Guha Roy et al [23] and also input parameters that were integrated into the soft computing analyses are different. It is certain that as the variety of the rock types increases, the model performances may decrease.…”

Section: Discussionmentioning

confidence: 72%

“…To deal with larger datasets with different rock origins, soft computing tools would enable one to establish comprehensive predictive models. Based on a comprehensive literature survey, only two studies by Guha Roy et al [23] and Afrasiabian and Eftekhari [24] proposed some soft computing-based predictive models for the evaluation of KIC for different rock types (Table 2). It is clear to figure out that using soft computing tools for the evaluation of KIC for different rock types has not been much studied.…”

Section: Introductionmentioning

confidence: 99%

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

Köken

Koca

2023

Turkish Journal of Engineering

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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 and independent variables should be increased to improve the established predictive models in future studies.

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Section: Discussionmentioning

confidence: 49%

Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

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

Köken

Koca

2023

Turkish Journal of Engineering

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“…The applications of ANN for predicting fracture toughness include studies by Wiangkham et al [16] on polymethyl methacrylate (PMMA), Hamdia et al [17] on polymer nanocomposites (PNCs), Guha Roy et al [18] on rocks, and Liu et al [19] on Nb-Si alloys. On the other hand, the applications of ANN for predicting stress intensity factors include studies by Muñoz-Abella et al [20] on unbalanced rotating cracked shafts, Wu et al [21] on cracked pavements under traffic loading, and Li et al [22] on through-thickness cracks in bending tubes.…”

Section: Introductionmentioning

confidence: 99%

Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network

Seenuan,

Noraphaiphipaksa,

Kanchanomai

2023

Applied Sciences

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During pipeline operation, internal cracks may occur. The severity around the crack tip can be quantified by the stress intensity factor (KI), which is a linear–elastic fracture mechanics parameter. For pressurized pipes featuring infinitely long internal surface cracks, KI can be interpolated from a function considering pressure, geometry, and crack size, as presented in API 579-1/ASME FFS-1. To enhance KI prediction accuracy, an artificial neural network (ANN) model was developed for such pressurized pipes. Predictions from the ANN model and API 579-1/ASME FFS-1 were compared with precise finite element analysis (FEA). The ANN model with an eight-neuron sub-layer outperformed others, displaying the lowest mean squared error (MSE) and minimal validation discrepancies. Nonlinear validation data improved both MSE and testing performance compared to uniform validation. The ANN model accurately predicted normalized KI, with differences of 2.2% or lower when compared to FEA results. Conversely, API 579-1/ASME FFS-1′s bilinear interpolation predicted inaccurately, exhibiting disparities of up to 4.3% within the linear zone and 24% within the nonlinearity zone. Additionally, the ANN model effectively forecasted the critical crack size (aC), differing by 0.59% from FEA, while API 579-1/ASME FFS-1′s bilinear interpolation underestimated aC by 4.13%. In summary, the developed ANN model offers accurate forecasts of normalized KI and critical crack size for pressurized pipes, providing valuable insights for structural assessments in critical engineering applications.

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“…Hence, K IC prediction, in terms of rock properties, can substitute testing when needed [7,14,15]. Many researchers have suggested empirical formulas for the K IC estimation, in terms of rock properties such as Tensile Strength (TS) [9,16], Uniaxial Compressive Strength (UCS) [14,17], point Load Strength [18], Young's modulus [17,19], Poisson's ratio [17], P-wave velocity [14,17,20], S-wave velocity, density [14,17,21], porosity [17] and permeability [6].…”

Section: Introductionmentioning

confidence: 99%

Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices

Ameen,

Elwageeh,

Abdelaziz

et al. 2023

Applied Sciences

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Mode I fracture toughness (KIC) and rock brittleness are important properties that influence many rock engineering applications. Due to the difficulties in determining KIC experimentally, previous studies have investigated the relationship between KIC and rock brittleness indices. However, only rock brittleness indices (based on strength parameters) and KIC obtained from Chevron Bend and Short Rod test methods were considered. In this paper, regression analysis was carried out to investigate the relationship between KIC and rock brittleness using literature data collected from different rock types and core KIC test methods under level I and static test conditions. Rock brittleness was assessed using ten indices based on strength and pre-peak elastic parameters. The results showed that elastic-based indices were not good predictors of KIC, while strength-based indices correlated well with KIC. A comparison with previous studies revealed that the correlations between KIC and strength-based indices were significantly sensitive to the rock type, i.e., soft or hard, and the KIC test method. However, a brittleness index, based on both strength and pre-peak elastic parameters, was found to be the best index to predict KIC because of its lower sensitivity to the test method and rock type.

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Predicting mode-I fracture toughness of rocks using soft computing and multiple regression

Cited by 33 publications

References 53 publications

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

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

Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network

Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices

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