Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability

Yağız, Saffet; Yazitova, A.; Karahan, Halil

doi:10.1080/17480930.2019.1709012

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…The CAI [7] test has been introduced in the 1970s by the Centre d'Etudes et Recherches des Charbonages (CER-CHAR) de France for abrasivity testing in coal-bearing rocks in mining industries while gradually being adopted for application in the tunneling industry [15,[25][26][27]. Different generations of testing devices and the impact of various testing parameters on the test results have been discussed in the literature [15,18,[28][29][30][31][32][33]. A typical CAI device is given in Figures 3 and 4.…”

Section: Cerchar Abrasivity Index (Cai)mentioning

confidence: 99%

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

2022

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The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2) and lower Mean square error (MSE).

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Section: Cerchar Abrasivity Index (Cai)mentioning

confidence: 99%

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

2022

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“…Furthermore, rock BI cannot be predicted due to the insufficient accuracy level of these models [22]. Recently, many researchers have applied machine learning (ML) methods and metaheuristic algorithms to solve engineering and science problems [27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…They indicated the effectiveness of proposed SVM methods in the BI prediction field. In another study, Yagiz et al [28] predicted BI values through a differential evolution (DE) algorithm using 48 datasets. With this aim, they employed DE to develop linear and nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model

Jamei

Mohammed

Ahmadianfar³

et al. 2022

Applied Sciences

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Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and IA = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and IA = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and IA = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and IA = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI.

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“…[10][11][12] Such BI models are suitable for near-surface applications with relatively low magnitudes of deviatoric stress, e.g., tunnel engineering. [13][14][15] However, they may have limited application in deeper underground environments where natural or anthropogenic fluid-driven fracturing takes place. The depth, and therefore the in situ effective (triaxial) stress will affect the mechanical response of the rock, e.g., static and dynamic elastic properties, brittleness/ductility, yield/failure type, post-failure behaviour.…”

Section: Introductionmentioning

confidence: 99%

Laboratory validation of a new hydro-mechanical energy-based brittleness index model for hydraulic fracturing

Feng¹,

Sarout²,

Dautriat³

et al. 2022

Preprint

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Brittleness Index (BI) is a critical parameter characterising the deformation regime of geo-materials, covering the range from purely brittle (fractures) to ductile (plastic flow). A variety of BI models have been developed based on rock properties such as mineralogy, elastic parameters, or stress-strain data. However, very few of them are based on the deformation induced by hydro-mechanical interactions emerging in a wide range of underground engineering applications. In this study, we develop a BI model based on the partitioning of the injection energy EI into non-seismic deformation energy Ed associated with hydraulic fracture propagation. To calculate the Ed, we apply a model for temporal fracturing area (Ad) within the penny-shaped fracture; we also correlate the wellbore pressure and the three-dimensional strain induced by hydraulic fracturing of the different types of rock samples subjected to true triaxial stress conditions (TTSC), either σv = 6.5 MPa, σH = 3 MPa, σh = 1.5 MPa or σv = 15 MPa, σH = 10 MPa, σh = 5 MPa for all tests. As a comparison, the BI is also quantified based on the existing models: (i) acoustic measurement from Rickman et al (2008), and (ii) the Mohr-Coulomb’s criteria from Papanastasiou et al (2016). The non-seismic deformation energy Ed ranges between 32.4% and 90.6% of the total injection energy EI, which is slightly higher than reported from field-scale data (15% to 80%), and is comparable to other laboratory-derived data (18% to 94%). The results show that the predictions based on our newly proposed hydro-mechanical energy-based BI model are qualitatively consistent with Papanastasiou et al.’s, but less so with Rickman et al.’s. Our BI model is shown to be stress-dependent and capable of capturing the brittle-to-ductile behaviour within a wide range of rheological samples subjected to hydraulic fracturing. This study demonstrates that our BI model opens a new way for quantifying the brittleness index regarding to realistic propagation scenarios, showing its superior robustness for such underground applications.

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Application of differential evolution algorithm and comparing its performance with literature to predict rock brittleness for excavatability

Cited by 17 publications

References 29 publications

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model

Laboratory validation of a new hydro-mechanical energy-based brittleness index model for hydraulic fracturing

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