Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

Sun, Dongmei; Lonbani, Mahshid; Askarian, Behnam; Armaghani, Danial Jahed; Tarinejad, Reza; Pham, Binh Thai; Khang, Huynh Van

doi:10.3390/app10051691

Cited by 36 publications

(14 citation statements)

References 51 publications

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“…One of the reasons for different disasters due to rock mechanics, such as rock bursts, is brittleness [7][8][9]. The literature shows that brittleness can be an effective and significant factor that can predict tunnel boring machines (TBMs) and road header performance [10,11].…”

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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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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“…Limited test at field-practical scales is presented, thus restricting the reliability and persuasion of these BI calculations. However, the successful applications of machine learning techniques in petroleum engineering have paved a potential data-driven approach for BI optimization at field-practical scales (Lin, Kang, Oh, & Canbulat, 2020;D. Sun et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimization of brittleness index for hydraulic fracturing

Hou¹,

Ren²,

Fang³

et al. 2021

Preprint

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Evaluation of brittleness index (BI) is a fundamental principle of a hydraulic fracturing design. A wide variety of BI calculations often baffle field engineers. The traditional value comparison may also not make the best of BI. Moreover, it is often mixed up with the fracability in field applications, thus causing concerns. We, therefore, redefine fracability as the fracturing pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify the confusion. Then, we propose a data-driven workflow to optimize BIs by controlling the geological and injecting conditions. The machine learning (ML) workflow is employed to predict the fracability (fracturing pressure) based on field measurement. Three representative ML algorithms are applied to average the prediction, aiming to restrict the interference of algorithm performances. The contribution of brittleness on pressure/fracability prediction by error analysis (rather than the traditional method of BI-value comparison) is proposed as the new criterion for optimization. Six classic BI correlations (mineral-, logging- and elastic-based) are evaluated, three of which are optimized for the derivation of a new BI using the backward elimination strategy. The stress ratio (ratio of minimum and maximum horizontal principal stress), representing the geological feature, is introduced into the derived calculation based on the independent variable analysis. The reliability of the new BI is verified by error analyses using data of eight fracturing stages from seven different wells. Approximately 40%~50% of the errors are reduced based on the new BI. The differences among the performances of algorithms are also significantly restrained. The new brittleness index provides a more reliable option for evaluating the brittleness and fracability of the fracturing formation. The machine learning workflow also proposes a promising application scenario of the BI for hydraulic fracturing, which makes more efficient and broader usages of the BI compared with the traditional value comparison.

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“…Nevertheless, it is possible to find a greater number of references that develop techniques based on artificial intelligence applied to other industrial materials, mainly steel [23]. These studies take advantage of the ability of these tools to obtain predictions about the behavior or properties of a certain material or industrial component [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

Fernández

Rodríguez-Prieto

Camacho

2020

Metals

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Aluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5 k entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95 % . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.

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Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

Cited by 36 publications

References 51 publications

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

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

Data-driven optimization of brittleness index for hydraulic fracturing

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

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