The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

Hasanipanah, Mahdi; Zhang, Wengang; Armaghani, Danial Jahed; Rad, Hima Nikafshan

doi:10.1109/access.2020.2980623

Cited by 35 publications

(10 citation statements)

References 50 publications

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“…A critical analysis of predictive modelling results by several researchers using soft computing techniques such as ANN was conducted to validate the outcomes of this research. For instance, the range of R 2 for an ANFIS predictive model proposed by Hasanipanah et al [59] that integrates R n , DD, and point-load index is between 0.857 to 0.897 for the testing stage of the model, which is lower than the performance prediction obtained in this study. Ceryan et al [43], who developed LS-SVM in predicting tensile strength of rock, obtained a R 2 of 0.86 which is lower than this study.…”

Section: Discussioncontrasting

confidence: 74%

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The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

et al. 2021

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Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

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Section: Discussioncontrasting

confidence: 74%

“…The most fitting model for MLR analysis is the one that generates the highest-ranking score. By referring to previous studies [58,59], 80% (102) of the whole data samples (127) were selected randomly for the model's training purposes. The remaining 20% (25) were used to test the models.…”

Section: Mlr Modellingmentioning

confidence: 99%

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

et al. 2021

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“…It is well known that English is the most widely spoken language in the world and its importance cannot be overstated [1]. In China, there are more and more people devoted to learning English [2]. Reading not only enriches vocabulary and grammar, improves writing skills, but also broadens the horizons and access to information [3].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

2021

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As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

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“…These techniques are a branch of computational intelligence that employ a variety of statistical and optimization tools to learn from past examples and to then utilize that prior training to estimate novel trends. ML and SC methods have been widely employed in several research areas [26], [27], [36]- [45], [28], [46]- [55], [29], [56]- [65], [30], [66], [67], [31]- [35]. In terms of the applications of ML and SC in RB classification and prediction, the initial attempts were made by Feng and Wang [68], who established artificial neural networks (ANNs) for controlling and predicting the likelihood of RB.…”

Section: Introductionmentioning

confidence: 99%

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

Khandelwal²,

Asteris³

et al. 2021

IEEE Access

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Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.

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The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock

Cited by 35 publications

References 50 publications

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models

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