A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks

Ma, Junjie; Li, Tianbin; Li, Xiang; Zhou, Shuanglong; Ma, Chunchi; Wei, Daqiang; Dai, Kunkun

doi:10.1038/s41598-022-19301-6

Cited by 8 publications

(3 citation statements)

References 56 publications

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“…During the testing phase, the predicted probabilities [26] for each class can be obtained by evaluating the input instance x with each decision boundary (𝑤𝑤, 𝑏𝑏) 𝑗𝑗,𝑘𝑘 and applying a softmax function, given by Eq. 1: 𝑃𝑃(𝑗𝑗│𝑥𝑥) = exp (−𝑤𝑤_((𝑗𝑗, 𝑘𝑘))^𝑇𝑇 𝑥𝑥 + 〖 𝑏𝑏〗_((𝑗𝑗 , 𝑘𝑘) ) )/ ((exp (−𝑤𝑤_((𝑗𝑗, 𝑘𝑘))^𝑇𝑇 𝑥𝑥 + 〖 𝑏𝑏〗_((𝑗𝑗 , 𝑘𝑘) ) ) + exp (𝑤𝑤_((𝑗𝑗, 𝑘𝑘))^𝑇𝑇 𝑥𝑥 + 〖 𝑏𝑏〗_((𝑗𝑗 , 𝑘𝑘) ) ) ) )…”

Section: F Neutrosophic Classificationmentioning

confidence: 99%

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Ibrahim,

Essameldin,

Saad

2024

IEEE Access

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Cyberbullying is a social media network issue, a global crisis affecting the victims and society. Automatically identifying cyberbullying on social media has become extremely hard because of the complicated nature and intricate language employed within these platforms. The brevity and informal nature of text often results in ambiguous or unclear expressions, making it challenging to accurately interpret the intended meaning. Identifying cyberbullying becomes even more complex when faced with uncertain or contextually vague content. Presently, numerous approaches are available for cyberbullying detection, However, they continue to grapple with the challenge of distinguishing between various forms of cyberbullying-related hate speech due to its ambiguous and vague nature, and they also fall short in terms of accuracy. This paper proposes a novel approach to fine-grained cyberbullying classification by integrating Neutrosophic Logic within the Multi-Layer Perceptron (MLP) model. The proposed model enhances cyberbullying types by mitigating the challenges posed by the ambiguity and overlapping boundaries between distinct categories of cyberbullying. The incorporation of Neutrosophic Logic aims to address the uncertainty, ambiguity, and indeterminacy within classification decisions, offering a more comprehensive and flexible approach for handling complex classification scenarios. The model, leveraging the one-against-one strategy in MLP classification, captures complex relationships between various types of cyberbullying, due to the overlaps and ambiguous instances within cyberbullying types. The testing phase of this model emphasizes the significance of Neutrosophic Logic, employing class probabilities from multiple one-against-one classifiers to provide a comprehensive insight into classification outcomes. The results of the proposed model demonstrate the performance enhancement of incorporating Neutrosophic Logic for fine-grained cyberbullying classification tasks.

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Section: F Neutrosophic Classificationmentioning

confidence: 99%

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Ibrahim,

Essameldin,

Saad

2024

IEEE Access

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“…According to the previous research [38], and considering the specific project situation of the project, five factors are selected in accordance with the aforementioned principles [39]. The rock quality designation (RQD), uniaxial compressive strength (R w ), integrality coefficient of the rock mass (K V ), strength coefficient of the structural surface (K f ), and groundwater seepage (W) were selected as evaluation indicators for the classification system.…”

Section: Selection and Principle Of Evaluation Indexesmentioning

confidence: 99%

Evaluation and Application of Surrounding Rock Stability Based on an Improved Fuzzy Comprehensive Evaluation Method

Mao

Zhao

et al. 2023

Mathematics

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Ensuring the stability of surrounding rock is crucial for the safety of underground engineering projects. In this study, an improved fuzzy comprehensive evaluation method is proposed to accurately predict the stability of surrounding rock. Five key factors, namely, rock quality designation, uniaxial compressive strength, integrality coefficient of the rock mass, strength coefficient of the structural surface, and groundwater seepage, are selected as evaluation indicators, and a five-grade evaluation system is established. An improved analytic hierarchy process (IAHP) is proposed to enhance the accuracy of the evaluation. Using interval numbers rather than real numbers in constructing an interval judgment matrix can better account for the subjective fuzziness and uncertainty of expert judgment. Subjective and objective weights are obtained through IAHP and coefficient of variation, and the comprehensive weight is calculated on the basis of game theory principles. In addition, trapezoidal and triangular membership functions are employed to determine the membership degree, and an improved fuzzy comprehensive evaluation model is constructed. The model is then used to determine the stability of the surrounding rock based on the improved criterion. It is applied to six samples from an actual underground project in China to validate its effectiveness. Results show that the proposed model accurately and effectively predicts the stability of surrounding rock, which aligns with the findings from field investigations. The proposed method provides a valuable reference for evaluating surrounding rock stability and controlling construction risks.

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“…Numerous factors influence the penetration rate, including geological conditions, TBM characteristics, site-specific issues, operator experience, contractor management, and expertise 10 . Among these, changes in the geological conditions are of significant importance for TBM construction 11 .…”

Section: Introductionmentioning

confidence: 99%

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

Li,

Liu,

Fang

et al. 2024

Sci Rep

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Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.

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A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks

Cited by 8 publications

References 56 publications

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Social Media Forensics: An Adaptive Cyberbullying-Related Hate Speech Detection Approach Based on Neural Networks With Uncertainty

Evaluation and Application of Surrounding Rock Stability Based on an Improved Fuzzy Comprehensive Evaluation Method

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

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